| Title: | Multivariate Data Analysis for Chemometrics |
|---|---|
| Description: | Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>. |
| Authors: | Sergey Kucheryavskiy [aut, cre]
|
| Maintainer: | Sergey Kucheryavskiy <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.14.2 |
| Built: | 2025-02-16 05:31:47 UTC |
| Source: | https://github.com/svkucheryavski/mdatools |
Generic as.matrix function for classification results. Returns matrix with performance
values for specific class.
## S3 method for class 'classres' as.matrix(x, ncomp = NULL, nc = 1, ...)## S3 method for class 'classres' as.matrix(x, ncomp = NULL, nc = 1, ...)
x |
classification results (object of class |
ncomp |
model complexity (number of components) to show the parameters for. |
nc |
if there are several classes, which class to show the parameters for. |
... |
other arguments |
Generic as.matrix function for linear decomposition. Returns a matrix with information
about the decomposition.
## S3 method for class 'ldecomp' as.matrix(x, ncomp = NULL, ...)## S3 method for class 'ldecomp' as.matrix(x, ncomp = NULL, ...)
x |
object of class |
ncomp |
number of components to get the result for (if NULL will return for each available) |
... |
other arguments |
Returns a matrix with model performance statistics for PLS-DA results
## S3 method for class 'plsdares' as.matrix(x, ncomp = NULL, nc = 1, ...)## S3 method for class 'plsdares' as.matrix(x, ncomp = NULL, nc = 1, ...)
x |
PLS-DA results (object of class |
ncomp |
number of components to calculate the statistics for (if NULL gets for all components) |
nc |
for which class to calculate the statistics for |
... |
other arguments |
Returns a matrix with model performance statistics for PLS results
## S3 method for class 'plsres' as.matrix(x, ncomp = NULL, ny = 1, ...)## S3 method for class 'plsres' as.matrix(x, ncomp = NULL, ny = 1, ...)
x |
PLS results (object of class |
ncomp |
number of components to calculate the statistics for |
ny |
for which response variable calculate the statistics for |
... |
other arguments |
returns matrix with regression coeffocoents for given response number and amount of components
## S3 method for class 'regcoeffs' as.matrix(x, ncomp = 1, ny = 1, ...)## S3 method for class 'regcoeffs' as.matrix(x, ncomp = 1, ny = 1, ...)
x |
regression coefficients object (class |
ncomp |
number of components to return the coefficients for |
ny |
number of response variable to return the coefficients for |
... |
other arguments |
Returns a matrix with model performance statistics for regression results
## S3 method for class 'regres' as.matrix(x, ncomp = NULL, ny = 1, ...)## S3 method for class 'regres' as.matrix(x, ncomp = NULL, ny = 1, ...)
x |
regression results (object of class |
ncomp |
model complexity (number of components) to calculate the statistics for (can be a vector) |
ny |
for which response variable calculate the statistics for |
... |
other arguments |
Generic as.matrix function for SIMCAM results. Returns matrix with performance
values for specific class.
## S3 method for class 'simcamres' as.matrix(x, nc = seq_len(x$nclasses), ...)## S3 method for class 'simcamres' as.matrix(x, nc = seq_len(x$nclasses), ...)
x |
classification results (object of class |
nc |
vector with classes to use. |
... |
other arguments |
Generic as.matrix function for classification results. Returns matrix with performance
values for specific class.
## S3 method for class 'simcares' as.matrix(x, ncomp = NULL, ...)## S3 method for class 'simcares' as.matrix(x, ncomp = NULL, ...)
x |
classification results (object of class |
ncomp |
model complexity (number of components) to show the parameters for. |
... |
other arguments |
Capitalize text or vector with text values
capitalize(str)capitalize(str)
str |
text of vector with text values |
The dataset consists of Raman spectra of fructose, lactose, and ribose as well as spectra of their mixtures.
data(simdata)data(simdata)
The data is a list (carbs) with the following fields:
$D |
a matrix (21x1401) with spectral values for the mixtures. |
$S |
a matrix (1401x3) with spectral values for the pure components. |
$C |
a matrix (21x3) with concentration of the pure components. |
The dataset consists of Raman spectra of fructose, lactose, and ribose as well as spectra of their mixtures. The original spectra were downloaded from publicly available SPECARB library [1], created by S.B. Engelsen. The specta were truncated to the range from 200 to 1600 cm-1.
The spectra of mixtures were created by linear combinations of the original spectra:
D = CS' + E
Concentrations of the components, C, follow a simplex lattice design with four levels. Some noise calculated as a random number uniformly distributed between 0% and 3% of maximum initial intensity (E) was added to each spectrum of the dataset, D, individually.
1. Engelsen S.B., Database on Raman spectra of carbohydrates. Available at: http://www.models.life.ku.dk/~specarb/specarb.html [visited 31.05.2020]
Categorize PCA results
categorize(obj, ...)categorize(obj, ...)
obj |
object with PCA model |
... |
other parameters |
The method compares score and orthogonal distances of PCA results from res with
critical limits computed for the PCA model and categorizes the corresponding objects as
"regular", "extreme" or "outlier".
## S3 method for class 'pca' categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)## S3 method for class 'pca' categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)
obj |
object with PCA model |
res |
object with PCA results |
ncomp |
number of components to use for the categorization |
... |
other parameters |
The method does not categorize hidden values if any.
vector (factor) with results of categorization.
The method uses full distance for decomposition of X-data and squared Y-residuals of PLS results
from res with critical limits computed for the PLS model and categorizes the
corresponding objects as "regular", "extreme" or "outlier".
## S3 method for class 'pls' categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)## S3 method for class 'pls' categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)
obj |
object with PCA model |
res |
object with PCA results |
ncomp |
number of components to use for the categorization |
... |
other parameters |
The method does not categorize hidden values if any. It is based on the approach described in [1] and works only if data driven approach is used for computing critical limits.
vector (factor) with results of categorization.
1. Rodionova O. Ye., Pomerantsev A. L. Detection of Outliers in Projection-Based Modeling. Analytical Chemistry (2020, in publish). doi: 10.1021/acs.analchem.9b04611
The method is based on Chi-squared distribution with DF = 2 * (m(u)/s(u)^2
chisq.crit(param, alpha = 0.05, gamma = 0.01)chisq.crit(param, alpha = 0.05, gamma = 0.01)
param |
matrix with distribution parameters |
alpha |
significance level for extreme objects |
gamma |
significance level for outliers |
Calculate probabilities for distance values using Chi-square distribution
chisq.prob(u, param)chisq.prob(u, param)
u |
vector with distances |
param |
vector with distribution parameters |
Converts PLS predictions of y values to predictions of classes
classify.plsda(model, y)classify.plsda(model, y)
model |
a PLS-DA model (object of class |
y |
a matrix with predicted y values |
This is a service function for PLS-DA class, do not use it manually.
Classification results (an object of class classres)
Make classification based on calculated T2 and Q values and corresponding limits
classify.simca(obj, pca.res, c.ref = NULL)classify.simca(obj, pca.res, c.ref = NULL)
obj |
a SIMCA model (object of class |
pca.res |
results of projection data to PCA space |
c.ref |
vector with class reference values |
This is a service function for SIMCA class, do not use it manually.
vector with predicted class values (c.pred)
Check reference class values and convert it to a factor if necessary
classmodel.processRefValues(c.ref, classnames = NULL)classmodel.processRefValues(c.ref, classnames = NULL)
c.ref |
class reference values provided by user |
classnames |
text with class name in case of logical reference values |
classres is used to store results classification for one or multiple classes.
classres(c.pred, c.ref = NULL, p.pred = NULL, ncomp.selected = 1)classres(c.pred, c.ref = NULL, p.pred = NULL, ncomp.selected = 1)
c.pred |
matrix with predicted values (+1 or -1) for each class. |
c.ref |
matrix with reference values for each class. |
p.pred |
matrix with probability values for each class. |
ncomp.selected |
vector with selected number of components for each class. |
There is no need to create a classres object manually, it is created automatically when
build a classification model (e.g. using simca or plsda) or apply
the model to new data. For any classification method from mdatools, a class using to
represent results of classification (e.g. simcares) inherits fields and methods of
classres.
c.pred |
predicted class values (+1 or -1). |
p.pred |
predicted class probabilities. |
c.ref |
reference (true) class values if provided. |
The following fields are available only if reference values were provided.
tp |
number of true positives. |
tn |
number of true negatives. |
fp |
nmber of false positives. |
fn |
number of false negatives. |
specificity |
specificity of predictions. |
sensitivity |
sensitivity of predictions. |
misclassified |
ratio of misclassified objects. |
Methods classres class:
showPredictions.classres |
shows table with predicted values. |
plotPredictions.classres |
makes plot with predicted values. |
plotSensitivity.classres |
makes sn plot. |
plotSpecificity.classres |
makes specificity plot. |
plotMisclassified.classres |
makes ms ratio plot. |
plotPerformance.classres |
makes plot with misclassified ratio, specificity and sensitivity values. |
Calculates and returns performance parameters for classification result (e.g. number of false negatives, false positives, sn, specificity, etc.).
classres.getPerformance(c.ref, c.pred)classres.getPerformance(c.ref, c.pred)
c.ref |
reference class values for objects (vector with numeric or text values) |
c.pred |
predicted class values for objects (array nobj x ncomponents x nclasses) |
The function is called automatically when a classification result with reference values is
created, for example when applying a plsda or simca models.
Returns a list with following fields:
$fn |
number of false negatives (nclasses x ncomponents) |
$fp |
number of false positives (nclasses x ncomponents) |
$tp |
number of true positives (nclasses x ncomponents) |
$sensitivity |
sn values (nclasses x ncomponents) |
$specificity |
specificity values (nclasses x ncomponents) |
$specificity |
ms ratio values (nclasses x ncomponents) |
returns matrix with confidence intervals for regression coeffocoents for given response number and number of components.
## S3 method for class 'regcoeffs' confint(object, parm = NULL, level = 0.95, ncomp = 1, ny = 1, ...)## S3 method for class 'regcoeffs' confint(object, parm = NULL, level = 0.95, ncomp = 1, ny = 1, ...)
object |
regression coefficients object (class |
parm |
not used, needed for compatiility with general method |
level |
confidence level |
ncomp |
number of components (one value) |
ny |
index of response variable (one value) |
... |
other arguments |
Class for MCR-ALS constraint
constraint(name, params = NULL, method = NULL)constraint(name, params = NULL, method = NULL)
name |
short text with name for the constraint |
params |
a list with parameters for the constraint method (if NULL - default parameters will be used) |
method |
method to call when applying the constraint, provide it only for user defined constraints |
Use this class to create constraints and add them to a list for MCR-ALS curve resuliton (see
mcrals). Either provide name and parameters to one of the existing constraint
implementations or make your own. See the list of implemented constraints by running
constraints()
For your own constraint you need to create a method, which takes matrix with values (either spectra or contributions being resolved) as the first argument, does something and then return a matrix with the same dimension as the result. The method can have any number of optional parameters.
See help for mcrals or Bookdown tutorial for details.
Adds a small portion of mean to contributions or spectra to increase contrast
constraintAngle(x, d, weight = 0.05)constraintAngle(x, d, weight = 0.05)
x |
data matrix (spectra or contributions) |
d |
matrix with the original spectral values |
weight |
how many percent of mean to add (between 0 and 1) |
Force rows of data sum up to given value
constraintClosure(x, d, sum = 1)constraintClosure(x, d, sum = 1)
x |
data matrix (spectra or contributions) |
d |
matrix with the original spectral values |
sum |
which value the specra or contributions should sum up to |
Set all negative values in the matrix to 0
constraintNonNegativity(x, d)constraintNonNegativity(x, d)
x |
data matrix (spectra or contributions) |
d |
matrix with the original spectral values |
Normalize rows of matrix to unit length or area
constraintNorm(x, d, type = "length")constraintNorm(x, d, type = "length")
x |
data matrix (spectra or contributions) |
d |
matrix with the original spectral values |
type |
type of normalization ("area", "length" or "sum") |
Shows information about all implemented constraints
constraints.list()constraints.list()
forces column of matrix to have one maximum each
constraintUnimod(x, d, tol = 0)constraintUnimod(x, d, tol = 0)
x |
data matrix (spectra or contributions) |
d |
matrix with the original spectral values |
tol |
tolerance (value between 0 and 1) to take make method stable to small fluctuations |
Generates and returns sequence of object indices for each segment in random segmented cross-validation
crossval(cv = 1, nobj = NULL, resp = NULL)crossval(cv = 1, nobj = NULL, resp = NULL)
cv |
cross-validation settings, can be a number or a list. If cv is a number, it will be used as a number of segments for random cross-validation (if cv = 1, full cross-validation will be preformed), if it is a list, the following syntax can be used: cv = list('rand', nseg, nrep) for random repeated cross-validation with nseg segments and nrep repetitions or cv = list('ven', nseg) for systematic splits to nseg segments ('venetian blinds'). |
nobj |
number of objects in a dataset |
resp |
vector with response values to use in case of venetian blinds |
matrix with object indices for each segment
Define parameters based on 'cv' value
crossval.getParams(cv, nobj)crossval.getParams(cv, nobj)
cv |
settings for cross-validation provided by user |
nobj |
number of objects in calibration set |
Does cross-validation of a regression model
crossval.regmodel(obj, x, y, cv, cal.fun, pred.fun, cv.scope = "local")crossval.regmodel(obj, x, y, cv, cal.fun, pred.fun, cv.scope = "local")
obj |
a regression model (object of class |
x |
a matrix with x values (predictors from calibration set) |
y |
a matrix with y values (responses from calibration set) |
cv |
number of segments (if cv = 1, full cross-validation will be used) |
cal.fun |
reference to function for model calibration |
pred.fun |
reference to function for getting predicted y-values (see description) |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
object of class plsres with results of cross-validation
Function 'pred.fun' must take four agruments: autoscaled x-values, array with regression coefficients, vectors for centring and scaling of y-values (if used). The function must return predicted y-values in original units (unscaled and uncentered).
Does the cross-validation of a SIMCA model
crossval.simca(obj, x, cv)crossval.simca(obj, x, cv)
obj |
a SIMCA model (object of class |
x |
a matrix with x values (predictors from calibration set) |
cv |
number of segments (if cv = 1, full cross-validation will be used) |
object of class simcares with results of cross-validation
String with description of cross-validation method
crossval.str(cv)crossval.str(cv)
cv |
a list with cross-validation settings |
a string with the description text
Calculates critical limits for distance values using Data Driven moments approach
dd.crit(paramQ, paramT2, alpha = 0.05, gamma = 0.01)dd.crit(paramQ, paramT2, alpha = 0.05, gamma = 0.01)
paramQ |
matrix with parameters for distribution of Q distances |
paramT2 |
matrix with parameters for distribution of T2 distances |
alpha |
significance level for extreme objects |
gamma |
significance level for outliers |
Calculates critical limits for distance values using Data Driven moments approach
ddmoments.param(U)ddmoments.param(U)
U |
matrix or vector with distance values |
Calculates critical limits for distance values using Data Driven robust approach
ddrobust.param(U, ncomp, alpha, gamma)ddrobust.param(U, ncomp, alpha, gamma)
U |
matrix or vector with distance values |
ncomp |
number of components |
alpha |
significance level for extreme objects |
gamma |
significance level for outliers |
Create ellipse on the current plot
ellipse(xc = 0, yc = 0, a, b, col = "black", lty = 1, ...)ellipse(xc = 0, yc = 0, a, b, col = "black", lty = 1, ...)
xc |
coordinate of center (x) |
yc |
coordinate of center (y) |
a |
major axis |
b |
minor axis |
col |
color of the ellipse line |
lty |
type of the ellipse line |
... |
any argument suitable for |
Applies constraint to a dataset
employ.constraint(obj, x, d, ...)employ.constraint(obj, x, d, ...)
obj |
object with constraint |
x |
matrix with pure spectra or contributions |
d |
matrix with original spectral values |
... |
other arguments |
Applies a list with preprocessing methods to a dataset
employ.prep(obj, x, ...)employ.prep(obj, x, ...)
obj |
list with preprocssing methods (created using |
x |
matrix with dataset |
... |
other arguments |
Imitation of fprinf() function
fprintf(...)fprintf(...)
... |
arguments for sprintf function |
Calibration data
getCalibrationData(obj)getCalibrationData(obj)
obj |
a model object |
Generic function getting calibration data from a linear decomposition model (e.g. PCA)
Returns matrix with original calibration data
## S3 method for class 'pca' getCalibrationData(obj)## S3 method for class 'pca' getCalibrationData(obj)
obj |
object with PCA model |
Get data, used for calibration of the SIMCAM individual models and combine to one dataset.
## S3 method for class 'simcam' getCalibrationData(obj)## S3 method for class 'simcam' getCalibrationData(obj)
obj |
SIMCAM model (object of class |
See examples in help for simcam function.
Compute confidence ellipse for a set of points
getConfidenceEllipse(points, conf.level = 0.95, n = 100)getConfidenceEllipse(points, conf.level = 0.95, n = 100)
points |
matrix of data frame with coordinates of the points |
conf.level |
confidence level for the ellipse |
n |
number of points in the ellipse coordinates |
matrix with coordinates of the ellipse points (x and y)
Confusion matrix for classification results
getConfusionMatrix(obj, ...)getConfusionMatrix(obj, ...)
obj |
classification results (object of class |
... |
other parameters. |
Returns confusion matrix for classification results represented by the object.
The columns of the matrix correspond to classification results, rows - to the real classes. In case of soft classification with multiple classes (e.g. SIMCAM) sum of values for every row will not correspond to the total number of class members as the same object can be classified as a member of several classes or non of them.
## S3 method for class 'classres' getConfusionMatrix(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'classres' getConfusionMatrix(obj, ncomp = obj$ncomp.selected, ...)
obj |
classification results (object of class |
ncomp |
number of components to make the matrix for (NULL - use selected for a model). |
... |
other arguments |
Returns confusion matrix for classification results represented by the object.
Compute coordinates of a closed convex hull for data points
getConvexHull(points)getConvexHull(points)
points |
matrix of data frame with coordinates of the points |
For scatter plots labels correspond to rows of the data (names, values, indices, etc.). For non-scatter plots labels correspond to the columns (names, indices or max value for each column)
getDataLabels(ps, labels = NULL)getDataLabels(ps, labels = NULL)
ps |
'plotseries' object |
labels |
vector with user defined labels or type of labels to show ("values", "names", "indices") |
Shows a list with implemented constraints
getImplementedConstraints()getImplementedConstraints()
Shows a list with implemented preprocessing methods
getImplementedPrepMethods()getImplementedPrepMethods()
Create labels as column or row indices
getLabelsAsIndices(ps)getLabelsAsIndices(ps)
ps |
'plotseries' object |
Create labels from data values
getLabelsAsValues(ps)getLabelsAsValues(ps)
ps |
'plotseries' object |
returns main title for a plot depending on a user choice
getMainTitle(main, ncomp, default)getMainTitle(main, ncomp, default)
main |
main title of a plot, provided by user |
ncomp |
number of components to select, provided by user |
default |
default title for the plot |
Depedning on a user choice it returns main title for a plot
Define colors for plot series
getPlotColors(ps, col, opacity, cgroup, colmap)getPlotColors(ps, col, opacity, cgroup, colmap)
ps |
'plotseries' object |
col |
color specified by user (if any) |
opacity |
opacity for the color |
cgroup |
vector for color grouping (if any) |
colmap |
name or values for colormap |
Compute class belonging probabilities for classification results.
getProbabilities(obj, ...)getProbabilities(obj, ...)
obj |
an object with classification results (e.g. SIMCA) |
... |
other parameters |
Probabilities for residual distances
## S3 method for class 'pca' getProbabilities(obj, ncomp, q, h, ...)## S3 method for class 'pca' getProbabilities(obj, ncomp, q, h, ...)
obj |
object with PCA model |
ncomp |
number of components to compute the probability for |
q |
vector with squared orthogonal distances for given number of components |
h |
vector with score distances for given number of components |
... |
other parameters |
Computes p-value for every object being from the same populaion as calibration set based on its orthogonal and score distances.
Probabilities of class belonging for PCA/SIMCA results
## S3 method for class 'simca' getProbabilities(obj, ncomp, q, h, ...)## S3 method for class 'simca' getProbabilities(obj, ncomp, q, h, ...)
obj |
object with PCA model |
ncomp |
number of components to compute the probability for |
q |
vector with squared orthogonal distances for given number of components |
h |
vector with score distances for given number of components |
... |
other parameters |
Computes p-value for every object being from the same populaion as calibration set based on its orthogonal and score distances.
The method identifies indices of pure variables using the SIMPLISMA algorithm.
getPureVariables(D, ncomp, purevars, offset)getPureVariables(D, ncomp, purevars, offset)
D |
matrix with the spectra |
ncomp |
number of pure components |
purevars |
user provided values gor pure variables (no calculation will be run in this case) |
offset |
offset (between 0 and 1) for calculation of parameter alpha |
The function returns a list with with following fields:
ncomp |
number of pure components. |
purvars |
vector with indices for pure variables. |
purityspec |
matrix with purity values for each resolved components. |
purity |
vector with purity values for resolved components. |
Generic function for getting regression coefficients from PLS model
getRegcoeffs(obj, ...)getRegcoeffs(obj, ...)
obj |
a PLS model |
... |
other parameters |
Returns a matrix with regression coefficients for the PLS model which can be applied to a data directly
## S3 method for class 'regmodel' getRegcoeffs( obj, ncomp = obj$ncomp.selected, ny = 1, full = FALSE, alpha = 0.05, ... )## S3 method for class 'regmodel' getRegcoeffs( obj, ncomp = obj$ncomp.selected, ny = 1, full = FALSE, alpha = 0.05, ... )
obj |
a PLS model (object of class |
ncomp |
number of components to return the coefficients for |
ny |
if y is multivariate which variables you want to see the coefficients for |
full |
if TRUE the method also shows p-values and t-values as well as confidence intervals for the coefficients (if available) |
alpha |
significance level for confidence intervals (a number between 0 and 1, e.g. 0.05) |
... |
other parameters |
The method recalculates the regression coefficients found by the PLS algorithm taking into account centering and scaling of predictors and responses, so the matrix with coefficients can be applied directly to original data (yp = Xb).
If number of components is not specified, the optimal number, selected by user or identified by a model will be used.
If Jack-knifing method was used to get statistics for the coefficient the method returns all statistics as well (p-value, t-value, confidence interval). In this case user has to specified a number of y-variable (if there are many) to get the statistics and the coefficients for. The confidence interval is computed for unstandardized coefficients.
A matrix with regression coefficients and (optinally) statistics.
Return list with valid results
getRes(res, classname = "ldecomp")getRes(res, classname = "ldecomp")
res |
list with results |
classname |
name of class (for result object) to look for |
returns number of components depending on a user choice
getSelectedComponents(obj, ncomp = NULL)getSelectedComponents(obj, ncomp = NULL)
obj |
an MDA model or result object (e.g. |
ncomp |
number of components to select, provided by user |
Depedning on a user choice it returns optimal number of component for the model (if use did not provide any value) or check the user choice for correctness and returns it back
Generic function for returning selectivity ratio values for regression model (PCR, PLS, etc)
getSelectivityRatio(obj, ...)getSelectivityRatio(obj, ...)
obj |
a regression model |
... |
other parameters |
Returns vector with Selectivity ratio values. This function is a proxy for selratio
and will be removed in future releases.
## S3 method for class 'pls' getSelectivityRatio(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'pls' getSelectivityRatio(obj, ncomp = obj$ncomp.selected, ...)
obj |
a PLS model (object of class |
ncomp |
number of components to get the values for (if NULL user selected as optimal will be used) |
... |
other parameters |
vector with selectivity ratio values
[1] Tarja Rajalahti et al. Chemometrics and Laboratory Systems, 95 (2009), pp. 35-48.
Compute explained variance for MCR case
getVariance.mcr(obj, x)getVariance.mcr(obj, x)
obj |
object of class |
x |
original spectral data |
Generic function for returning VIP scores values for regression model (PCR, PLS, etc)
getVIPScores(obj, ...)getVIPScores(obj, ...)
obj |
a regression model |
... |
other parameters |
Returns vector with VIP scores values. This function is a proxy for vipscores
and will be removed in future releases.
## S3 method for class 'pls' getVIPScores(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'pls' getVIPScores(obj, ncomp = obj$ncomp.selected, ...)
obj |
a PLS model (object of class |
ncomp |
number of components to count |
... |
other parameters |
matrix nvar x 1 with VIP score values
Calculate critical limits for distance values using Hotelling T2 distribution
hotelling.crit(nobj, ncomp, alpha = 0.05, gamma = 0.01)hotelling.crit(nobj, ncomp, alpha = 0.05, gamma = 0.01)
nobj |
number of objects in calibration set |
ncomp |
number of components |
alpha |
significance level for extreme objects |
gamma |
significance level for outliers |
vector with four values: critical limits for given alpha and gamma, mean distance and DoF.
Calculate probabilities for distance values and given parameters using Hotelling T2 distribution
hotelling.prob(u, ncomp, nobj)hotelling.prob(u, ncomp, nobj)
u |
vector with distances |
ncomp |
number of components |
nobj |
number of objects in calibration set |
show image data as an image
imshow( data, channels = 1, show.excluded = FALSE, main = paste0(" ", colnames(data)[channels]), colmap = "jet" )imshow( data, channels = 1, show.excluded = FALSE, main = paste0(" ", colnames(data)[channels]), colmap = "jet" )
data |
data with image |
channels |
indices for one or three columns to show as image channels |
show.excluded |
logical, if TRUE the method also shows the excluded (hidden) pixels |
main |
main title for the image |
colmap |
colormap using to show the intensity levels |
Applies iPLS algorithm to find variable intervals most important for prediction.
ipls( x, y, glob.ncomp = 10, center = TRUE, scale = FALSE, cv = list("ven", 10), exclcols = NULL, exclrows = NULL, int.ncomp = glob.ncomp, int.num = NULL, int.width = NULL, int.limits = NULL, int.niter = NULL, ncomp.selcrit = "min", method = "forward", x.test = NULL, y.test = NULL, silent = FALSE, full = FALSE, cv.scope = "local" )ipls( x, y, glob.ncomp = 10, center = TRUE, scale = FALSE, cv = list("ven", 10), exclcols = NULL, exclrows = NULL, int.ncomp = glob.ncomp, int.num = NULL, int.width = NULL, int.limits = NULL, int.niter = NULL, ncomp.selcrit = "min", method = "forward", x.test = NULL, y.test = NULL, silent = FALSE, full = FALSE, cv.scope = "local" )
x |
a matrix with predictor values. |
y |
a vector with response values. |
glob.ncomp |
maximum number of components for a global PLS model. |
center |
logical, center or not the data values. |
scale |
logical, standardize or not the data values. |
cv |
cross-validation settings (see details). |
exclcols |
columns of x to be excluded from calculations (numbers, names or vector with logical values). |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values). |
int.ncomp |
maximum number of components for interval PLS models. |
int.num |
number of intervals. |
int.width |
width of intervals. |
int.limits |
a two column matrix with manual intervals specification. |
int.niter |
maximum number of iterations (if NULL it will be the smallest of two values: number of intervals and 30). |
ncomp.selcrit |
criterion for selecting optimal number of components ('min' for minimum of RMSECV). |
method |
iPLS method ( |
x.test |
matrix with predictors for test set (by default is NULL, if specified, is used instead of cv). |
y.test |
matrix with responses for test set. |
silent |
logical, show or not information about selection process. |
full |
logical, if TRUE the procedure will continue even if no improvements is observed. |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
The algorithm splits the predictors into several intervals and tries to find a combination of the intervals, which gives best prediction performance. There are two selection methods: "forward" when the intervals are successively included, and "backward" when the intervals are successively excluded from a model. On the first step the algorithm finds the best (forward) or the worst (backward) individual interval. Then it tests the others to find the one which gives the best model in a combination with the already selected/excluded one. The procedure continues until no improvements is observed or the maximum number of iteration is reached.
There are several ways to specify the intervals. First of all either number of intervals
(int.num) or width of the intervals (int.width) can be provided. Alternatively
one can specify the limits (first and last variable number) of the intervals manually
with int.limits.
Cross-validation settings, cv, can be a number or a list. If cv is a number, it
will be used as a number of segments for random cross-validation (if cv = 1, full
cross-validation will be preformed). If it is a list, the following syntax can be used:
cv = list('rand', nseg, nrep) for random repeated cross-validation with nseg
segments and nrep repetitions or cv = list('ven', nseg) for systematic splits
to nseg segments ('venetian blinds').
object of 'ipls' class with several fields, including:
var.selected |
a vector with indices of selected variables |
int.selected |
a vector with indices of selected intervals |
int.num |
total number of intervals |
int.width |
width of the intervals |
int.limits |
a matrix with limits for each interval |
int.stat |
a data frame with statistics for the selection algorithm |
glob.stat |
a data frame with statistics for the first step (individual intervals) |
gm |
global PLS model with all variables included |
om |
optimized PLS model with selected variables |
[1] Lars Noergaard at al. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl.Spec. 2000; 54: 413-419
library(mdatools) ## forward selection for simdata data(simdata) Xc = simdata$spectra.c yc = simdata$conc.c[, 3, drop = FALSE] # run iPLS and show results im = ipls(Xc, yc, int.ncomp = 5, int.num = 10, cv = 4, method = "forward") summary(im) plot(im) # show "developing" of RMSECV during the algorithm execution plotRMSE(im) # plot predictions before and after selection par(mfrow = c(1, 2)) plotPredictions(im$gm) plotPredictions(im$om) # show selected intervals on spectral plot ind = im$var.selected mspectrum = apply(Xc, 2, mean) plot(simdata$wavelength, mspectrum, type = 'l', col = 'lightblue') points(simdata$wavelength[ind], mspectrum[ind], pch = 16, col = 'blue')library(mdatools) ## forward selection for simdata data(simdata) Xc = simdata$spectra.c yc = simdata$conc.c[, 3, drop = FALSE] # run iPLS and show results im = ipls(Xc, yc, int.ncomp = 5, int.num = 10, cv = 4, method = "forward") summary(im) plot(im) # show "developing" of RMSECV during the algorithm execution plotRMSE(im) # plot predictions before and after selection par(mfrow = c(1, 2)) plotPredictions(im$gm) plotPredictions(im$om) # show selected intervals on spectral plot ind = im$var.selected mspectrum = apply(Xc, 2, mean) plot(simdata$wavelength, mspectrum, type = 'l', col = 'lightblue') points(simdata$wavelength[ind], mspectrum[ind], pch = 16, col = 'blue')
Runs the backward iPLS algorithm
ipls.backward(x, y, obj, int.stat, glob.stat, full, cv.scope)ipls.backward(x, y, obj, int.stat, glob.stat, full, cv.scope)
x |
a matrix with predictor values. |
y |
a vector with response values. |
obj |
object with initial settings for iPLS algorithm. |
int.stat |
data frame with initial interval statistics. |
glob.stat |
data frame with initial global statistics. |
full |
logical, if TRUE the procedure will continue even if no improvements is observed. |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
Runs the forward iPLS algorithm
ipls.forward(x, y, obj, int.stat, glob.stat, full, cv.scope)ipls.forward(x, y, obj, int.stat, glob.stat, full, cv.scope)
x |
a matrix with predictor values. |
y |
a vector with response values. |
obj |
object with initial settings for iPLS algorithm. |
int.stat |
data frame with initial interval statistics. |
glob.stat |
data frame with initial global statistics. |
full |
logical, if TRUE the procedure will continue even if no improvements is observed. |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
Calculate critical limits for distance values using Jackson-Mudholkar approach
jm.crit(residuals, eigenvals, alpha = 0.05, gamma = 0.01)jm.crit(residuals, eigenvals, alpha = 0.05, gamma = 0.01)
residuals |
matrix with PCA residuals |
eigenvals |
vector with eigenvalues for PCA components |
alpha |
significance level for extreme objects |
gamma |
significance level for outliers |
vector with four values: critical limits for given alpha and gamma, mean distance and DoF.
Calculate probabilities for distance values and given parameters using Hotelling T2 distribution
jm.prob(u, eigenvals, ncomp)jm.prob(u, eigenvals, ncomp)
u |
vector with distances |
eigenvals |
vector with eigenvalues for PCA components |
ncomp |
number of components |
Creates an object of ldecomp class.
ldecomp(scores, loadings, residuals, eigenvals, ncomp.selected = ncol(scores))ldecomp(scores, loadings, residuals, eigenvals, ncomp.selected = ncol(scores))
scores |
matrix with score values (I x A). |
loadings |
matrix with loading values (J x A). |
residuals |
matrix with data residuals (I x J) |
eigenvals |
vector with eigenvalues for the loadings |
ncomp.selected |
number of selected components |
ldecomp is a general class for storing results of decomposition of dataset in
form X = TP' + E. Here, X is a data matrix, T - matrix with scores, P - matrix with
loadings and E - matrix with residuals. It is used, for example, for PCA results
(pcares), in PLS and other methods. The class also includes methods for
calculation of residual distances and explained variance.
There is no need to use the ldecomp manually. For example, when build PCA model
with pca or apply it to a new data, the results will automatically inherit
all methods of ldecomp.
Returns an object (list) of ldecomp class with following fields:
scores |
matrix with score values (I x A). |
residuals |
matrix with data residuals (I x J). |
T2 |
matrix with score distances (I x A). |
Q |
matrix with orthogonal distances (I x A). |
ncomp.selected |
selected number of components. |
expvar |
explained variance for each component. |
cumexpvar |
cumulative explained variance. |
Compute orthogonal Euclidean distance from object to PC space (Q, q) and Mahalanobis squared distance between projection of the object to the space and its origin (T2, h).
ldecomp.getDistances(scores, loadings, residuals, eigenvals)ldecomp.getDistances(scores, loadings, residuals, eigenvals)
scores |
matrix with scores (T). |
loadings |
matrix with loadings (P). |
residuals |
matrix with residuals (E). |
eigenvals |
vector with eigenvalues for the components |
The distances are calculated for every 1:n components, where n goes from 1 to ncomp (number of columns in scores and loadings).
Returns a list with Q, T2 and tnorm values for each component.
Compute coordinates of lines or curves with critical limits
ldecomp.getLimitsCoordinates( Qlim, T2lim, ncomp, norm, log, show.limits = c(TRUE, TRUE) )ldecomp.getLimitsCoordinates( Qlim, T2lim, ncomp, norm, log, show.limits = c(TRUE, TRUE) )
Qlim |
matrix with critical limits for orthogonal distances |
T2lim |
matrix with critical limits for score distances |
ncomp |
number of components for computing the coordinates |
norm |
logical, shall distance values be normalized or not |
log |
logical, shall log transformation be applied or not |
show.limits |
vector with two logical values defining if limits for extreme and/or outliers must be shown |
list with two matrices (x and y coordinates of corresponding limits)
Compute parameters for critical limits based on calibration results
ldecomp.getLimParams(U)ldecomp.getLimParams(U)
U |
matrix with residual distances |
Compute critical limits for orthogonal distances (Q)
ldecomp.getQLimits(lim.type, alpha, gamma, params, residuals, eigenvals)ldecomp.getQLimits(lim.type, alpha, gamma, params, residuals, eigenvals)
lim.type |
which method to use for calculation of critical limits for residuals |
alpha |
significance level for extreme limits. |
gamma |
significance level for outlier limits. |
params |
distribution parameters returned by ldecomp.getLimParams |
residuals |
matrix with residuals (E) |
eigenvals |
egenvalues for the components used to decompose the data |
Compute critical limits for score distances (T2)
ldecomp.getT2Limits(lim.type, alpha, gamma, params)ldecomp.getT2Limits(lim.type, alpha, gamma, params)
lim.type |
which method to use for calculation ("chisq", "ddmoments", "ddrobust") |
alpha |
significance level for extreme limits. |
gamma |
significance level for outlier limits. |
params |
distribution parameters returned by ldecomp.getLimParams |
Computes explained variance and cumulative explained variance for data decomposition.
ldecomp.getVariances(scores, loadings, residuals, Q)ldecomp.getVariances(scores, loadings, residuals, Q)
scores |
matrix with scores (T). |
loadings |
matrix with loadings (P). |
residuals |
matrix with residuals (E). |
Q |
matrix with squared orthogonal distances. |
Returns a list with two vectors.
Shows a plot with score (T2, h) vs orthogonal (Q, q) distances and corresponding critical limits for given number of components.
ldecomp.plotResiduals( res, Qlim, T2lim, ncomp, log = FALSE, norm = FALSE, cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", show.excluded = FALSE, ... )ldecomp.plotResiduals( res, Qlim, T2lim, ncomp, log = FALSE, norm = FALSE, cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", show.excluded = FALSE, ... )
res |
list with result objects to show the plot for |
Qlim |
matrix with critical limits for orthogonal distance |
T2lim |
matrix with critical limits for score distance |
ncomp |
how many components to use (by default optimal value selected for the model will be used) |
log |
logical, apply log tranformation to the distances or not (see details) |
norm |
logical, normalize distance values or not (see details) |
cgroup |
color grouping of plot points (works only if one result object is available) |
xlim |
limits for x-axis (if NULL will be computed automatically) |
ylim |
limits for y-axis (if NULL will be computed automatically) |
show.limits |
vector with two logical values defining if limits for extreme and/or outliers must be shown |
lim.col |
vector with two values - line color for extreme and outlier limits |
lim.lwd |
vector with two values - line width for extreme and outlier limits |
lim.lty |
vector with two values - line type for extreme and outlier limits |
show.legend |
logical, show or not legend on the plot (if more than one result object) |
legend.position |
if legend must be shown, where it should be |
show.excluded |
logical, show or hide rows marked as excluded (attribute 'exclrows'). |
... |
other plot parameters (see |
The function is a bit more advanced version of plotResiduals.ldecomp. It allows to
show distance values for several result objects (e.g. calibration and test set or calibration
and new prediction set) as well as display the correspondng critical limits in form of lines
or curves.
Depending on how many result objects your model has or how many you specified manually,
using the res parameter, the plot behaves in a bit different way.
If only one result object is provided, then it allows to colorise the points using cgroup
parameter. If two or more result objects are provided, then the function show
distances in groups, and adds corresponding legend.
The function can show distance values normalised (h/h0 and q/q0) as well as with log transformation (log(1 + h/h0), log(1 + q/q0)). The latter is useful if distribution of the points is skewed and most of them are densely located around bottom left corner.
mcr is used to store and visualise general MCR data and results.
mcr(x, ncomp, method, exclrows = NULL, exclcols = NULL, info = "", ...)mcr(x, ncomp, method, exclrows = NULL, exclcols = NULL, info = "", ...)
x |
spectra of mixtures (as matrix or data frame) |
ncomp |
number of pure components to resolve |
method |
function for computing spectra of pure components |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
exclcols |
columns to be excluded from calculations (numbers, names or vector with logical values) |
info |
text with information about the MCR model |
... |
other parameters realted to specific method |
mcralls allows to resolve spectroscopic data to linear combination of individual spectra
and contributions using the alternating least squares (ALS) algorithm with constraints.
mcrals( x, ncomp, cont.constraints = list(), spec.constraints = list(), spec.ini = matrix(runif(ncol(x) * ncomp), ncol(x), ncomp), cont.forced = matrix(NA, nrow(x), ncomp), spec.forced = matrix(NA, ncol(x), ncomp), cont.solver = mcrals.nnls, spec.solver = mcrals.nnls, exclrows = NULL, exclcols = NULL, verbose = FALSE, max.niter = 100, tol = 10^-6, info = "" )mcrals( x, ncomp, cont.constraints = list(), spec.constraints = list(), spec.ini = matrix(runif(ncol(x) * ncomp), ncol(x), ncomp), cont.forced = matrix(NA, nrow(x), ncomp), spec.forced = matrix(NA, ncol(x), ncomp), cont.solver = mcrals.nnls, spec.solver = mcrals.nnls, exclrows = NULL, exclcols = NULL, verbose = FALSE, max.niter = 100, tol = 10^-6, info = "" )
x |
spectra of mixtures (matrix or data frame). |
ncomp |
number of components to calculate. |
cont.constraints |
a list with constraints to be applied to contributions (see details). |
spec.constraints |
a list with constraints to be applied to spectra (see details). |
spec.ini |
a matrix with initial estimation of the pure components spectra. |
cont.forced |
a matrix which allows to force some of the concentration values (see details). |
spec.forced |
a matrix which allows to force some of the spectra values (see details). |
cont.solver |
which function to use as a solver for resolving of pure components contributions (see detials). |
spec.solver |
which function to use as a solver for resolving of pure components spectra (see detials). |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values). |
exclcols |
columns to be excluded from calculations (numbers, names or vector with logical values). |
verbose |
logical, if TRUE information about every iteration will be shown. |
max.niter |
maximum number of iterations. |
tol |
tolerance, when explained variance change is smaller than this value, iterations stop. |
info |
a short text with description of the case (optional). |
The method implements the iterative ALS algorithm, where, at each iteration, spectra and contributions of each chemical component are estimated and then a set of constraints is applied to each. The method is well described in [1, 2].
The method assumes that the spectra (D) is a linear combination of pure components spectra (S) and pure component concentrations (C):
D = CS' + E
So the task is to get C and S by knowing D. In order to do that you need to provide:
1. Constraints for spectra and contributions. The constraints should be provided as a list
with name of the constraint and all necessary parameters. You can see which constraints and
parameters are currently supported by running constraintList(). See the code examples
below or a Bookdown tutorial for more details.
2. Initial estimation of the pure components spectra, S. By default method uses a matrix with
random numbers but you can provide a better guess (for example by running mcrpure)
as a first step.
3. Which solver to use for resolving spectra and concentrations. There are two built in solvers:
mcrals.nnls (default) and mcrals.ols. The first implements non-negative least
squares method which gives non-negative (thus physically meaningful) solutions. The second is
ordinary least squares and if you want to get non-negative spectra and/or contributions in this
case you need to provide a non-negativity constraint.
The algorithm iteratively resolves C and S and checks how well CS' is to D. The iterations stop
either when number exceeds value in max.niter or when improvements (difference between
explained variance on current and previous steps) is smaller than tol value.
Parameters cont.force and spec.force allows you to force some parts of the
contributions or the spectra to be equal to particular pre-defined values. In this case you need
to provide the parameters (or just one of them) in form of a matrix. For example cont.force
should have as many rows as many you have in the original spectral data x and as many
columns as many pure components you want to resolve. Feel all values of this matrix with
NA and the values you want to force with real numbers. For example if you know that in
the first measurement concentration of 2 and 3 components was zero, set the corresponding
values of cont.force to zero. See also the last case in the examples section.
Returns an object of mcrpure class with the following fields:
resspec |
matrix with resolved spectra. |
rescont |
matrix with resolved contributions. |
cont.constraints |
list with contribution constraints provided by user. |
spec.constraints |
list with spectra constraints provided by user. |
expvar |
vector with explained variance for each component (in percent). |
cumexpvar |
vector with cumulative explained variance for each component (in percent). |
ncomp |
number of resolved components |
max.niter |
maximum number of iterations |
info |
information about the model, provided by user when build the model. |
More details and examples can be found in the Bookdown tutorial.
Sergey Kucheryavskiy ([email protected])
1. J. Jaumot, R. Gargallo, A. de Juan, and R. Tauler, "A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB", Chemometrics and Intelligent #' Laboratory Systems 76, 101-110 (2005).
Methods for mcrals objects:
summary.mcrals |
shows some statistics for the case. |
predict.mcrals |
computes contributions by projection of new spectra to the resolved ones. |
Plotting methods for mcrals objects:
plotSpectra.mcr |
shows plot with resolved spectra. |
plotContributions.mcr |
shows plot with resolved contributions. |
plotVariance.mcr |
shows plot with explained variance. |
plotCumVariance.mcr |
shows plot with cumulative explained variance. |
library(mdatools) # resolve mixture of carbonhydrates Raman spectra data(carbs) # define constraints for contributions cc <- list( constraint("nonneg") ) # define constraints for spectra cs <- list( constraint("nonneg"), constraint("norm", params = list(type = "area")) ) # because by default initial approximation is made by using random numbers # we need to seed the generator in order to get reproducable results set.seed(6) # run ALS m <- mcrals(carbs$D, ncomp = 3, cont.constraints = cc, spec.constraints = cs) summary(m) # plot cumulative and individual explained variance par(mfrow = c(1, 2)) plotVariance(m) plotCumVariance(m) # plot resolved spectra (all of them or individually) par(mfrow = c(2, 1)) plotSpectra(m) plotSpectra(m, comp = 2:3) # plot resolved contributions (all of them or individually) par(mfrow = c(2, 1)) plotContributions(m) plotContributions(m, comp = 2:3) # of course you can do this manually as well, e.g. show original # and resolved spectra par(mfrow = c(1, 1)) mdaplotg( list( "original" = prep.norm(carbs$D, "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # in case if you have reference spectra of components you can compare them with # the resolved ones: par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg( list( "pure" = prep.norm(mda.subset(mda.t(carbs$S), 1), "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l", lwd = c(3, 1) ) } # This example shows how to force some of the contribution values # First of all we combine the matrix with mixtures and the pure spectra, so the pure # spectra are on top of the combined matrix Dplus <- mda.rbind(mda.t(carbs$S), carbs$D) # since we know that concentration of C2 and C3 is zero in the first row (it is a pure # spectrum of first component), we can force them to be zero in the optimization procedure. # Similarly we can do this for second and third rows. cont.forced <- matrix(NA, nrow(Dplus), 3) cont.forced[1, ] <- c(NA, 0, 0) cont.forced[2, ] <- c(0, NA, 0) cont.forced[3, ] <- c(0, 0, NA) m <- mcrals(Dplus, 3, cont.forced = cont.forced, cont.constraints = cc, spec.constraints = cs) plot(m) # See bookdown tutorial for more details.library(mdatools) # resolve mixture of carbonhydrates Raman spectra data(carbs) # define constraints for contributions cc <- list( constraint("nonneg") ) # define constraints for spectra cs <- list( constraint("nonneg"), constraint("norm", params = list(type = "area")) ) # because by default initial approximation is made by using random numbers # we need to seed the generator in order to get reproducable results set.seed(6) # run ALS m <- mcrals(carbs$D, ncomp = 3, cont.constraints = cc, spec.constraints = cs) summary(m) # plot cumulative and individual explained variance par(mfrow = c(1, 2)) plotVariance(m) plotCumVariance(m) # plot resolved spectra (all of them or individually) par(mfrow = c(2, 1)) plotSpectra(m) plotSpectra(m, comp = 2:3) # plot resolved contributions (all of them or individually) par(mfrow = c(2, 1)) plotContributions(m) plotContributions(m, comp = 2:3) # of course you can do this manually as well, e.g. show original # and resolved spectra par(mfrow = c(1, 1)) mdaplotg( list( "original" = prep.norm(carbs$D, "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # in case if you have reference spectra of components you can compare them with # the resolved ones: par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg( list( "pure" = prep.norm(mda.subset(mda.t(carbs$S), 1), "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l", lwd = c(3, 1) ) } # This example shows how to force some of the contribution values # First of all we combine the matrix with mixtures and the pure spectra, so the pure # spectra are on top of the combined matrix Dplus <- mda.rbind(mda.t(carbs$S), carbs$D) # since we know that concentration of C2 and C3 is zero in the first row (it is a pure # spectrum of first component), we can force them to be zero in the optimization procedure. # Similarly we can do this for second and third rows. cont.forced <- matrix(NA, nrow(Dplus), 3) cont.forced[1, ] <- c(NA, 0, 0) cont.forced[2, ] <- c(0, NA, 0) cont.forced[3, ] <- c(0, 0, NA) m <- mcrals(Dplus, 3, cont.forced = cont.forced, cont.constraints = cc, spec.constraints = cs) plot(m) # See bookdown tutorial for more details.
The method identifies indices of pure variables using the SIMPLISMA algorithm.
mcrals.cal( D, ncomp, cont.constraints, spec.constraints, spec.ini, cont.forced, spec.forced, cont.solver, spec.solver, max.niter, tol, verbose )mcrals.cal( D, ncomp, cont.constraints, spec.constraints, spec.ini, cont.forced, spec.forced, cont.solver, spec.solver, max.niter, tol, verbose )
D |
matrix with the spectra |
ncomp |
number of pure components |
cont.constraints |
a list with constraints to be applied to contributions (see details). |
spec.constraints |
a list with constraints to be applied to spectra (see details). |
spec.ini |
a matrix with initial estimation of the pure components spectra. |
cont.forced |
a matrix which allows to force some of the concentration values (see details). |
spec.forced |
a matrix which allows to force some of the spectra values (see details). |
cont.solver |
which function to use as a solver for resolving of pure components contributions (see detials). |
spec.solver |
which function to use as a solver for resolving of pure components spectra (see detials). |
max.niter |
maximum number of iterations. |
tol |
tolerance, when explained variance change is smaller than this value, iterations stop. |
verbose |
logical, if TRUE information about every iteration will be shown. |
The function returns a list with with following fields:
ncomp |
number of pure components. |
resspec |
matrix with resolved spectra. |
rescont |
matrix with resolved contributions. |
cont.constraints |
list with contribution constraints provided by user. |
spec.constraints |
list with spectra constraints provided by user. |
max.niter |
maximum number of iterations |
Fast combinatorial non-negative least squares
mcrals.fcnnls( D, A, tol = 10 * .Machine$double.eps * as.numeric(sqrt(crossprod(A[, 1]))) * nrow(A) )mcrals.fcnnls( D, A, tol = 10 * .Machine$double.eps * as.numeric(sqrt(crossprod(A[, 1]))) * nrow(A) )
D |
a matrix |
A |
a matrix |
tol |
tolerance parameter for algorithm convergence |
Computes Fast combinatorial NNLS solution for B: D = AB' subject to B >= 0. Implements the method described in [1].
1. Van Benthem, M.H. and Keenan, M.R. (2004), Fast algorithm for the solution of large scale non-negativity-constrained least squares problems. J. Chemometrics, 18: 441-450. doi:10.1002/cem.889
Non-negative least squares
mcrals.nnls( D, A, tol = 10 * .Machine$double.eps * as.numeric(sqrt(crossprod(A[, 1]))) * nrow(A) )mcrals.nnls( D, A, tol = 10 * .Machine$double.eps * as.numeric(sqrt(crossprod(A[, 1]))) * nrow(A) )
D |
a matrix |
A |
a matrix |
tol |
tolerance parameter for algorithm convergence |
Computes NNLS solution for B: D = AB' subject to B >= 0. Implements the active-set based algorithm proposed by Lawson and Hanson [1].
1. Lawson, Charles L.; Hanson, Richard J. (1995). Solving Least Squares Problems. SIAM.
Ordinary least squares
mcrals.ols(D, A)mcrals.ols(D, A)
D |
a matrix |
A |
a matrix |
Computes OLS solution for D = AB' (or D' = AB'), where D, A are known
mcrpure allows to resolve spectroscopic data to linear combination of individual spectra
and contributions using the pure variables approach.
mcrpure( x, ncomp, purevars = NULL, offset = 0.05, exclrows = NULL, exclcols = NULL, info = "" )mcrpure( x, ncomp, purevars = NULL, offset = 0.05, exclrows = NULL, exclcols = NULL, info = "" )
x |
spectra of mixtures (matrix or data frame). |
ncomp |
maximum number of components to calculate. |
purevars |
vector with indices for pure variables (optional, if you want to provide the variables directly). |
offset |
offset for correcting noise in computing maximum angles (should be value within [0, 1)). |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values). |
exclcols |
columns to be excluded from calculations (numbers, names or vector with logical values). |
info |
a short text with description of the case (optional). |
The method estimates purity of each variable and then uses the purest ones to decompose the spectral data into spectra ('resspec') and contributions ('rescont') of individual chemical components by ordinary least squares.
The pure variabes are identified using stepwise maximum angle calculations and described in detail in [1]. So the purity of a spectral variable (wavelength, wavenumber) is actually an angle (measured in degrees) between the variable and vector of ones for the first component; and between the variable and space formed by previously found pure variables for the other components.
Returns an object of mcrpure class with the following fields:
resspec |
matrix with resolved spectra. |
rescont |
matrix with resolved contributions. |
purevars |
indices of the selected pure variables. |
purevals |
purity values for the selected pure variables. |
purityspec |
purity spectra (matrix with purity values for each variable and component). |
expvar |
vector with explained variance for each component (in percent). |
cumexpvar |
vector with cumulative explained variance for each component (in percent). |
offset |
offset value used to compute the purity |
ncomp |
number of resolved components |
info |
information about the model, provided by user when build the model. |
More details and examples can be found in the Bookdown tutorial.
Sergey Kucheryavskiy ([email protected])
1. Willem Windig, Neal B. Gallagher, Jeremy M. Shaver, Barry M. Wise. A new approach for interactive self-modeling mixture analysis. Chemometrics and Intelligent Laboratory Systems, 77 (2005) 85–96. DOI: 10.1016/j.chemolab.2004.06.009
Methods for mcrpure objects:
summary.mcrpure |
shows some statistics for the case. |
unmix.mcrpure |
makes unmixing of new set of spectra. |
predict.mcrpure |
computes contributions by projection of new spectra to the resolved ones. |
Plotting methods for mcrpure objects:
plotPurity.mcrpure |
shows plot with maximum purity of each component. |
plotPuritySpectra.mcrpure |
shows plot with purity spectra. |
plotSpectra.mcr |
shows plot with resolved spectra. |
plotContributions.mcr |
shows plot with resolved contributions. |
plotVariance.mcr |
shows plot with explained variance. |
plotCumVariance.mcr |
shows plot with cumulative explained variance. |
library(mdatools) # resolve mixture of carbonhydrates Raman spectra data(carbs) m = mcrpure(carbs$D, ncomp = 3) # examples for purity spectra plot (you can select which components to show) par(mfrow = c(2, 1)) plotPuritySpectra(m) plotPuritySpectra(m, comp = 2:3) # you can do it manually and combine e.g. with original spectra par(mfrow = c(1, 1)) mdaplotg( list( "spectra" = prep.norm(carbs$D, "area"), "purity" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # show the maximum purity for each component par(mfrow = c(1, 1)) plotPurity(m) # plot cumulative and individual explained variance par(mfrow = c(1, 2)) plotVariance(m) plotCumVariance(m) # plot resolved spectra (all of them or individually) par(mfrow = c(2, 1)) plotSpectra(m) plotSpectra(m, comp = 2:3) # plot resolved contributions (all of them or individually) par(mfrow = c(2, 1)) plotContributions(m) plotContributions(m, comp = 2:3) # of course you can do this manually as well, e.g. show original # and resolved spectra par(mfrow = c(1, 1)) mdaplotg( list( "original" = prep.norm(carbs$D, "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # in case if you have reference spectra of components you can compare them with # the resolved ones: par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg( list( "pure" = prep.norm(mda.subset(mda.t(carbs$S), 1), "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l", lwd = c(3, 1) ) } # See bookdown tutorial for more details.library(mdatools) # resolve mixture of carbonhydrates Raman spectra data(carbs) m = mcrpure(carbs$D, ncomp = 3) # examples for purity spectra plot (you can select which components to show) par(mfrow = c(2, 1)) plotPuritySpectra(m) plotPuritySpectra(m, comp = 2:3) # you can do it manually and combine e.g. with original spectra par(mfrow = c(1, 1)) mdaplotg( list( "spectra" = prep.norm(carbs$D, "area"), "purity" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # show the maximum purity for each component par(mfrow = c(1, 1)) plotPurity(m) # plot cumulative and individual explained variance par(mfrow = c(1, 2)) plotVariance(m) plotCumVariance(m) # plot resolved spectra (all of them or individually) par(mfrow = c(2, 1)) plotSpectra(m) plotSpectra(m, comp = 2:3) # plot resolved contributions (all of them or individually) par(mfrow = c(2, 1)) plotContributions(m) plotContributions(m, comp = 2:3) # of course you can do this manually as well, e.g. show original # and resolved spectra par(mfrow = c(1, 1)) mdaplotg( list( "original" = prep.norm(carbs$D, "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l" ) # in case if you have reference spectra of components you can compare them with # the resolved ones: par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg( list( "pure" = prep.norm(mda.subset(mda.t(carbs$S), 1), "area"), "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area") ), col = c("gray", "red"), type = "l", lwd = c(3, 1) ) } # See bookdown tutorial for more details.
A wrapper for cbind() method with proper set of attributes
mda.cbind(...)mda.cbind(...)
... |
datasets (data frames or matrices) to bind |
the merged datasets
Convert data matrix to an image
mda.data2im(data)mda.data2im(data)
data |
data matrix |
The function converts data frame to a numeric matrix.
mda.df2mat(x, full = FALSE)mda.df2mat(x, full = FALSE)
x |
a data frame |
full |
logical, if TRUE number of dummy variables for a factor will be the same as number of levels, otherwise by one smaller |
If one or several columns of the data frame are factors they will be converted to a set of dummy variables. If any columns/rows were hidden in the data frame they will remain hidden in the matrix. If there are factors among the hidden columns, the corresponding dummy variables will be hidden as well.
All other attributes (names, axis names, etc.) will be inherited.
a numeric matrix
Exclude/hide columns in a dataset
mda.exclcols(x, ind)mda.exclcols(x, ind)
x |
dataset (data frame or matrix). |
ind |
indices of columns to exclude (numbers, names or logical values) |
The method assign attribute 'exclcols', which contains number of columns, which should be
excluded/hidden from calculations and plots (without removing them physically). The argument
ind should contain column numbers (excluding already hidden), names or logical values.
dataset with excluded columns
Exclude/hide rows in a dataset
mda.exclrows(x, ind)mda.exclrows(x, ind)
x |
dataset (data frame or matrix). |
ind |
indices of rows to exclude (numbers, names or logical values) |
The method assign attribute 'exclrows', which contains number of rows, which should be
excluded/hidden from calculations and plots (without removing them physically). The
argument ind should contain rows numbers (excluding already hidden), names or logical
values.
dataset with excluded rows
Returns a list with important data attributes (name, xvalues, excluded rows and columns, etc.)
mda.getattr(x)mda.getattr(x)
x |
a dataset |
Get indices of excluded rows or columns
mda.getexclind(excl, names, n)mda.getexclind(excl, names, n)
excl |
vector with excluded values (logical, text or numbers) |
names |
vector with names for rows or columns |
n |
number of rows or columns |
Convert image to data matrix
mda.im2data(img)mda.im2data(img)
img |
an image (3-way array) |
include colmns specified by user (earlier excluded using mda.exclcols)
mda.inclcols(x, ind)mda.inclcols(x, ind)
x |
dataset (data frame or matrix). |
ind |
number of excluded columns to include |
dataset with included columns.
include rows specified by user (earlier excluded using mda.exclrows)
mda.inclrows(x, ind)mda.inclrows(x, ind)
x |
dataset (data frame or matrix). |
ind |
number of excluded rows to include |
dataset with included rows
Removes excluded (hidden) rows and colmns from data
mda.purge(data)mda.purge(data)
data |
data frame or matrix with data |
Removes excluded (hidden) colmns from data
mda.purgeCols(data)mda.purgeCols(data)
data |
data frame or matrix with data |
Removes excluded (hidden) rows from data
mda.purgeRows(data)mda.purgeRows(data)
data |
data frame or matrix with data |
A wrapper for rbind() method with proper set of attributes
mda.rbind(...)mda.rbind(...)
... |
datasets (data frames or matrices) to bind |
the merged datasets
Set most important data attributes (name, xvalues, excluded rows and columns, etc.) to a dataset
mda.setattr(x, attrs, type = "all")mda.setattr(x, attrs, type = "all")
x |
a dataset |
attrs |
list with attributes |
type |
a text variable telling which attributes to set ('all', 'row', 'col') |
Remove background pixels from image data
mda.setimbg(data, bgpixels)mda.setimbg(data, bgpixels)
data |
a matrix with image data |
bgpixels |
vector with indices or logical values corresponding to background pixels |
Wrapper for show() method
mda.show(x, n = 50)mda.show(x, n = 50)
x |
data set |
n |
number of rows to show |
A wrapper for subset() method with proper set of attributed
mda.subset(x, subset = NULL, select = NULL)mda.subset(x, subset = NULL, select = NULL)
x |
dataset (data frame or matrix) |
subset |
which rows to keep (indices, names or logical values) |
select |
which columns to select (indices, names or logical values) |
The method works similar to the standard subset() method, with minor differences. First
of all it keeps (and correct, if necessary) all important attributes. If only columns are
selected, it keeps all excluded rows as excluded. If only rows are selected, it keeps all
excluded columns. If both rows and columns are selected it removed all excluded elements first
and then makes the subset.
The parameters subset and select may each be a vector with numbers or nanes
without excluded elements, or a logical expression.
a data with the subset
A wrapper for t() method with proper set of attributes
mda.t(x)mda.t(x)
x |
dataset (data frames or matrices) to transpose |
the transposed dataset
mdaplot is used to make different kinds of plot for one set of data objects.
mdaplot( data = NULL, ps = NULL, type = "p", pch = 16, col = NULL, bg = par("bg"), bwd = 0.8, border = NA, lty = 1, lwd = 1, cex = 1, cgroup = NULL, xlim = NULL, ylim = NULL, colmap = "default", labels = NULL, main = NULL, xlab = NULL, ylab = NULL, show.labels = FALSE, show.colorbar = !is.null(cgroup), show.lines = FALSE, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", show.axes = TRUE, xticks = NULL, yticks = NULL, xticklabels = NULL, yticklabels = NULL, xlas = 0, ylas = 0, lab.col = "darkgray", lab.cex = 0.65, show.excluded = FALSE, col.excluded = "#C0C0C0", nbins = 60, force.x.values = NA, opacity = 1, pch.colinv = FALSE, ... )mdaplot( data = NULL, ps = NULL, type = "p", pch = 16, col = NULL, bg = par("bg"), bwd = 0.8, border = NA, lty = 1, lwd = 1, cex = 1, cgroup = NULL, xlim = NULL, ylim = NULL, colmap = "default", labels = NULL, main = NULL, xlab = NULL, ylab = NULL, show.labels = FALSE, show.colorbar = !is.null(cgroup), show.lines = FALSE, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", show.axes = TRUE, xticks = NULL, yticks = NULL, xticklabels = NULL, yticklabels = NULL, xlas = 0, ylas = 0, lab.col = "darkgray", lab.cex = 0.65, show.excluded = FALSE, col.excluded = "#C0C0C0", nbins = 60, force.x.values = NA, opacity = 1, pch.colinv = FALSE, ... )
data |
a vector, matrix or a data.frame with data values. |
ps |
'plotseries' object, if NULL will be created based on the provided data values |
type |
type of the plot ("p", "d", "l", "b", "h", "e"). |
pch |
a character for markers (same as |
col |
a color for markers or lines (same as |
bg |
background color for scatter plots wich 'pch=21:25'. |
bwd |
a width of a bar as a percent of a maximum space available for each bar. |
border |
color for border of bars (if barplot is used) |
lty |
line type |
lwd |
line width |
cex |
scale factor for the marker |
cgroup |
a vector with values to use for make color groups. |
xlim |
limits for the x axis (if NULL, will be calculated automatically). |
ylim |
limits for the y axis (if NULL, will be calculated automatically). |
colmap |
a colormap to use for coloring the plot items. |
labels |
a vector with text labels for data points or one of the following: "names", "indices", "values". |
main |
an overall title for the plot (same as |
xlab |
a title for the x axis (same as |
ylab |
a title for the y axis (same as |
show.labels |
logical, show or not labels for the data objects. |
show.colorbar |
logical, show or not colorbar legend if color grouping is on. |
show.lines |
vector with two coordinates (x, y) to show horizontal and vertical line cross the point. |
show.grid |
logical, show or not a grid for the plot. |
grid.lwd |
line thinckness (width) for the grid. |
grid.col |
line color for the grid. |
show.axes |
logical, make a normal plot or show only elements (markers, lines, bars) without axes. |
xticks |
values for x ticks. |
yticks |
values for y ticks. |
xticklabels |
labels for x ticks. |
yticklabels |
labels for y ticks. |
xlas |
orientation of xticklabels. |
ylas |
orientation of yticklabels. |
lab.col |
color for data point labels. |
lab.cex |
size for data point labels. |
show.excluded |
logical, show or hide rows marked as excluded (attribute 'exclrows'). |
col.excluded |
color for the excluded objects (rows). |
nbins |
if scatter density plot is shown, number of segments to split the plot area into. (see also ?smoothScatter) |
force.x.values |
vector with corrected x-values for a bar plot (do not specify this manually). |
opacity |
opacity for plot colors (value between 0 and 1). |
pch.colinv |
allows to swap values for 'col' and 'bg' for scatter plots with 'pch' valyes from 21 to 25. |
... |
other plotting arguments. |
Most of the parameters are similar to what are used with standard plot function. The
differences are described below.
The function makes a plot of one set of objects. It can be a set of points (scatter plot),
bars, lines, scatter-lines, errorbars og an image. The data is organized as a data frame,
matrix or vector. For scatter and only first two columns will be used, for bar plot only
values from the first row. It is recommended to use mda.subset method if plot
should be made only for a subset of the data, especially if you have any excluded rows or
columns or other special attributed, described in the Bookdown tutorial.
If data is a data frame and contains one or more factors, they will be converted to a dummy
variables (using function mda.df2mat) and appears at the end (last columns) if
line or bar plot is selected.
The function allows to colorize lines and points according to values of a parameter
cgroup. The parameter must be a vector with the same elements as number of objects (rows)
in the data. The values are divided into up to eight intervals and for each interval a
particular color from a selected color scheme is assigned. Parameter show.colorbar
allows to turn off and on a color bar legend for this option.
The used color scheme is defined by the colmap parameter. The default scheme is based
on color brewer (colorbrewer2.org) diverging scheme with eight colors. There is also a gray
scheme (colmap = "gray") and user can define its own just by specifing the needed
sequence of colors (e.g. colmap = c("red", "yellow", "green"), two colors is minimum).
The scheme will then be generated automatically as a gradient among the colors.
Besides that the function allows to change tick values and corresponding tick labels for x and y axis, see Bookdown tutorial for more details.
Sergey Kucheryavskiy ([email protected])
mdaplotg - to make plots for several sets of data objects (groups of objects).
# See all examples in the tutorial.# See all examples in the tutorial.
Checks if elements of argument are valid color values
mdaplot.areColors(palette)mdaplot.areColors(palette)
palette |
vector with possibly color values (names, RGB, etc.) |
Format vector with values, so only significant decimal numbers are left.
mdaplot.formatValues(data, round.only = FALSE, digits = 3)mdaplot.formatValues(data, round.only = FALSE, digits = 3)
data |
vector or matrix with values |
round.only |
logical, do formatting or only round the values |
digits |
how many significant digits take into account |
Function takes into accound difference between values and the values themselves.
matrix with formatted values
Generate vector with color values for plot objects (lines, points, bars), depending on number of groups for the objects.
mdaplot.getColors( ngroups = NULL, cgroup = NULL, colmap = "default", opacity = 1, maxsplits = 64 )mdaplot.getColors( ngroups = NULL, cgroup = NULL, colmap = "default", opacity = 1, maxsplits = 64 )
ngroups |
number of colors to create. |
cgroup |
vector of values, used for color grouping of plot points or lines. |
colmap |
which colormap to use ('default', 'gray', 'old', or user defined in form c('col1', 'col2', ...)). |
opacity |
opacity for colors (between 0 and 1) |
maxsplits |
if contenuous values are used for color gruping - how many groups to create? |
Returns vector with generated color values
Calculates limits for x-axis depending on data values that have to be plotted, extra plot elements that have to be shown and margins.
mdaplot.getXAxisLim( ps, xlim, show.labels = FALSE, show.lines = FALSE, show.excluded = FALSE, bwd = 0.8 )mdaplot.getXAxisLim( ps, xlim, show.labels = FALSE, show.lines = FALSE, show.excluded = FALSE, bwd = 0.8 )
ps |
'plotseries' object. |
xlim |
limits provided by user |
show.labels |
logical, will data labels be shown on the plot |
show.lines |
logical or numeric with line coordinates to be shown on the plot. |
show.excluded |
logical, will excluded values be shown on the plot |
bwd |
if limits are computed for bar plot, this is a bar width (otherwise NULL) |
Returns a vector with two limits.
Prepare xticklabels for plot
mdaplot.getXTickLabels(xticklabels, xticks, excluded_cols)mdaplot.getXTickLabels(xticklabels, xticks, excluded_cols)
xticklabels |
xticklables provided by user (if any) |
xticks |
xticks (provided or computed) |
excluded_cols |
columns excluded from plot data (if any) |
Prepare xticks for plot
mdaplot.getXTicks(xticks, xlim, x_values = NULL, type = NULL)mdaplot.getXTicks(xticks, xlim, x_values = NULL, type = NULL)
xticks |
xticks provided by user (if any) |
xlim |
limits for x axis |
x_values |
x values for the plot data object |
type |
type of the plot |
Calculates limits for y-axis depending on data values that have to be plotted, extra plot elements that have to be shown and margins.
mdaplot.getYAxisLim( ps, ylim, show.lines = FALSE, show.excluded = FALSE, show.labels = FALSE, show.colorbar = FALSE )mdaplot.getYAxisLim( ps, ylim, show.lines = FALSE, show.excluded = FALSE, show.labels = FALSE, show.colorbar = FALSE )
ps |
'plotseries' object. |
ylim |
limits provided by user |
show.lines |
logical or numeric with line coordinates to be shown on the plot. |
show.excluded |
logical, will excluded values be shown on the plot |
show.labels |
logical, will data labels be shown on the plot |
show.colorbar |
logical, will colorbar be shown on the plot |
Returns a vector with two limits.
Prepare yticklabels for plot
mdaplot.getYTickLabels(yticklabels, yticks, excluded_rows)mdaplot.getYTickLabels(yticklabels, yticks, excluded_rows)
yticklabels |
yticklables provided by user (if any) |
yticks |
yticks (provided or computed) |
excluded_rows |
rows excluded from plot data (if any) |
Prepare yticks for plot
mdaplot.getYTicks(yticks, ylim, y_values = NULL, type = NULL)mdaplot.getYTicks(yticks, ylim, y_values = NULL, type = NULL)
yticks |
yticks provided by user (if any) |
ylim |
limits for y axis |
y_values |
y values for the plot data object |
type |
type of the plot |
Creates an empty axes plane for given parameters
mdaplot.plotAxes( xticklabels = NULL, yticklabels = NULL, xlim = xlim, ylim = ylim, xticks = NULL, yticks = NULL, main = NULL, xlab = NULL, ylab = NULL, xlas = 0, ylas = 0, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray" )mdaplot.plotAxes( xticklabels = NULL, yticklabels = NULL, xlim = xlim, ylim = ylim, xticks = NULL, yticks = NULL, main = NULL, xlab = NULL, ylab = NULL, xlas = 0, ylas = 0, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray" )
xticklabels |
labels for x ticks |
yticklabels |
labels for y ticks |
xlim |
vector with limits for x axis |
ylim |
vector with limits for y axis |
xticks |
values for x ticks |
yticks |
values for y ticks |
main |
main title for the plot |
xlab |
label for x axis |
ylab |
label for y axis |
xlas |
orientation of xticklabels |
ylas |
orientation of yticklabels |
show.grid |
logical, show or not axes grid |
grid.lwd |
line thinckness (width) for the grid |
grid.col |
line color for the grid |
Prepare colors based on palette and opacity value
mdaplot.prepareColors(palette, ncolors, opacity)mdaplot.prepareColors(palette, ncolors, opacity)
palette |
vector with main colors for current pallette |
ncolors |
number of colors to generate |
opacity |
opacity for the colors (one value or individual for each color) |
vector with colors
Shows a colorbar if plot has color grouping of elements (points or lines).
mdaplot.showColorbar( cgroup, colmap = "default", lab.col = "darkgray", lab.cex = 0.65 )mdaplot.showColorbar( cgroup, colmap = "default", lab.col = "darkgray", lab.cex = 0.65 )
cgroup |
a vector with values used to make color grouping of the elements |
colmap |
a colormap to be used for color generation |
lab.col |
color for legend labels |
lab.cex |
size for legend labels |
Shows horisontal and vertical lines on a plot.
mdaplot.showLines(point, lty = 2, lwd = 0.75, col = rgb(0.2, 0.2, 0.2))mdaplot.showLines(point, lty = 2, lwd = 0.75, col = rgb(0.2, 0.2, 0.2))
point |
vector with two values: x coordinate for vertical point y for horizontal |
lty |
line type |
lwd |
line width |
col |
color of lines |
If it is needed to show only one line, the other coordinate shall be set to NA.
mdaplotg is used to make different kinds of plots or their combination for several sets
of objects.
mdaplotg( data, groupby = NULL, type = "p", pch = 16, lty = 1, lwd = 1, cex = 1, col = NULL, bwd = 0.8, legend = NULL, xlab = NULL, ylab = NULL, main = NULL, labels = NULL, ylim = NULL, xlim = NULL, colmap = "default", legend.position = "topright", show.legend = TRUE, show.labels = FALSE, show.lines = FALSE, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", xticks = NULL, xticklabels = NULL, yticks = NULL, yticklabels = NULL, show.excluded = FALSE, lab.col = "darkgray", lab.cex = 0.65, xlas = 1, ylas = 1, opacity = 1, ... )mdaplotg( data, groupby = NULL, type = "p", pch = 16, lty = 1, lwd = 1, cex = 1, col = NULL, bwd = 0.8, legend = NULL, xlab = NULL, ylab = NULL, main = NULL, labels = NULL, ylim = NULL, xlim = NULL, colmap = "default", legend.position = "topright", show.legend = TRUE, show.labels = FALSE, show.lines = FALSE, show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", xticks = NULL, xticklabels = NULL, yticks = NULL, yticklabels = NULL, show.excluded = FALSE, lab.col = "darkgray", lab.cex = 0.65, xlas = 1, ylas = 1, opacity = 1, ... )
data |
a matrix, data frame or a list with data values (see details below). |
groupby |
one or several factors used to create groups of data matrix rows (works if data is a matrix) |
type |
type of the plot ('p', 'l', 'b', 'h', 'e'). |
pch |
a character for markers (same as |
lty |
the line type (same as |
lwd |
the line width (thickness) (same as |
cex |
the cex factor for the markers (same as |
col |
colors for the plot series |
bwd |
a width of a bar as a percent of a maximum space available for each bar. |
legend |
a vector with legend elements (if NULL, no legend will be shown). |
xlab |
a title for the x axis (same as |
ylab |
a title for the y axis (same as |
main |
an overall title for the plot (same as |
labels |
what to use as labels ('names' - row names, 'indices' - row indices, 'values' - values). |
ylim |
limits for the y axis (if NULL, will be calculated automatically). |
xlim |
limits for the x axis (if NULL, will be calculated automatically). |
colmap |
a colormap to generate colors if |
legend.position |
position of the legend ('topleft', 'topright', 'top', 'bottomleft', 'bottomright', 'bottom'). |
show.legend |
logical, show or not legend for the data objects. |
show.labels |
logical, show or not labels for the data objects. |
show.lines |
vector with two coordinates (x, y) to show horizontal and vertical line cross the point. |
show.grid |
logical, show or not a grid for the plot. |
grid.lwd |
line thinckness (width) for the grid |
grid.col |
line color for the grid |
xticks |
tick values for x axis. |
xticklabels |
labels for x ticks. |
yticks |
tick values for y axis. |
yticklabels |
labels for y ticks. |
show.excluded |
logical, show or hide rows marked as excluded (attribute 'exclrows') |
lab.col |
color for data point labels. |
lab.cex |
size for data point labels. |
xlas |
orientation of xticklabels |
ylas |
orientation of yticklabels |
opacity |
opacity for plot colors (value between 0 and 1) |
... |
other plotting arguments. |
The mdaplotg function is used to make a plot with several sets of objects. Simply
speaking, use it when you need a plot with legend. For example to show line plot with spectra
from calibration and test set, scatter plot with height and weight values for women and men, and
so on.
Most of the parameters are similar to mdaplot, the difference is described below.
The data should be organized as a list, every item is a matrix (or data frame) with data for one
set of objects. Alternatively you can provide data as a matrix and use parameter
groupby to create the groups. See tutorial for more details.
There is no color grouping option, because color is used to separate the sets. Marker symbol, line style and type, etc. can be defined as a single value (one for all sets) and as a vector with one value for each set.
Sergey Kucheryavskiy ([email protected])
Create and return vector with legend values
mdaplotg.getLegend(ps, data.names, legend = NULL)mdaplotg.getLegend(ps, data.names, legend = NULL)
ps |
list with plot series |
data.names |
names of the data sets |
legend |
legend values provided by user |
vector of text values for the legend
Compute x-axis limits for mdaplotg
mdaplotg.getXLim( ps, xlim, show.excluded, show.legend, show.labels, legend.position, bwd = NULL )mdaplotg.getXLim( ps, xlim, show.excluded, show.legend, show.labels, legend.position, bwd = NULL )
ps |
list with plotseries |
xlim |
limits provided by user |
show.excluded |
logical, will excluded values also be shown |
show.legend |
will legend be shown on the plot |
show.labels |
will labels be shown on the plot |
legend.position |
position of legend on the plot (if shown) |
bwd |
size of bar for bar plot |
vector with two values
Compute y-axis limits for mdaplotg
mdaplotg.getYLim( ps, ylim, show.excluded, show.legend, legend.position, show.labels )mdaplotg.getYLim( ps, ylim, show.excluded, show.legend, legend.position, show.labels )
ps |
list with plotseries |
ylim |
limits provided by user |
show.excluded |
logical, will excluded values also be shown |
show.legend |
will legend be shown on the plot |
legend.position |
position of legend on the plot (if shown) |
show.labels |
logical, will data ponit labels also be shown |
vector with two values
Prepare data for mdaplotg
mdaplotg.prepareData(data, type, groupby)mdaplotg.prepareData(data, type, groupby)
data |
datasets (in form of list, matrix or data frame) |
type |
vector with type for dataset |
groupby |
factor or data frame with factors - used to split data matrix into groups |
list of datasets
The method should prepare data as a list of datasets (matrices or data frames). One list element will be used to create one plot series.
If 'data' is matrix or data frame and not 'groupby' parameter is provided, then every row will be taken as separate set. This option is available only for line or bar plots.
Check mdaplotg parameters and replicate them if necessary
mdaplotg.processParam(param, name, is.type, ngroups)mdaplotg.processParam(param, name, is.type, ngroups)
param |
A parameter to check |
name |
name of the parameter (needed for error message) |
is.type |
function to use for checking parameter type |
ngroups |
number of groups (plot series) |
Shows a legend for plot elements or their groups.
mdaplotg.showLegend( legend, col, pt.bg = NA, pch = NULL, lty = NULL, lwd = NULL, cex = 1, bty = "o", position = "topright", plot = TRUE, ... )mdaplotg.showLegend( legend, col, pt.bg = NA, pch = NULL, lty = NULL, lwd = NULL, cex = 1, bty = "o", position = "topright", plot = TRUE, ... )
legend |
vector with text elements for the legend items |
col |
vector with color values for the legend items |
pt.bg |
vector with background colors for the legend items (e.g. for pch = 21:25) |
pch |
vector with marker symbols for the legend items |
lty |
vector with line types for the legend items |
lwd |
vector with line width values for the legend items |
cex |
vector with cex factor for the points |
bty |
border type for the legend |
position |
legend position ("topright", "topleft', "bottomright", "bottomleft", "top", "bottom") |
plot |
logical, show legend or just calculate and return its size |
... |
other parameters |
mdaplotyy create line plot for two plot series and uses separate y-axis for each.
mdaplotyy( data, type = "l", col = mdaplot.getColors(2), lty = c(1, 1), lwd = c(1, 1), pch = (if (type == "b") c(16, 16) else c(NA, NA)), cex = 1, xlim = NULL, ylim = NULL, main = attr(data, "name"), xlab = attr(data, "xaxis.name"), ylab = rownames(data), labels = "values", show.labels = FALSE, lab.cex = 0.65, lab.col = "darkgray", show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", xticks = NULL, xticklabels = NULL, xlas = 0, ylas = 0, show.legend = TRUE, legend.position = "topright", legend = ylab, ... )mdaplotyy( data, type = "l", col = mdaplot.getColors(2), lty = c(1, 1), lwd = c(1, 1), pch = (if (type == "b") c(16, 16) else c(NA, NA)), cex = 1, xlim = NULL, ylim = NULL, main = attr(data, "name"), xlab = attr(data, "xaxis.name"), ylab = rownames(data), labels = "values", show.labels = FALSE, lab.cex = 0.65, lab.col = "darkgray", show.grid = TRUE, grid.lwd = 0.5, grid.col = "lightgray", xticks = NULL, xticklabels = NULL, xlas = 0, ylas = 0, show.legend = TRUE, legend.position = "topright", legend = ylab, ... )
data |
a matrix or a data.frame with two rows of values. |
type |
type of the plot ("l" or "b"). |
col |
a color for markers or lines (same as |
lty |
line type for each series (two values) |
lwd |
line width for each series (two values) |
pch |
a character for markers (same as |
cex |
scale factor for the markers |
xlim |
limits for the x axis (if NULL, will be calculated automatically). |
ylim |
limits for the y axis, either list with two vectors (one for each series) or NULL. |
main |
an overall title for the plot (same as |
xlab |
a title for the x axis (same as |
ylab |
a title for each of the two y axis (as a vector of two text values). |
labels |
a vector with text labels for data points or one of the following: "names", "indices", "values". |
show.labels |
logical, show or not labels for the data objects. |
lab.cex |
size for data point labels. |
lab.col |
color for data point labels. |
show.grid |
logical, show or not a grid for the plot. |
grid.lwd |
line thinckness (width) for the grid. |
grid.col |
line color for the grid. |
xticks |
values for x ticks. |
xticklabels |
labels for x ticks. |
xlas |
orientation of xticklabels. |
ylas |
orientation of yticklabels (will be applied to both y axes). |
show.legend |
logical show legend with name of each plot series or not |
legend.position |
position of legend if it must be shown |
legend |
values for the legend |
... |
other plotting arguments. |
This plot has properties both mdaplot and mdaplotg, so when you specify color,
line properties etc. you have to do it for both plot series.
Sergey Kucheryavskiy ([email protected])
mdaplotg - to make plots for several sets of data objects (groups of objects).
# See all examples in the tutorial.# See all examples in the tutorial.
This package contains classes and functions for most common methods used in Chemometrics. For a complete list of functions, use library(help = 'mdatools').
The project is hosted on GitHub (https://svkucheryavski.github.io/mdatools/), there you can also find a Bookdown user tutorial explaining most important features of the package. There is also a dedicated YouTube channel (https://www.youtube.com/channel/UCox0H4utfMq4FIu2kymuyTA) with introductory Chemometric course with examples based on mdatools functionality.
Every method is represented by two classes: a model class for keeping all parameters and information about the model, and a class for keeping and visualising results of applying the model to particular data values.
Every model class, e.g. pls, has all needed functionality implemented as class methods, including model calibration, validation (test set and cross-validation), visualisation of the calibration and validation results with various plots and summary statistics.
So far the following modelling and validation methods are implemented:
pca, pcares |
Principal Component Analysis (PCA). |
pls, plsres |
Partial Least Squares regression (PLS). |
simca, simcares |
Soft Independent Modelling of Class Analogues (SIMCA) |
simcam, simcamres |
SIMCA for multiple classes case (SIMCA) |
plsda, plsdares |
Partial Least Squares Dscriminant Analysis (PLS-DA). |
randtest |
Randomization test for PLS-regression. |
ipls |
Interval PLS variable. |
mcrals |
Multivariate Curve Resolution with Alternating Least Squares. |
mcrpure |
Multivariate Curve Resolution with Purity approach. |
Methods for data preprocessing:
prep.autoscale |
data mean centering and/or standardization. |
prep.savgol |
Savitzky-Golay transformation. |
prep.snv |
Standard normal variate. |
prep.msc |
Multiplicative scatter correction. |
prep.norm |
Spectra normalization. |
prep.alsbasecorr |
Baseline correction with Asymmetric Least Squares. |
All plotting methods are based on two functions, mdaplot and mdaplotg. The functions extend the basic functionality of R plots and allow to make automatic legend and color grouping of data points or lines with colorbar legend, automatically adjust axes limits when several data groups are plotted and so on.
Sergey Kucheryavskiy ([email protected])
pca is used to build and explore a principal component analysis (PCA) model.
pca( x, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, exclrows = NULL, exclcols = NULL, x.test = NULL, method = "svd", rand = NULL, lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, info = "" )pca( x, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, exclrows = NULL, exclcols = NULL, x.test = NULL, method = "svd", rand = NULL, lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, info = "" )
x |
calibration data (matrix or data frame). |
ncomp |
maximum number of components to calculate. |
center |
logical, do mean centering of data or not. |
scale |
logical, do standardization of data or not. |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
exclcols |
columns to be excluded from calculations (numbers, names or vector with logical values) |
x.test |
test data (matrix or data frame). |
method |
method to compute principal components ("svd", "nipals"). |
rand |
vector with parameters for randomized PCA methods (if NULL, conventional PCA is used instead) |
lim.type |
which method to use for calculation of critical limits for residual distances (see details) |
alpha |
significance level for extreme limits for T2 and Q disances. |
gamma |
significance level for outlier limits for T2 and Q distances. |
info |
a short text with model description. |
Note, that from v. 0.10.0 cross-validation is no more supported in PCA.
If number of components is not specified, a minimum of number of objects - 1 and number of
variables in calibration set is used. One can also specified an optimal number of component,
once model is calibrated (ncomp.selected). The optimal number of components is used to
build a residuals distance plot, as well as for SIMCA classification.
If some of rows of calibration set should be excluded from calculations (e.g. because they are
outliers) you can provide row numbers, names, or logical values as parameter exclrows. In
this case they will be completely ignored we model is calibrated. However, score and residuls
distances will be computed for these rows as well and then hidden. You can show them
on corresponding plots by using parameter show.excluded = TRUE.
It is also possible to exclude selected columns from calculations by provideing parameter
exclcols in form of column numbers, names or logical values. In this case loading matrix
will have zeros for these columns. This allows to compute PCA models for selected variables
without removing them physically from a dataset.
Take into account that if you see other packages to make plots (e.g. ggplot2) you will not be able to distinguish between hidden and normal objects.
By default loadings are computed for the original dataset using either SVD or NIPALS algorithm.
However, for datasets with large number of rows (e.g. hyperspectral images), there is a
possibility to run algorithms based on random permutations [1, 2]. In this case you have
to define parameter rand as a vector with two values: p - oversampling parameter
and k - number of iterations. Usually rand = c(15, 0) or rand = c(5, 1)
are good options, which give quite almost precise solution but much faster.
There are several ways to calculate critical limits for orthogonal (Q, q) and score (T2, h)
distances. In mdatools you can specify one of the following methods via parameter
lim.type: "jm" Jackson-Mudholkar approach [3], "chisq" - method based on
chi-square distribution [4], "ddmoments" and "ddrobust" - related to data
driven method proposed in [5]. The "ddmoments" is based on method of moments for
estimation of distribution parameters (also known as "classical" approach) while
"ddrobust" is based in robust estimation.
If lim.type="chisq" or lim.type="jm" is used, only limits for Q-distances are
computed based on corresponding approach, limits for T2-distances are computed using
Hotelling's T-squared distribution. The methods utilizing the data driven approach calculate
limits for combination of the distances bases on chi-square distribution and parameters
estimated from the calibration data.
The critical limits are calculated for a significance level defined by parameter 'alpha'.
You can also specify another parameter, 'gamma', which is used to calculate acceptance
limit for outliers (shown as dashed line on residual distance plot).
You can also recalculate the limits for existent model by using different values for alpha and
gamme, without recomputing the model itself. In this case use the following code (it is assumed
that you current PCA/SIMCA model is stored in variable m):
m = setDistanceLimits(m, lim.type, alpha, gamma).
In case of PCA the critical limits are just shown on residual plot as lines and can be used for
detection of extreme objects (solid line) and outliers (dashed line). When PCA model is used for
classification in SIMCA (see simca) the limits are also employed for
classification of objects.
Returns an object of pca class with following fields:
ncomp |
number of components included to the model. |
ncomp.selected |
selected (optimal) number of components. |
loadings |
matrix with loading values (nvar x ncomp). |
eigenvals |
vector with eigenvalues for all existent components. |
expvar |
vector with explained variance for each component (in percent). |
cumexpvar |
vector with cumulative explained variance for each component (in percent). |
T2lim |
statistical limit for T2 distance. |
Qlim |
statistical limit for Q residuals. |
info |
information about the model, provided by user when build the model. |
calres |
an object of class |
testres |
an object of class |
More details and examples can be found in the Bookdown tutorial.
Sergey Kucheryavskiy ([email protected])
1. N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53 (2010) pp. 217-288.
2. S. Kucheryavskiy, Blessing of randomness against the curse of dimensionality, Journal of Chemometrics, 32 (2018).
3. J.E. Jackson, A User's Guide to Principal Components, John Wiley & Sons, New York, NY (1991).
4. A.L. Pomerantsev, Acceptance areas for multivariate classification derived by projection methods, Journal of Chemometrics, 22 (2008) pp. 601-609.
5. A.L. Pomerantsev, O.Ye. Rodionova, Concept and role of extreme objects in PCA/SIMCA, Journal of Chemometrics, 28 (2014) pp. 429-438.
Methods for pca objects:
plot.pca |
makes an overview of PCA model with four plots. |
summary.pca |
shows some statistics for the model. |
categorize.pca |
categorize data rows as "normal", "extreme" or "outliers". |
selectCompNum.pca |
set number of optimal components in the model |
setDistanceLimits.pca |
set critical limits for residuals |
predict.pca |
applies PCA model to a new data. |
Plotting methods for pca objects:
plotScores.pca |
shows scores plot. |
plotLoadings.pca |
shows loadings plot. |
plotVariance.pca |
shows explained variance plot. |
plotCumVariance.pca |
shows cumulative explained variance plot. |
plotResiduals.pca |
shows plot for residual distances (Q vs. T2). |
plotBiplot.pca |
shows bi-plot. |
plotExtreme.pca |
shows extreme plot. |
plotT2DoF |
plot with degrees of freedom for score distance. |
plotQDoF |
plot with degrees of freedom for orthogonal distance. |
plotDistDoF |
plot with degrees of freedom for both distances. |
Most of the methods for plotting data are also available for PCA results (pcares)
objects. Also check pca.mvreplace, which replaces missing values in a data matrix
with approximated using iterative PCA decomposition.
library(mdatools) ### Examples for PCA class ## 1. Make PCA model for People data with autoscaling data(people) model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) summary(model) plot(model, show.labels = TRUE) ## 2. Show scores and loadings plots for the model par(mfrow = c(2, 2)) plotScores(model, comp = c(1, 3), show.labels = TRUE) plotScores(model, comp = 2, type = "h", show.labels = TRUE) plotLoadings(model, comp = c(1, 3), show.labels = TRUE) plotLoadings(model, comp = c(1, 2), type = "h", show.labels = TRUE) par(mfrow = c(1, 1)) ## 3. Show residual distance and variance plots for the model par(mfrow = c(2, 2)) plotVariance(model, type = "h") plotCumVariance(model, show.labels = TRUE, legend.position = "bottomright") plotResiduals(model, show.labels = TRUE) plotResiduals(model, ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1))library(mdatools) ### Examples for PCA class ## 1. Make PCA model for People data with autoscaling data(people) model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) summary(model) plot(model, show.labels = TRUE) ## 2. Show scores and loadings plots for the model par(mfrow = c(2, 2)) plotScores(model, comp = c(1, 3), show.labels = TRUE) plotScores(model, comp = 2, type = "h", show.labels = TRUE) plotLoadings(model, comp = c(1, 3), show.labels = TRUE) plotLoadings(model, comp = c(1, 2), type = "h", show.labels = TRUE) par(mfrow = c(1, 1)) ## 3. Show residual distance and variance plots for the model par(mfrow = c(2, 2)) plotVariance(model, type = "h") plotCumVariance(model, show.labels = TRUE, legend.position = "bottomright") plotResiduals(model, show.labels = TRUE) plotResiduals(model, ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1))
Calibrates (builds) a PCA model for given data and parameters
pca.cal(x, ncomp, center, scale, method, rand = NULL)pca.cal(x, ncomp, center, scale, method, rand = NULL)
x |
matrix with data values |
ncomp |
number of principal components to calculate |
center |
logical, do mean centering or not |
scale |
logical, do standardization or not |
method |
algorithm for compiting PC space (only 'svd' and 'nipals' are supported so far) |
rand |
vector with parameters for randomized PCA methods (if NULL, conventional PCA is used instead) |
an object with calibrated PCA model
Low-dimensional approximation of data matrix X
pca.getB(X, k = NULL, rand = NULL, dist = "unif")pca.getB(X, k = NULL, rand = NULL, dist = "unif")
X |
data matrix |
k |
rank of X (number of components) |
rand |
a vector with two values - number of iterations (q) and oversmapling parameter (p) |
dist |
distribution for generating random numbers, 'unif' or 'norm' |
pca.mvreplace is used to replace missing values in a data matrix with
approximated by iterative PCA decomposition.
pca.mvreplace( x, center = TRUE, scale = FALSE, maxncomp = 10, expvarlim = 0.95, covlim = 10^-6, maxiter = 100 )pca.mvreplace( x, center = TRUE, scale = FALSE, maxncomp = 10, expvarlim = 0.95, covlim = 10^-6, maxiter = 100 )
x |
a matrix with data, containing missing values. |
center |
logical, do centering of data values or not. |
scale |
logical, do standardization of data values or not. |
maxncomp |
maximum number of components in PCA model. |
expvarlim |
minimum amount of variance, explained by chosen components (used for selection of optimal number of components in PCA models). |
covlim |
convergence criterion. |
maxiter |
maximum number of iterations if convergence criterion is not met. |
The function uses iterative PCA modeling of the data to approximate and impute missing values. The result is most optimal for data sets with low or moderate level of noise and with number of missing values less than 10% for small dataset and up to 20% for large data.
Returns the same matrix x where missing values are replaced with approximated.
Sergey Kucheryavskiy ([email protected])
Philip R.C. Nelson, Paul A. Taylor, John F. MacGregor. Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35 (1), 1996.
library(mdatools) ## A very simple example of imputing missing values in a data with no noise # generate a matrix with values s = 1:6 odata = cbind(s, 2*s, 4*s) # make a matrix with missing values mdata = odata mdata[5, 2] = mdata[2, 3] = NA # replace missing values with approximated rdata = pca.mvreplace(mdata, scale = TRUE) # show all matrices together show(cbind(odata, mdata, round(rdata, 2)))library(mdatools) ## A very simple example of imputing missing values in a data with no noise # generate a matrix with values s = 1:6 odata = cbind(s, 2*s, 4*s) # make a matrix with missing values mdata = odata mdata[5, 2] = mdata[2, 3] = NA # replace missing values with approximated rdata = pca.mvreplace(mdata, scale = TRUE) # show all matrices together show(cbind(odata, mdata, round(rdata, 2)))
Calculates principal component space using non-linear iterative partial least squares algorithm (NIPALS)
pca.nipals(x, ncomp = min(ncol(x), nrow(x) - 1), tol = 10^-10)pca.nipals(x, ncomp = min(ncol(x), nrow(x) - 1), tol = 10^-10)
x |
a matrix with data values (preprocessed) |
ncomp |
number of components to calculate |
tol |
tolerance (if difference in eigenvalues is smaller - convergence achieved) |
a list with scores, loadings and eigenvalues for the components
Geladi, Paul; Kowalski, Bruce (1986), "Partial Least Squares Regression:A Tutorial", Analytica Chimica Acta 185: 1-17
Runs one of the selected PCA methods
pca.run(x, ncomp, method, rand = NULL)pca.run(x, ncomp, method, rand = NULL)
x |
data matrix |
ncomp |
number of components |
method |
name of PCA methods ('svd', 'nipals') |
rand |
parameters for randomized algorithm (if not NULL) |
Computes principal component space using Singular Values Decomposition
pca.svd(x, ncomp = min(ncol(x), nrow(x) - 1))pca.svd(x, ncomp = min(ncol(x), nrow(x) - 1))
x |
a matrix with data values (preprocessed) |
ncomp |
number of components to calculate |
a list with scores, loadings and eigenvalues for the components
pcares is used to store and visualise results for PCA decomposition.
pcares(...)pcares(...)
... |
all arguments supported by |
In fact pcares is a wrapper for ldecomp - general class for storing
results for linear decomposition X = TP' + E. So, most of the methods, arguments and
returned values are inherited from ldecomp.
There is no need to create a pcares object manually, it is created automatically when
build a PCA model (see pca) or apply the model to a new data (see
predict.pca). The object can be used to show summary and plots for the results.
It is assumed that data is a matrix or data frame with I rows and J columns.
Returns an object (list) of class pcares and ldecomp with following fields:
scores |
matrix with score values (I x A). |
residuals |
matrix with data residuals (I x J). |
T2 |
matrix with score distances (I x A). |
Q |
matrix with orthogonal distances (I x A). |
ncomp.selected |
selected number of components. |
expvar |
explained variance for each component. |
cumexpvar |
cumulative explained variance. |
Methods for pcares objects:
print.pcares |
shows information about the object. |
summary.pcares |
shows statistics for the PCA results. |
Methods, inherited from ldecomp class:
plotScores.ldecomp |
makes scores plot. |
plotVariance.ldecomp |
makes explained variance plot. |
plotCumVariance.ldecomp |
makes cumulative explained variance plot. |
plotResiduals.ldecomp |
makes Q vs. T2 distance plot. |
### Examples for PCA results class library(mdatools) ## 1. Make a model for every odd row of People data ## and apply it to the objects from every even row data(people) x = people[seq(1, 32, 2), ] x.new = people[seq(1, 32, 2), ] model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) res = predict(model, x.new) summary(res) plot(res) ## 1. Make PCA model for People data with autoscaling ## and full cross-validation and get calibration results data(people) model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) res = model$calres summary(res) plot(res) ## 2. Show scores plots for the results par(mfrow = c(2, 2)) plotScores(res) plotScores(res, cgroup = people[, "Beer"], show.labels = TRUE) plotScores(res, comp = c(1, 3), show.labels = TRUE) plotScores(res, comp = 2, type = "h", show.labels = TRUE) par(mfrow = c(1, 1)) ## 3. Show residuals and variance plots for the results par(mfrow = c(2, 2)) plotVariance(res, type = "h") plotCumVariance(res, show.labels = TRUE) plotResiduals(res, show.labels = TRUE, cgroup = people[, "Sex"]) plotResiduals(res, ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1))### Examples for PCA results class library(mdatools) ## 1. Make a model for every odd row of People data ## and apply it to the objects from every even row data(people) x = people[seq(1, 32, 2), ] x.new = people[seq(1, 32, 2), ] model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) res = predict(model, x.new) summary(res) plot(res) ## 1. Make PCA model for People data with autoscaling ## and full cross-validation and get calibration results data(people) model = pca(people, scale = TRUE, info = "Simple PCA model") model = selectCompNum(model, 4) res = model$calres summary(res) plot(res) ## 2. Show scores plots for the results par(mfrow = c(2, 2)) plotScores(res) plotScores(res, cgroup = people[, "Beer"], show.labels = TRUE) plotScores(res, comp = c(1, 3), show.labels = TRUE) plotScores(res, comp = 2, type = "h", show.labels = TRUE) par(mfrow = c(1, 1)) ## 3. Show residuals and variance plots for the results par(mfrow = c(2, 2)) plotVariance(res, type = "h") plotCumVariance(res, show.labels = TRUE) plotResiduals(res, show.labels = TRUE, cgroup = people[, "Sex"]) plotResiduals(res, ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1))
Dataset for showing how mdatools works with images. It is an RGB image represented as 3-way array.
data(people)data(people)
a 3-way array (height x width x channels).
This is an image with pellets of four different colours mixed in a glas volume.
Dataset for exploratory analysis with 32 objects (male and female persons) and 12 variables.
data(people)data(people)
a matrix with 32 observations (persons) and 12 variables.
[, 1] |
Height in cm. |
[, 2] |
Weight in kg. |
[, 3] |
Hair length (-1 for short, +1 for long). |
[, 4] |
Shoe size (EU standard). |
[, 5] |
Age, years. |
[, 6] |
Income, euro per year. |
[, 7] |
Beer consumption, liters per year. |
[, 8] |
Wine consumption, liters per year. |
[, 9] |
Sex (-1 for male, +1 for female). |
[, 10] |
Swimming ability (index, based on 500 m swimming time). |
[, 11] |
Region (-1 for Scandinavia, +1 for Mediterranean. |
[, 12] |
IQ (European standardized test). |
The data was taken from the book [1] and is in fact a small subset of a pan-European demographic survey. It includes information about 32 persons, 16 represent northern Europe (Scandinavians) and 16 are from the Mediterranean regions. In both groups there are 8 male and 8 female persons. The data includes both quantitative and qualitative variables and is particularly useful for benchmarking exploratory data analysis methods.
1. K. Esbensen. Multivariate Data Analysis in Practice. Camo, 2002.
Computes pseudo-inverse matrix using SVD
pinv(data)pinv(data)
data |
a matrix with data values to compute inverse for |
Generic plot function for classification results.
Alias for plotPredictions.classres.
## S3 method for class 'classres' plot(x, ...)## S3 method for class 'classres' plot(x, ...)
x |
classification results (object of class |
... |
other arguments for |
Shows a plot for iPLS results.
## S3 method for class 'ipls' plot(x, ...)## S3 method for class 'ipls' plot(x, ...)
x |
a (object of class |
... |
other arguments. |
See details for plotSelection.ipls.
Plot summary for MCR model
## S3 method for class 'mcr' plot(x, ...)## S3 method for class 'mcr' plot(x, ...)
x |
|
... |
other parameters |
Shows a set of plots (scores, loadings, residuals and explained variance) for PCA model.
## S3 method for class 'pca' plot( x, comp = c(1, 2), ncomp = x$ncomp.selected, show.labels = FALSE, show.legend = TRUE, ... )## S3 method for class 'pca' plot( x, comp = c(1, 2), ncomp = x$ncomp.selected, show.labels = FALSE, show.legend = TRUE, ... )
x |
a PCA model (object of class |
comp |
vector with two values - number of components to show the scores and loadings plots for |
ncomp |
number of components to show the residuals plot for |
show.labels |
logical, show or not labels for the plot objects |
show.legend |
logical, show or not a legend on the plot |
... |
other arguments |
See examples in help for pca function.
Show several plots to give an overview about the PCA results
## S3 method for class 'pcares' plot(x, comp = c(1, 2), ncomp = x$ncomp.selected, show.labels = TRUE, ...)## S3 method for class 'pcares' plot(x, comp = c(1, 2), ncomp = x$ncomp.selected, show.labels = TRUE, ...)
x |
PCA results (object of class |
comp |
which components to show the scores plot for (can be one value or vector with two values). |
ncomp |
how many components to use for showing the residual distance plot |
show.labels |
logical, show or not labels for the plot objects |
... |
other arguments |
Shows a set of plots (x residuals, regression coefficients, RMSE and predictions) for PLS model.
## S3 method for class 'pls' plot(x, ncomp = x$ncomp.selected, ny = 1, show.legend = TRUE, ...)## S3 method for class 'pls' plot(x, ncomp = x$ncomp.selected, ny = 1, show.legend = TRUE, ...)
x |
a PLS model (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
ny |
which y variable to show the summary for (if NULL, will be shown for all) |
show.legend |
logical, show or not a legend on the plot |
... |
other arguments |
See examples in help for pls function.
Shows a set of plots (x residuals, regression coefficients, misclassification ratio and predictions) for PLS-DA model.
## S3 method for class 'plsda' plot(x, ncomp = x$ncomp.selected, nc = 1, show.legend = TRUE, ...)## S3 method for class 'plsda' plot(x, ncomp = x$ncomp.selected, nc = 1, show.legend = TRUE, ...)
x |
a PLS-DA model (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
nc |
which class to show the plots |
show.legend |
logical, show or not a legend on the plot |
... |
other arguments |
See examples in help for plsda function.
Shows a set of plots (x residuals, y variance, classification performance and predictions) for PLS-DA results.
## S3 method for class 'plsdares' plot(x, nc = 1, ncomp = x$ncomp.selected, show.labels = FALSE, ...)## S3 method for class 'plsdares' plot(x, nc = 1, ncomp = x$ncomp.selected, show.labels = FALSE, ...)
x |
PLS-DA results (object of class |
nc |
which class to show the plot for |
ncomp |
how many components to use |
show.labels |
logical, show or not labels for the plot objects |
... |
other arguments |
See examples in help for pls function.
Shows a set of plots for PLS results.
## S3 method for class 'plsres' plot(x, ncomp = x$ncomp.selected, ny = 1, show.labels = FALSE, ...)## S3 method for class 'plsres' plot(x, ncomp = x$ncomp.selected, ny = 1, show.labels = FALSE, ...)
x |
PLS results (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
ny |
which y variable to show the summary for (if NULL, will be shown for all) |
show.labels |
logical, show or not labels for the plot objects |
... |
other arguments |
See examples in help for plsres function.
Makes a bar plot with alpha values for each component.
## S3 method for class 'randtest' plot(x, main = "Alpha", xlab = "Components", ylab = "", ...)## S3 method for class 'randtest' plot(x, main = "Alpha", xlab = "Components", ylab = "", ...)
x |
results of randomization test (object of class 'randtest') |
main |
main title for the plot |
xlab |
label for x axis |
ylab |
label for y axis |
... |
other optional arguments |
See examples in help for randtest function.
Shows plot with regression coefficient values for every predictor variable (x)
## S3 method for class 'regcoeffs' plot( x, ncomp = 1, ny = 1, type = (if (x$nvar > 30) "l" else "h"), col = c(mdaplot.getColors(1), "lightgray"), show.lines = c(NA, 0), show.ci = FALSE, alpha = 0.05, ylab = paste0("Coefficients (", x$respnames[ny], ")"), ... )## S3 method for class 'regcoeffs' plot( x, ncomp = 1, ny = 1, type = (if (x$nvar > 30) "l" else "h"), col = c(mdaplot.getColors(1), "lightgray"), show.lines = c(NA, 0), show.ci = FALSE, alpha = 0.05, ylab = paste0("Coefficients (", x$respnames[ny], ")"), ... )
x |
regression coefficients object (class |
ncomp |
number of components to use for creating the plot |
ny |
index of response variable to make the plot for |
type |
type of the plot |
col |
vector with two colors for the plot (one is used to show real coefficient and another one to show confidence intervals) |
show.lines |
allows to show horizontal line at c(NA, 0) |
show.ci |
logical, show or not confidence intervals if they are available |
alpha |
significance level for confidence intervals (a number between 0 and 1, e.g. for 95% alpha = 0.05) |
ylab |
label for y-axis |
... |
other arguments for plotting methods (e.g. main, xlab, etc) |
Plot method for regression results
## S3 method for class 'regres' plot(x, ...)## S3 method for class 'regres' plot(x, ...)
x |
regression results (object of class |
... |
other arguments |
This is a shortcut for plotPredictions.regres
Shows a set of plots for SIMCA model.
## S3 method for class 'simca' plot(x, comp = c(1, 2), ncomp = x$ncomp.selected, ...)## S3 method for class 'simca' plot(x, comp = c(1, 2), ncomp = x$ncomp.selected, ...)
x |
a SIMCA model (object of class |
comp |
which components to show on scores and loadings plot |
ncomp |
how many components to use for residuals plot |
... |
other arguments |
See examples in help for simcam function.
Shows a set of plots for SIMCAM model.
## S3 method for class 'simcam' plot(x, nc = c(1, 2), ...)## S3 method for class 'simcam' plot(x, nc = c(1, 2), ...)
x |
a SIMCAM model (object of class |
nc |
vector with two values - classes (SIMCA models) to show the plot for |
... |
other arguments |
See examples in help for simcam function.
Just shows a prediction plot for SIMCAM results.
## S3 method for class 'simcamres' plot(x, ...)## S3 method for class 'simcamres' plot(x, ...)
x |
SIMCAM results (object of class |
... |
other arguments |
See examples in help for simcamres function.
First row of the data matrix is taken for creating the bar series. In case of barplot color grouping is made based on columns (not rows as for all other plots).
plotBars(ps, col = ps$col, bwd = 0.8, border = NA, force.x.values = NA)plotBars(ps, col = ps$col, bwd = 0.8, border = NA, force.x.values = NA)
ps |
'plotseries' object |
col |
colors of the bars |
bwd |
width of the bars (as a ratio for max width) |
border |
color of bar edges |
force.x.values |
vector with corrected x-values for a bar plot (needed for group plots, do not change manually). |
Biplot
plotBiplot(obj, ...)plotBiplot(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for biplot
Shows a biplot for selected components.
## S3 method for class 'pca' plotBiplot( obj, comp = c(1, 2), pch = c(16, NA), col = mdaplot.getColors(2), main = "Biplot", lty = 1, lwd = 1, show.labels = FALSE, show.axes = TRUE, show.excluded = FALSE, lab.col = adjustcolor(col, alpha.f = 0.5), ... )## S3 method for class 'pca' plotBiplot( obj, comp = c(1, 2), pch = c(16, NA), col = mdaplot.getColors(2), main = "Biplot", lty = 1, lwd = 1, show.labels = FALSE, show.axes = TRUE, show.excluded = FALSE, lab.col = adjustcolor(col, alpha.f = 0.5), ... )
obj |
a PCA model (object of class |
comp |
a value or vector with several values - number of components to show the plot for |
pch |
a vector with two values - markers for scores and loadings |
col |
a vector with two colors for scores and loadings |
main |
main title for the plot |
lty |
line type for loadings |
lwd |
line width for loadings |
show.labels |
logical, show or not labels for the plot objects |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
show.excluded |
logical, show or hide rows marked as excluded (attribute 'exclrows') |
lab.col |
a vector with two colors for scores and loadings labels |
... |
other plot parameters (see |
The method shows confidence ellipse for groups of points on a scatter plot made using 'mdaplot()' function with 'cgroup' parameter. It will work only if 'cgroup' is a factor.
plotConfidenceEllipse(p, conf.level = 0.95, lwd = 1, lty = 1, opacity = 0)plotConfidenceEllipse(p, conf.level = 0.95, lwd = 1, lty = 1, opacity = 0)
p |
plot data returned by function 'mdaplot()'. |
conf.level |
confidence level to make the ellipse for (between 0 and 1). |
lwd |
thickness of line used to show the hull. |
lty |
type of line used to show the hull. |
opacity |
of opacity is 0 ellipse is transparent otherwise semi-transparent. |
# adds 90% confidence ellipse with semi-transparent area over two clusters of points library(mdatools) data(people) group <- factor(people[, "Sex"], labels = c("Male", "Female")) # first make plot and then add confidence ellipse p <- mdaplot(people, type = "p", cgroup = group) plotConfidenceEllipse(p, conf.level = 0.90, opacity = 0.2)# adds 90% confidence ellipse with semi-transparent area over two clusters of points library(mdatools) data(people) group <- factor(people[, "Sex"], labels = c("Male", "Female")) # first make plot and then add confidence ellipse p <- mdaplot(people, type = "p", cgroup = group) plotConfidenceEllipse(p, conf.level = 0.90, opacity = 0.2)
Plot resolved contributions
plotContributions(obj, ...)plotContributions(obj, ...)
obj |
object with mcr case |
... |
other parameters |
Show plot with resolved contributions
## S3 method for class 'mcr' plotContributions( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), ... )## S3 method for class 'mcr' plotContributions( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), ... )
obj |
object of clacc |
comp |
vector with number of components to make the plot for |
type |
type of the plot |
col |
vector with colors for individual components |
... |
other parameters suitable for |
The method shows convex hull for groups of points on a scatter plot made using 'mdaplot()' function with 'cgroup' parameter. It will work only if 'cgroup' is a factor.
plotConvexHull(p, lwd = 1, lty = 1, opacity = 0)plotConvexHull(p, lwd = 1, lty = 1, opacity = 0)
p |
plot data returned by function 'mdaplot()'. |
lwd |
thickness of line used to show the hull. |
lty |
type of line used to show the hull. |
opacity |
of opacity is larger than 0 a semi-transparent polygon is shown over points. |
# adds convex hull with semi-transparent area over two clusters of points library(mdatools) data(people) group <- factor(people[, "Sex"], labels = c("Male", "Female")) p <- mdaplot(people, type = "p", cgroup = group) plotConvexHull(p)# adds convex hull with semi-transparent area over two clusters of points library(mdatools) data(people) group <- factor(people[, "Sex"], labels = c("Male", "Female")) p <- mdaplot(people, type = "p", cgroup = group) plotConvexHull(p)
Cooman's plot
plotCooman(obj, ...)plotCooman(obj, ...)
obj |
classification model or result object |
... |
other arguments |
Generic function for Cooman's plot
Shows a Cooman's plot for a pair of SIMCA models
## S3 method for class 'simcam' plotCooman( obj, nc = c(1, 2), res = list(cal = obj$res[["cal"]]), groupby = res[[1]]$c.ref, main = "Cooman's plot", show.limits = TRUE, ... )## S3 method for class 'simcam' plotCooman( obj, nc = c(1, 2), res = list(cal = obj$res[["cal"]]), groupby = res[[1]]$c.ref, main = "Cooman's plot", show.limits = TRUE, ... )
obj |
a SIMCAM model (object of class |
nc |
vector with two values - classes (SIMCA models) to show the plot for |
res |
list with results to show the plot for |
groupby |
factor to use for grouping points on the plot |
main |
title of the plot |
show.limits |
logical, show or not critical limits |
... |
other plot parameters (see |
Cooman's plot shows squared orthogonal distance from data points to two selected SIMCA models as well as critical limits for the distance (optional). In case if critical limits must be shown they are computed using chi-square distribution regardless which type of limits is employed for classification.
If only one result object is provided (e.g. results for calibration set or new predictions), then the points can be color grouped using 'groupby' parameter (by default reference class values are used to make the groups). In case of multiple result objects, the points are color grouped according to the objects (e.g. calibration set and test set).
Shows a Cooman's plot for a pair of SIMCA models
## S3 method for class 'simcamres' plotCooman( obj, nc = c(1, 2), main = "Cooman's plot", cgroup = obj$c.ref, show.plot = TRUE, ... )## S3 method for class 'simcamres' plotCooman( obj, nc = c(1, 2), main = "Cooman's plot", cgroup = obj$c.ref, show.plot = TRUE, ... )
obj |
SIMCAM results (object of class |
nc |
vector with two values - classes (SIMCA models) to show the plot for |
main |
main plot title |
cgroup |
vector of values to use for color grouping of plot points |
show.plot |
logical, show plot or just return plot data |
... |
other plot parameters (see |
The plot is similar to plotCooman.simcam but shows points only for this result
object and does not show critical limits (which are part of a model).
Correlation plot
plotCorr(obj, ...)plotCorr(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for correlation plot
Makes a plot with statistic values vs. coefficient of determination between permuted and reference y-values.
## S3 method for class 'randtest' plotCorr( obj, ncomp = obj$ncomp.selected, ylim = NULL, xlab = expression(r^2), ylab = "Test statistic", ... )## S3 method for class 'randtest' plotCorr( obj, ncomp = obj$ncomp.selected, ylim = NULL, xlab = expression(r^2), ylab = "Test statistic", ... )
obj |
results of randomization test (object of class 'randtest') |
ncomp |
number of component to make the plot for |
ylim |
limits for y axis |
xlab |
label for x-axis |
ylab |
label for y-axis |
... |
other optional arguments |
See examples in help for randtest function.
Variance plot
plotCumVariance(obj, ...)plotCumVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting explained variance for data decomposition
Shows a plot with cumulative explained variance vs. number of components.
## S3 method for class 'ldecomp' plotCumVariance(obj, type = "b", labels = "values", show.plot = TRUE, ...)## S3 method for class 'ldecomp' plotCumVariance(obj, type = "b", labels = "values", show.plot = TRUE, ...)
obj |
object of |
type |
type of the plot |
labels |
what to show as labels for plot objects |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of graphical parameters from |
Show plot with cumulative explained variance
## S3 method for class 'mcr' plotCumVariance( obj, type = "b", labels = "values", main = "Cumulative variance", xticks = seq_len(obj$ncomp), ... )## S3 method for class 'mcr' plotCumVariance( obj, type = "b", labels = "values", main = "Cumulative variance", xticks = seq_len(obj$ncomp), ... )
obj |
object of clacc |
type |
type of the plot |
labels |
what to use as data labels |
main |
title of the plot |
xticks |
vector with ticks for x-axis |
... |
other parameters suitable for |
Shows a plot with cumulative explained variance for components.
## S3 method for class 'pca' plotCumVariance(obj, legend.position = "bottomright", ...)## S3 method for class 'pca' plotCumVariance(obj, legend.position = "bottomright", ...)
obj |
a PCA model (object of class |
legend.position |
position of the legend |
... |
other plot parameters (see |
See examples in help for pca function.
Show plot series as density plot (using hex binning)
plotDensity(ps, nbins = 60, colmap = ps$colmap)plotDensity(ps, nbins = 60, colmap = ps$colmap)
ps |
'plotseries' object |
nbins |
number of bins in one dimension |
colmap |
colormap name or values used to create color gradient |
Discrimination power plot
plotDiscriminationPower(obj, ...)plotDiscriminationPower(obj, ...)
obj |
a model object |
... |
other arguments |
Generic function for plotting discrimination power values for classification model
Shows a plot with discrimination power of predictors for a pair of SIMCA models
## S3 method for class 'simcam' plotDiscriminationPower( obj, nc = c(1, 2), type = "h", main = paste0("Discrimination power: ", obj$classnames[nc[1]], " vs. ", obj$classname[nc[2]]), xlab = attr(obj$dispower, "xaxis.name"), ylab = "", ... )## S3 method for class 'simcam' plotDiscriminationPower( obj, nc = c(1, 2), type = "h", main = paste0("Discrimination power: ", obj$classnames[nc[1]], " vs. ", obj$classname[nc[2]]), xlab = attr(obj$dispower, "xaxis.name"), ylab = "", ... )
obj |
a SIMCAM model (object of class |
nc |
vector with two values - classes (SIMCA models) to show the plot for |
type |
type of the plot |
main |
main plot title |
xlab |
label for x axis |
ylab |
label for y axis |
... |
other plot parameters (see |
Discrimination power shows an ability of variables to separate classes. The power is computed similar to model distance, using variance of residuals. However in this case instead of sum the variance across all variables, we take the ratio separately for individual variables.
Discrimination power equal or above 3 is considered as high.
Shows a plot with degrees of freedom computed for score and orthogonal distances at given number of components using data driven approach ("ddmoments" or "ddrobust").
plotDistDoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ... )plotDistDoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ... )
obj |
a PCA model (object of class |
type |
type of the plot ("b", "l", "h") |
labels |
what to show as data points labels |
xticks |
vector with tick values for x-axis |
... |
other plot parameters (see |
Work only if parameter lim.type equal to "ddmoments" or "ddrobust".
It is assumed that first row of dataset contains the y-coordinates of points, second rows contains size of lower error bar and third - size for upper error bar. If only two rows are provided it is assumed that error bars are symmetric.
plotErrorbars(ps, col = ps$col, pch = 16, lwd = 1, cex = 1, ...)plotErrorbars(ps, col = ps$col, pch = 16, lwd = 1, cex = 1, ...)
ps |
'plotseries' object |
col |
color for the error bars |
pch |
marker symbol for the plot |
lwd |
line width for the error bars |
cex |
scale factor for the marker |
... |
other arguments for function 'points()'. |
Generic function for creating extreme plot for SIMCA model
plotExtreme(obj, ...)plotExtreme(obj, ...)
obj |
a SIMCA model |
... |
other parameters |
Shows a plot with number of expected vs. number of observed extreme objects for different significance levels (alpha values)
## S3 method for class 'pca' plotExtreme( obj, res = obj$res[["cal"]], comp = obj$ncomp.selected, main = "Extreme plot", xlab = "Expected", ylab = "Observed", pch = rep(21, length(comp)), bg = mdaplot.getColors(length(comp)), col = rep("white", length(comp)), lwd = ifelse(pch %in% 21:25, 0.25, 1), cex = rep(1.2, length(comp)), ellipse.col = "#cceeff", legend.position = "bottomright", ... )## S3 method for class 'pca' plotExtreme( obj, res = obj$res[["cal"]], comp = obj$ncomp.selected, main = "Extreme plot", xlab = "Expected", ylab = "Observed", pch = rep(21, length(comp)), bg = mdaplot.getColors(length(comp)), col = rep("white", length(comp)), lwd = ifelse(pch %in% 21:25, 0.25, 1), cex = rep(1.2, length(comp)), ellipse.col = "#cceeff", legend.position = "bottomright", ... )
obj |
a PCA model (object of class |
res |
object with PCA results to show the plot for (e.g. calibration, test, etc) |
comp |
vector, number of components to show the plot for |
main |
plot title |
xlab |
label for x-axis |
ylab |
label for y-axis |
pch |
vector with values for |
bg |
vector with background color values for series of points (if pch=21:25) |
col |
vector with color values for series of points |
lwd |
line width for point symbols |
cex |
scale factor for data points |
ellipse.col |
color for tolerance ellipse |
legend.position |
position of the legend |
... |
other arguments |
Statistic histogram
plotHist(obj, ...)plotHist(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting statistic histogram plot
Makes a histogram for statistic values distribution for particular component, also show critical value as a vertical line.
## S3 method for class 'randtest' plotHist(obj, ncomp = obj$ncomp.selected, bwd = 0.9, ...)## S3 method for class 'randtest' plotHist(obj, ncomp = obj$ncomp.selected, bwd = 0.9, ...)
obj |
results of randomization test (object of class 'randtest') |
ncomp |
number of component to make the plot for |
bwd |
width of bars (between 0 and 1) |
... |
other optional arguments |
See examples in help for randtest function.
Add Hotelling ellipse to a scatter plot
plotHotellingEllipse(p, conf.lim = 0.95, col = "#a0a0a0", lty = 3, ...)plotHotellingEllipse(p, conf.lim = 0.95, col = "#a0a0a0", lty = 3, ...)
p |
plot series (e.g. from PCA scores plot) |
conf.lim |
confidence limit |
col |
color of the ellipse line |
lty |
line type (e.g. 1 for solid, 2 for dashed, etc.) |
... |
any argument suitable for |
The method is created to be used with PCA and PLS scores plots, so it shows the statistical
limits computed using Hotelling T^2 distribution in form of ellipse. The function works similar
to plotConvexHull and plotConfidenceEllipse but does not require
grouping of data points. Can be used together with functions plotScores.pca,
plotScores.ldecomp, plotXScores.pls,
plotXScores.plsres.
See examples for more details.
# create PCA model for People data data(people) m <- pca(people, 4, scale = TRUE) # make scores plot and show Hotelling ellipse with default settings p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p) # make scores plot and show Hotelling ellipse with manual settings p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p, conf.lim = 0.99, col = "red") # in case if you have both calibration and test set, 'plotScores()' returns # plot series data for both, so you have to subset it and take the first series # (calibration set) as shown below. ind <- seq(1, 32, by = 4) xc <- people[-ind, , drop = FALSE] xt <- people[ind, , drop = FALSE] m <- pca(xc, 4, scale = TRUE, x.test = xt) p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p[[1]])# create PCA model for People data data(people) m <- pca(people, 4, scale = TRUE) # make scores plot and show Hotelling ellipse with default settings p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p) # make scores plot and show Hotelling ellipse with manual settings p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p, conf.lim = 0.99, col = "red") # in case if you have both calibration and test set, 'plotScores()' returns # plot series data for both, so you have to subset it and take the first series # (calibration set) as shown below. ind <- seq(1, 32, by = 4) xc <- people[-ind, , drop = FALSE] xt <- people[ind, , drop = FALSE] m <- pca(xc, 4, scale = TRUE, x.test = xt) p <- plotScores(m, xlim = c(-8, 8), ylim = c(-8, 8)) plotHotellingEllipse(p[[1]])
Show plot series as set of lines
plotLines( ps, col = ps$col, lty = 1, lwd = 1, cex = 1, col.excluded = "darkgray", show.excluded = FALSE, ... )plotLines( ps, col = ps$col, lty = 1, lwd = 1, cex = 1, col.excluded = "darkgray", show.excluded = FALSE, ... )
ps |
'plotseries' object |
col |
a color for markers or lines (same as |
lty |
line type |
lwd |
line width |
cex |
scale factor for the marker |
col.excluded |
color for the excluded lines. |
show.excluded |
logical, show or not the excluded data points |
... |
other arguments for function 'lines()'. |
Loadings plot
plotLoadings(obj, ...)plotLoadings(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting loadings values for data decomposition
Shows a loadings plot for selected components.
## S3 method for class 'pca' plotLoadings( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = (if (length(comp == 2)) "p" else "l"), show.legend = TRUE, show.axes = TRUE, ... )## S3 method for class 'pca' plotLoadings( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = (if (length(comp == 2)) "p" else "l"), show.legend = TRUE, show.axes = TRUE, ... )
obj |
a PCA model (object of class |
comp |
a value or vector with several values - number of components to show the plot for |
type |
type of the plot ('b', 'l', 'h') |
show.legend |
logical, show or not a legend on the plot |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
... |
other plot parameters (see |
See examples in help for pca function.
Misclassification ratio plot
plotMisclassified(obj, ...)plotMisclassified(obj, ...)
obj |
a model or a result object |
... |
other arguments |
Generic function for plotting missclassification values for classification model or results
Makes a plot with misclassified ratio values vs. model complexity (e.g. number of components)
## S3 method for class 'classmodel' plotMisclassified(obj, ...)## S3 method for class 'classmodel' plotMisclassified(obj, ...)
obj |
classification model (object of class |
... |
parameters for |
See examples in description of plsda, simca or simcam.
Makes a plot with ms ratio values vs. model complexity (e.g. number of components) for classification results.
## S3 method for class 'classres' plotMisclassified(obj, ...)## S3 method for class 'classres' plotMisclassified(obj, ...)
obj |
classification results (object of class |
... |
other parameters for |
See examples in description of plsdares, simcamres, etc.
Model distance plot
plotModelDistance(obj, ...)plotModelDistance(obj, ...)
obj |
a model object |
... |
other arguments |
Generic function for plotting distance from object to a multivariate model
Shows a plot with distance between one SIMCA model to others.
## S3 method for class 'simcam' plotModelDistance( obj, nc = 1, type = "h", xticks = seq_len(obj$nclasses), xticklabels = obj$classnames, main = paste0("Model distance (", obj$classnames[nc], ")"), xlab = "Models", ylab = "", ... )## S3 method for class 'simcam' plotModelDistance( obj, nc = 1, type = "h", xticks = seq_len(obj$nclasses), xticklabels = obj$classnames, main = paste0("Model distance (", obj$classnames[nc], ")"), xlab = "Models", ylab = "", ... )
obj |
a SIMCAM model (object of class |
nc |
one value - number of class (SIMCA model) to show the plot for |
type |
type of the plot ("h", "l" or "b") |
xticks |
vector with tick values for x-axis |
xticklabels |
vector with tick labels for x-axis |
main |
main plot title |
xlab |
label for x axis |
ylab |
label for y axis |
... |
other plot parameters (see |
The plot shows similarity between a selected model and the others as a ratio of residual variance using the following algorithm. Let's take two SIMCA/PCA models, m1 and m2, which have optimal number of components A1 and A2. The models have been calibrated using calibration sets X1 and X2 with number of rows n1 and n2. Then we do the following:
Project X2 to model m1 and compute residuals, E12
Compute variance of the residuals as s12 = sum(E12^2) / n1
Project X1 to model m2 and compute residuals, E21
Compute variance of the residuals as s21 = sum(E21^2) / n2
Compute variance of residuals for m1 as s1 = sum(E1^2) / (n1 - A1 - 1)
Compute variance of residuals for m2 as s2 = sum(E2^2) / (n2 - A2 - 1)
The model distance then can be computed as: d = sqrt((s12 + s21) / (s1 + s2))
As one can see, if the two models and corresponding calibration sets are identical, then the distance will be sqrt((n - A - 1) / n). For example, if n = 25 and A = 2, then the distance between the model and itself is sqrt(22/25) = sqrt(0.88) = 0.938. This case is demonstrated in the example section.
In general, if distance between models is below one classes are overlapping. If it is above 3 the classes are well separated.
# create two calibration sets with n = 25 objects in each data(iris) x1 <- iris[1:25, 1:4] x2 <- iris[51:75, 1:4] # create to SIMCA models with A = 2 m1 <- simca(x1, 'setosa', ncomp = 2) m2 <- simca(x2, 'versicolor', ncomp = 2) # combine the models into SIMCAM class m <- simcam(list(m1, m2)) # show the model distance plot with distance values as labels # note, that distance between setosa and setosa is 0.938 plotModelDistance(m, show.labels = TRUE, labels = "values")# create two calibration sets with n = 25 objects in each data(iris) x1 <- iris[1:25, 1:4] x2 <- iris[51:75, 1:4] # create to SIMCA models with A = 2 m1 <- simca(x1, 'setosa', ncomp = 2) m2 <- simca(x2, 'versicolor', ncomp = 2) # combine the models into SIMCAM class m <- simcam(list(m1, m2)) # show the model distance plot with distance values as labels # note, that distance between setosa and setosa is 0.938 plotModelDistance(m, show.labels = TRUE, labels = "values")
Modelling power plot
plotModellingPower(obj, ...)plotModellingPower(obj, ...)
obj |
a model object |
... |
other arguments |
Generic function for plotting modelling power values for classification model
Classification performance plot
plotPerformance(obj, ...)plotPerformance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting classification performance for model or results
Makes a plot with sensitivity values vs. model complexity (e.g. number of components)
## S3 method for class 'classmodel' plotPerformance( obj, nc = 1, param = "misclassified", type = "b", labels = "values", ylab = "", ylim = c(0, 1.15), xticks = seq_len(dim(obj$res$cal$c.pred)[2]), res = obj$res, ... )## S3 method for class 'classmodel' plotPerformance( obj, nc = 1, param = "misclassified", type = "b", labels = "values", ylab = "", ylim = c(0, 1.15), xticks = seq_len(dim(obj$res$cal$c.pred)[2]), res = obj$res, ... )
obj |
classification model (object of class |
nc |
class number to make the plot for. |
param |
which parameter to make the plot for ( |
type |
type of the plot |
labels |
what to show as labels for plot objects. |
ylab |
label for y axis |
ylim |
vector with two values - limits for y axis |
xticks |
vector with tick values for x-axis |
res |
list with result objects to show the plot for |
... |
most of the graphical parameters from |
Makes a plot with classification performance parameters vs. model complexity (e.g. number of components) for classification results.
## S3 method for class 'classres' plotPerformance( obj, nc = 1, type = "b", param = c("sensitivity", "specificity", "misclassified"), labels = "values", ylab = "", ylim = c(0, 1.1), xticks = seq_len(obj$ncomp), show.plot = TRUE, ... )## S3 method for class 'classres' plotPerformance( obj, nc = 1, type = "b", param = c("sensitivity", "specificity", "misclassified"), labels = "values", ylab = "", ylim = c(0, 1.1), xticks = seq_len(obj$ncomp), show.plot = TRUE, ... )
obj |
classification results (object of class |
nc |
if there are several classes, which class to make the plot for. |
type |
type of the plot |
param |
which performance parameter to make the plot for (can be a vector with several values). |
labels |
what to show as labels for plot objects. |
ylab |
label for y axis |
ylim |
vector with two values - limits for y axis |
xticks |
vector with x-axis tick values |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of the graphical parameters from |
See examples in description of plsdares, simcamres, etc.
Add confidence ellipse or convex hull for group of points
plotPointsShape(p, lwd, lty, opacity, shape_function, ...)plotPointsShape(p, lwd, lty, opacity, shape_function, ...)
p |
plot data returned by function 'mdaplot()' |
lwd |
thickness of line used to show the hull |
lty |
type of line used to show the hull |
opacity |
of opacity is larger than 0 a semi-transparent polygon is shown over points |
shape_function |
function which calculates and return coordinates of the shape |
... |
extra parameters for shape_function |
Predictions plot
plotPredictions(obj, ...)plotPredictions(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting predicted values for classification or regression model or results
Makes a plot with class predictions for a classification model.
## S3 method for class 'classmodel' plotPredictions( obj, res.name = NULL, nc = seq_len(obj$nclasses), ncomp = NULL, main = NULL, ... )## S3 method for class 'classmodel' plotPredictions( obj, res.name = NULL, nc = seq_len(obj$nclasses), ncomp = NULL, main = NULL, ... )
obj |
a classification model (object of class |
res.name |
name of result object to make the plot for ("test", "cv" or "cal"). |
nc |
vector with class numbers to make the plot for. |
ncomp |
what number of components to make the plot for. |
main |
title of the plot (if NULL will be set automatically) |
... |
most of the graphical parameters from |
See examples in description of plsda, simca or simcam.
Makes a plot with predicted class values for classification results.
## S3 method for class 'classres' plotPredictions( obj, nc = seq_len(obj$nclasses), ncomp = obj$ncomp.selected, ylab = "", show.plot = TRUE, ... )## S3 method for class 'classres' plotPredictions( obj, nc = seq_len(obj$nclasses), ncomp = obj$ncomp.selected, ylab = "", show.plot = TRUE, ... )
obj |
classification results (object of class |
nc |
vector with classes to show predictions for. |
ncomp |
model complexity (number of components) to make the plot for. |
ylab |
label for y axis |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of the graphical parameters from |
See examples in description of plsdares, simcamres, etc.
Shows plot with predicted vs. reference (measured) y values for selected components.
## S3 method for class 'regmodel' plotPredictions( obj, ncomp = obj$ncomp.selected, ny = 1, legend.position = "topleft", show.line = TRUE, res = obj$res, ... )## S3 method for class 'regmodel' plotPredictions( obj, ncomp = obj$ncomp.selected, ny = 1, legend.position = "topleft", show.line = TRUE, res = obj$res, ... )
obj |
a regression model (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
ny |
number of response variable to make the plot for (if y is multivariate) |
legend.position |
position of legend on the plot (if shown) |
show.line |
logical, show or not line fit for the plot points |
res |
list with result objects |
... |
other plot parameters (see |
Shows plot with predicted y values.
## S3 method for class 'regres' plotPredictions( obj, ny = 1, ncomp = obj$ncomp.selected, show.line = TRUE, show.stat = FALSE, stat.col = "#606060", stat.cex = 0.85, xlim = NULL, ylim = NULL, axes.equal = TRUE, show.plot = TRUE, ... )## S3 method for class 'regres' plotPredictions( obj, ny = 1, ncomp = obj$ncomp.selected, show.line = TRUE, show.stat = FALSE, stat.col = "#606060", stat.cex = 0.85, xlim = NULL, ylim = NULL, axes.equal = TRUE, show.plot = TRUE, ... )
obj |
regression results (object of class |
ny |
number of predictor to show the plot for (if y is multivariate) |
ncomp |
complexity of model (e.g. number of components) to show the plot for |
show.line |
logical, show or not line fit for the plot points |
show.stat |
logical, show or not legend with statistics on the plot |
stat.col |
color of text in legend with statistics |
stat.cex |
size of text in legend with statistics |
xlim |
limits for x-axis (if NULL will be computed automatically) |
ylim |
limits for y-axis (if NULL will be computed automatically) |
axes.equal |
logical, make limits for x and y axes equal or not |
show.plot |
logical, show plot or just return plot data |
... |
other plot parameters (see |
If reference values are available, the function shows a scatter plot with predicted vs. reference values, otherwise predicted values are shown vs. object numbers.
Makes a plot with class predictions for calibration dataset.
## S3 method for class 'simcam' plotPredictions( obj, nc = seq_len(obj$nclasses), main = "SIMCAM Predictions (cal)", ... )## S3 method for class 'simcam' plotPredictions( obj, nc = seq_len(obj$nclasses), main = "SIMCAM Predictions (cal)", ... )
obj |
a SIMCAM model (object of class |
nc |
vector with class numbers to make the plot for. |
main |
plot title. |
... |
most of the graphical parameters from |
See examples in description of plsda, simca or simcam.
Makes a plot with predicted class values for classification results.
## S3 method for class 'simcamres' plotPredictions(obj, nc = seq_len(obj$nclasses), main = "Predictions", ...)## S3 method for class 'simcamres' plotPredictions(obj, nc = seq_len(obj$nclasses), main = "Predictions", ...)
obj |
classification results (object of class |
nc |
vector with classes to show predictions for. |
main |
title of the plot |
... |
most of the graphical parameters from |
See examples in description of plsdares, simcamres, etc.
Makes a plot with class belonging probabilities for each object of the classification results. Works only with classification methods, which compute this probability (e.g. SIMCA).
plotProbabilities(obj, ...)plotProbabilities(obj, ...)
obj |
an object with classification results (e.g. SIMCA) |
... |
other parameters |
Makes a plot with class belonging probabilities for each object of the classification results. Works only with classification methods, which compute this probability (e.g. SIMCA).
## S3 method for class 'classres' plotProbabilities( obj, ncomp = obj$ncomp.selected, nc = 1, type = "h", ylim = c(0, 1.1), show.lines = c(NA, 0.5), ... )## S3 method for class 'classres' plotProbabilities( obj, ncomp = obj$ncomp.selected, nc = 1, type = "h", ylim = c(0, 1.1), show.lines = c(NA, 0.5), ... )
obj |
classification results (e.g. object of class |
ncomp |
number of components to use the probabilities for. |
nc |
if there are several classes, which class to make the plot for. |
type |
type of the plot |
ylim |
vector with limits for y-axis |
show.lines |
shows a horizontal line at p = 0.5 |
... |
most of the graphical parameters from |
Plot purity values
plotPurity(obj, ...)plotPurity(obj, ...)
obj |
object with mcr pure case |
... |
other parameters |
Purity values plot
## S3 method for class 'mcrpure' plotPurity( obj, xticks = seq_len(obj$ncomp), type = "h", labels = "values", ... )## S3 method for class 'mcrpure' plotPurity( obj, xticks = seq_len(obj$ncomp), type = "h", labels = "values", ... )
obj |
|
xticks |
ticks for x axis |
type |
type of the plot |
labels |
what to use as data labels |
... |
other parameters suitable for The plot shows largest weighted purity value for each component graphically. |
Plot purity spectra
plotPuritySpectra(obj, ...)plotPuritySpectra(obj, ...)
obj |
object with mcr pure case |
... |
other parameters |
Purity spectra plot
## S3 method for class 'mcrpure' plotPuritySpectra( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), show.lines = TRUE, lines.col = adjustcolor(col, alpha.f = 0.75), lines.lty = 3, lines.lwd = 1, ... )## S3 method for class 'mcrpure' plotPuritySpectra( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), show.lines = TRUE, lines.col = adjustcolor(col, alpha.f = 0.75), lines.lty = 3, lines.lwd = 1, ... )
obj |
|
comp |
vector of components to show the purity spectra for |
type |
type of the plot |
col |
colors for the plot (should be a vector with one value for each component in |
show.lines |
if |
lines.col |
color for the selected pure variable lines (by default same as for plots but semitransparent) |
lines.lty |
line type for the purity lines |
lines.lwd |
line width for the purity lines |
... |
other parameters suitable for The plot shows weighted purity value of each variable separately for each specified component. |
Shows a plot with degrees of freedom computed for score distances at given number of components using data driven approach ("ddmoments" or "ddrobust").
plotQDoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ylab = "Nq", ... )plotQDoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ylab = "Nq", ... )
obj |
a PCA model (object of class |
type |
type of the plot ("b", "l", "h") |
labels |
what to show as data points labels |
xticks |
vector with tick values for x-axis |
ylab |
label for y-axis |
... |
other plot parameters (see |
Work only if parameter lim.type equal to "ddmoments" or "ddrobust".
Regression coefficients plot
plotRegcoeffs(obj, ...)plotRegcoeffs(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting regression coefficients values for a regression model
Shows plot with regression coefficient values. Is a proxy for link{plot.regcoeffs} method.
## S3 method for class 'regmodel' plotRegcoeffs(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'regmodel' plotRegcoeffs(obj, ncomp = obj$ncomp.selected, ...)
obj |
a regression model (object of class |
ncomp |
number of components to show the plot for |
... |
other plot parameters (see |
Shows linear fit line for data points.
plotRegressionLine(p, col = p$col, ...)plotRegressionLine(p, col = p$col, ...)
p |
plot data returned by function 'mdaplot()' |
col |
color of line |
... |
other parameters available for 'abline()' function |
Residuals plot
plotResiduals(obj, ...)plotResiduals(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting residual values for data decomposition
Shows a plot with orthogonal (Q, q) vs. score (T2, h) distances for data objects.
## S3 method for class 'ldecomp' plotResiduals( obj, ncomp = obj$ncomp.selected, norm = FALSE, log = FALSE, show.plot = TRUE, ... )## S3 method for class 'ldecomp' plotResiduals( obj, ncomp = obj$ncomp.selected, norm = FALSE, log = FALSE, show.plot = TRUE, ... )
obj |
object of |
ncomp |
number of components to show the plot for (if NULL, selected by model value will be used). |
norm |
logical, normalize distance values or not (see details) |
log |
logical, apply log tranformation to the distances or not (see details) |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of graphical parameters from |
Shows a plot with score (T2, h) vs orthogonal (Q, q) distances and corresponding critical limits for given number of components.
## S3 method for class 'pca' plotResiduals( obj, ncomp = obj$ncomp.selected, log = FALSE, norm = TRUE, cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = TRUE, lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), res = obj$res, show.legend = TRUE, ... )## S3 method for class 'pca' plotResiduals( obj, ncomp = obj$ncomp.selected, log = FALSE, norm = TRUE, cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = TRUE, lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), res = obj$res, show.legend = TRUE, ... )
obj |
a PCA model (object of class |
ncomp |
how many components to use (by default optimal value selected for the model will be used) |
log |
logical, apply log tranformation to the distances or not (see details) |
norm |
logical, normalize distance values or not (see details) |
cgroup |
color grouping of plot points (works only if one result object is available) |
xlim |
limits for x-axis |
ylim |
limits for y-axis |
show.limits |
logical, show or not lines/curves with critical limits for the distances |
lim.col |
vector with two values - line color for extreme and outlier limits |
lim.lwd |
vector with two values - line width for extreme and outlier limits |
lim.lty |
vector with two values - line type for extreme and outlier limits |
res |
list with result objects to show the plot for (by defaul, model results are used) |
show.legend |
logical, show or not a legend on the plot (needed if several result objects are available) |
... |
other plot parameters (see |
The function is a bit more advanced version of plotResiduals.ldecomp. It allows to
show distance values for several result objects (e.g. calibration and test set or calibration
and new prediction set) as well as display the correspondng critical limits in form of lines
or curves.
Depending on how many result objects your model has or how many you specified manually,
using the res parameter, the plot behaves in a bit different way.
If only one result object is provided, then it allows to colorise the points using cgroup
parameter. If you specify cgroup = "categories" then it will show points as three groups:
normal, extreme and outliers. If two or more result objects are provided, then the function show
distances in groups, and adds corresponding legend.
The function can show distance values normalised (h/h0 and q/q0) as well as with log transformation (log(1 + h/h0), log(1 + q/q0)). The latter is useful if distribution of the points is skewed and most of them are densely located around bottom left corner.
See examples in help for pca function.
Shows plot with Y residuals (difference between predicted and reference values) for selected response variable and complexity (number of components).
## S3 method for class 'regres' plotResiduals( obj, ny = 1, ncomp = obj$ncomp.selected, show.lines = c(NA, 0), show.plot = TRUE, ... )## S3 method for class 'regres' plotResiduals( obj, ny = 1, ncomp = obj$ncomp.selected, show.lines = c(NA, 0), show.plot = TRUE, ... )
obj |
regression results (object of class |
ny |
number of predictor to show the plot for (if y is multivariate) |
ncomp |
complexity of model (e.g. number of components) to show the plot for |
show.lines |
allows to show the horisontal line at y = 0 |
show.plot |
logical, show plot or just return plot data |
... |
other plot parameters (see |
RMSE plot
plotRMSE(obj, ...)plotRMSE(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting RMSE values vs. complexity of a regression model
Shows how RMSE develops for each iteration of iPLS selection algorithm.
## S3 method for class 'ipls' plotRMSE( obj, glob.ncomp = obj$gm$ncomp.selected, main = "RMSE development", xlab = "Iterations", ylab = if (is.null(obj$cv)) "RMSEP" else "RMSECV", xlim = NULL, ylim = NULL, ... )## S3 method for class 'ipls' plotRMSE( obj, glob.ncomp = obj$gm$ncomp.selected, main = "RMSE development", xlab = "Iterations", ylab = if (is.null(obj$cv)) "RMSEP" else "RMSECV", xlim = NULL, ylim = NULL, ... )
obj |
iPLS results (object of class ipls). |
glob.ncomp |
number of components for global PLS model with all intervals. |
main |
main title for the plot. |
xlab |
label for x-axis. |
ylab |
label for y-axis. |
xlim |
limits for x-axis. |
ylim |
limits for y-axis. |
... |
other arguments. |
The plot shows RMSE values obtained at each iteration of the iPLS algorithm as bars. The first bar correspond to the global model with all variables included, second - to the model obtained at the first iteration and so on. Number at the bottom of each bar corresponds to the interval included or excluded at the particular iteration.
summary.ipls, plotSelection.ipls
Shows plot with root mean squared error values vs. number of components for PLS model.
## S3 method for class 'regmodel' plotRMSE( obj, ny = 1, type = "b", labels = "values", xticks = seq_len(obj$ncomp), res = obj$res, ylab = paste0("RMSE (", obj$res$cal$respnames[ny], ")"), ... )## S3 method for class 'regmodel' plotRMSE( obj, ny = 1, type = "b", labels = "values", xticks = seq_len(obj$ncomp), res = obj$res, ylab = paste0("RMSE (", obj$res$cal$respnames[ny], ")"), ... )
obj |
a regression model (object of class |
ny |
number of response variable to make the plot for (if y is multivariate) |
type |
type of the plot("b", "l" or "h") |
labels |
what to show as labels (vector or name, e.g. "names", "values", "indices") |
xticks |
vector with ticks for x-axis values |
res |
list with result objects |
ylab |
label for y-axis |
... |
other plot parameters (see |
Shows plot with RMSE values vs. model complexity (e.g. number of components).
## S3 method for class 'regres' plotRMSE( obj, ny = 1, type = "b", xticks = seq_len(obj$ncomp), labels = "values", show.plot = TRUE, ylab = paste0("RMSE (", obj$respnames[ny], ")"), ... )## S3 method for class 'regres' plotRMSE( obj, ny = 1, type = "b", xticks = seq_len(obj$ncomp), labels = "values", show.plot = TRUE, ylab = paste0("RMSE (", obj$respnames[ny], ")"), ... )
obj |
regression results (object of class |
ny |
number of predictor to show the plot for (if y is multivariate) |
type |
type of the plot |
xticks |
vector with ticks for x-axis |
labels |
what to use as labels ("names", "values" or "indices") |
show.plot |
logical, show plot or just return plot data |
ylab |
label for y-axis |
... |
other plot parameters (see |
Plot for ratio RMSEC/RMSECV vs RMSECV
plotRMSERatio(obj, ...)plotRMSERatio(obj, ...)
obj |
object with any regression model |
... |
other parameters |
Shows plot with RMSECV/RMSEC values vs. RMSECV for each component.
## S3 method for class 'regmodel' plotRMSERatio( obj, ny = 1, type = "b", show.labels = TRUE, labels = seq_len(obj$ncomp), main = paste0("RMSECV/RMSEC ratio (", obj$res$cal$respnames[ny], ")"), ylab = "RMSECV/RMSEC ratio", xlab = "RMSECV", ... )## S3 method for class 'regmodel' plotRMSERatio( obj, ny = 1, type = "b", show.labels = TRUE, labels = seq_len(obj$ncomp), main = paste0("RMSECV/RMSEC ratio (", obj$res$cal$respnames[ny], ")"), ylab = "RMSECV/RMSEC ratio", xlab = "RMSECV", ... )
obj |
a regression model (object of class |
ny |
number of response variable to make the plot for (if y is multivariate) |
type |
type of the plot (use only "b" or "l") |
show.labels |
logical, show or not labels for plot points |
labels |
vector with point labels (by default number of components) |
main |
main plot title |
ylab |
label for y-axis |
xlab |
label for x-axis |
... |
other plot parameters (see |
Show plot series as set of points
plotScatter( ps, pch = 16, col = ps$col, bg = "white", lwd = 1, cex = 1, col.excluded = "lightgray", pch.colinv = FALSE, show.excluded = FALSE, ... )plotScatter( ps, pch = 16, col = ps$col, bg = "white", lwd = 1, cex = 1, col.excluded = "lightgray", pch.colinv = FALSE, show.excluded = FALSE, ... )
ps |
'plotseries' object |
pch |
size of point markers |
col |
color of the points |
bg |
background color of the points if 'pch=21:25' |
lwd |
line width for the error bars |
cex |
scale factor for the marker |
col.excluded |
color for excluded values (if must be shown) |
pch.colinv |
logical, should 'col' and 'bg' be switched if 'pch=21:25' and 'cgroup' is used to create colors. |
show.excluded |
logical, show or not the excluded data points |
... |
other arguments for function 'points()'. |
Scores plot
plotScores(obj, ...)plotScores(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for scores values for data decomposition
Shows a plot with scores values for data objects.
## S3 method for class 'ldecomp' plotScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.plot = TRUE, ... )## S3 method for class 'ldecomp' plotScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.plot = TRUE, ... )
obj |
object of |
comp |
which components to show the plot for (can be one value or vector with two values). |
type |
type of the plot |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of graphical parameters from |
Shows a scores plot for selected components.
## S3 method for class 'pca' plotScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.legend = TRUE, res = obj$res, ... )## S3 method for class 'pca' plotScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.legend = TRUE, res = obj$res, ... )
obj |
a PCA model (object of class |
comp |
a value or vector with several values - number of components to show the plot for |
type |
type of the plot ("p", "l", "b", "h") |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
show.legend |
logical, show or not a legend on the plot |
res |
list with result objects to show the variance for |
... |
other plot parameters (see |
If plot is created only for one result object (e.g. calibration set), then the behaviour and
all settings for the scores plot are identical to plotScores.ldecomp. In this case
you can show scores as a scatter, line or bar plot for any number of components.
Otherwise (e.g. if model contains results for calibration and test set) the plot is a group
plot created using mdaplotg method and only scatter plot can be used.
See examples in help for pca function.
Selected intervals plot
plotSelection(obj, ...)plotSelection(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting selected intervals or variables
Shows PLS performance for each selected or excluded intervals at the first iteration.
## S3 method for class 'ipls' plotSelection( obj, glob.ncomp = obj$gm$ncomp.selected, main = "iPLS results", xlab = obj$xaxis.name, ylab = if (is.null(obj$cv)) "RMSEP" else "RMSECV", xlim = NULL, ylim = NULL, ... )## S3 method for class 'ipls' plotSelection( obj, glob.ncomp = obj$gm$ncomp.selected, main = "iPLS results", xlab = obj$xaxis.name, ylab = if (is.null(obj$cv)) "RMSEP" else "RMSECV", xlim = NULL, ylim = NULL, ... )
obj |
iPLS results (object of class ipls). |
glob.ncomp |
number of components for global PLS model with all intervals. |
main |
main title for the plot. |
xlab |
label for x-axis. |
ylab |
label for y-axis. |
xlim |
limits for x-axis. |
ylim |
limits for y-axis. |
... |
other arguments. |
The plot shows intervals as bars, which height corresponds to RMSECV obtained when particular interval was selected (forward) or excluded (backward) from a model at the first iteration. The intervals found optimal after backward/forward iPLS selection are shown with green color while the other intervals are gray.
See examples in help for ipls function.
@seealso
summary.ipls, plotRMSE.ipls
Generic function for plotting selectivity ratio values for regression model (PCR, PLS, etc)
plotSelectivityRatio(obj, ...)plotSelectivityRatio(obj, ...)
obj |
a regression model |
... |
other parameters |
Computes and shows a plot for Selectivity ratio values for given number of components and response variable
## S3 method for class 'pls' plotSelectivityRatio(obj, ny = 1, ncomp = obj$ncomp.selected, type = "l", ...)## S3 method for class 'pls' plotSelectivityRatio(obj, ny = 1, ncomp = obj$ncomp.selected, type = "l", ...)
obj |
a PLS model (object of class |
ny |
which response to plot the values for (if y is multivariate), can be a vector. |
ncomp |
number of components to count |
type |
type of the plot |
... |
other plot parameters (see |
See vipscores for more details.
Sensitivity plot
plotSensitivity(obj, ...)plotSensitivity(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting sensitivity values for classification model or results
Makes a plot with sensitivity values vs. model complexity (e.g. number of components)
## S3 method for class 'classmodel' plotSensitivity(obj, legend.position = "bottomright", ...)## S3 method for class 'classmodel' plotSensitivity(obj, legend.position = "bottomright", ...)
obj |
classification model (object of class |
legend.position |
position of the legend (as in |
... |
parameters for |
See examples in description of plsda, simca or simcam.
Makes a plot with sn values vs. model complexity (e.g. number of components) for classification results.
## S3 method for class 'classres' plotSensitivity(obj, legend.position = "bottomright", ...)## S3 method for class 'classres' plotSensitivity(obj, legend.position = "bottomright", ...)
obj |
classification results (object of class |
legend.position |
position of the legend (as in |
... |
other parameters for |
See examples in description of plsdares, simcamres, etc.
The 'plotseries' object contains all necessary paremeters to create main plots from data values, including values for x and y, correct handling of excluded rows and columns, color grouping (if any), limits and labels.
If both 'col' and 'cgroup' are specified, 'cgroup' will be ignored.
Labels can be either provided by user or generated automatically based on values, names or indices of data rows and columns. If series is made for scatter plot 'type="p"' then labels are required for each row of the original dataset. Otherwise (for line, bar and errobar plot) labels correspond to data columns (variables).
The object has the following plotting methods once created:
plotScatter
plotLines
plotBars
plotDensity
plotErrorbars
plotseries( data, type, cgroup = NULL, col = NULL, opacity = 1, colmap = "default", labels = NULL )plotseries( data, type, cgroup = NULL, col = NULL, opacity = 1, colmap = "default", labels = NULL )
data |
data to make the plot for (vector, matrix or data frame). |
type |
type of the plot. |
cgroup |
vector with values used to create a color grouping of the series instances. |
col |
color to show the series on plot with (user defined). |
opacity |
opacity of the colors (between 0 and 1). |
colmap |
colormap name to generate color/colors if they are not specified by user. See
|
labels |
either vector with labels for the series instances or string ("names", "values", or "indices") if labels should be generated automatically. |
Specificity plot
plotSpecificity(obj, ...)plotSpecificity(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting specificity values for classification model or results
Makes a plot with specificity values vs. model complexity (e.g. number of components)
## S3 method for class 'classmodel' plotSpecificity(obj, legend.position = "bottomright", ...)## S3 method for class 'classmodel' plotSpecificity(obj, legend.position = "bottomright", ...)
obj |
classification model (object of class |
legend.position |
position of the legend (as in |
... |
parameters for |
See examples in description of plsda, simca or simcam.
Makes a plot with specificity values vs. model complexity (e.g. number of components) for classification results.
## S3 method for class 'classres' plotSpecificity(obj, legend.position = "bottomright", ...)## S3 method for class 'classres' plotSpecificity(obj, legend.position = "bottomright", ...)
obj |
classification results (object of class |
legend.position |
position of the legend (as in |
... |
other parameters for |
See examples in description of plsdares, simcamres, etc.
Plot resolved spectra
plotSpectra(obj, ...)plotSpectra(obj, ...)
obj |
object with mcr case |
... |
other parameters |
Show plot with resolved spectra
## S3 method for class 'mcr' plotSpectra( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), ... )## S3 method for class 'mcr' plotSpectra( obj, comp = seq_len(obj$ncomp), type = "l", col = mdaplot.getColors(obj$ncomp), ... )
obj |
object of clacc |
comp |
vector with number of components to make the plot for |
type |
type of the plot |
col |
vector with colors for individual components |
... |
other parameters suitable for |
Shows a plot with degrees of freedom computed for score distances at given number of components using data driven approach ("ddmoments" or "ddrobust").
plotT2DoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ylab = "Nh", ... )plotT2DoF( obj, type = "b", labels = "values", xticks = seq_len(obj$ncomp), ylab = "Nh", ... )
obj |
a PCA model (object of class |
type |
type of the plot ("b", "l", "h") |
labels |
what to show as data points labels |
xticks |
vector with tick values for x-axis |
ylab |
label for y-axis |
... |
other plot parameters (see |
Work only if parameter lim.type equal to "ddmoments" or "ddrobust".
Variance plot
plotVariance(obj, ...)plotVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting explained variance for data decomposition
Shows a plot with explained variance vs. number of components.
## S3 method for class 'ldecomp' plotVariance( obj, type = "b", variance = "expvar", labels = "values", xticks = seq_len(obj$ncomp), show.plot = TRUE, ylab = "Explained variance, %", ... )## S3 method for class 'ldecomp' plotVariance( obj, type = "b", variance = "expvar", labels = "values", xticks = seq_len(obj$ncomp), show.plot = TRUE, ylab = "Explained variance, %", ... )
obj |
object of |
type |
type of the plot |
variance |
string, which variance to make the plot for ("expvar", "cumexpvar") |
labels |
what to show as labels for plot objects. |
xticks |
vector with ticks for x-axis |
show.plot |
logical, shall plot be created or just plot series object is needed |
ylab |
label for y-axis |
... |
most of graphical parameters from |
Show plot with explained variance
## S3 method for class 'mcr' plotVariance( obj, type = "h", labels = "values", main = "Variance", xticks = seq_len(obj$ncomp), ... )## S3 method for class 'mcr' plotVariance( obj, type = "h", labels = "values", main = "Variance", xticks = seq_len(obj$ncomp), ... )
obj |
object of clacc |
type |
type of the plot |
labels |
what to use as data labels |
main |
title of the plot |
xticks |
vector with ticks for x-axis |
... |
other parameters suitable for |
Shows a plot with explained variance or cumulative explained variance for components.
## S3 method for class 'pca' plotVariance( obj, type = "b", labels = "values", variance = "expvar", xticks = seq_len(obj$ncomp), res = obj$res, ylab = "Explained variance, %", ... )## S3 method for class 'pca' plotVariance( obj, type = "b", labels = "values", variance = "expvar", xticks = seq_len(obj$ncomp), res = obj$res, ylab = "Explained variance, %", ... )
obj |
a PCA model (object of class |
type |
type of the plot ("b", "l", "h") |
labels |
what to use as labels (if |
variance |
which variance to show |
xticks |
vector with ticks for x-axis |
res |
list with result objects to show the variance for |
ylab |
label for y-axis |
... |
other plot parameters (see |
See examples in help for pca function.
Shows plot with variance values vs. number of components.
## S3 method for class 'pls' plotVariance( obj, decomp = "xdecomp", variance = "expvar", type = "b", labels = "values", res = obj$res, ylab = "Explained variance, %", ... )## S3 method for class 'pls' plotVariance( obj, decomp = "xdecomp", variance = "expvar", type = "b", labels = "values", res = obj$res, ylab = "Explained variance, %", ... )
obj |
a PLS model (object of class |
decomp |
which decomposition to use ("xdecomp" for x or "ydecomp" for y) |
variance |
which variance to use ("expvar", "cumexpvar") |
type |
type of the plot("b", "l" or "h") |
labels |
what to show as labels for plot objects. |
res |
list with result objects to show the plot for (by defaul, model results are used) |
ylab |
label for y-axis |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with explained X variance vs. number of components.
## S3 method for class 'plsres' plotVariance(obj, decomp = "xdecomp", variance = "expvar", ...)## S3 method for class 'plsres' plotVariance(obj, decomp = "xdecomp", variance = "expvar", ...)
obj |
PLS results (object of class |
decomp |
which dcomposition to use ("xdecomp" or "ydecomp") |
variance |
which variance to use ("expvar", "cumexpvar") |
... |
other plot parameters (see |
See examples in help for plsres function.
Generic function for plotting VIP scores values for regression model (PCR, PLS, etc)
plotVIPScores(obj, ...)plotVIPScores(obj, ...)
obj |
a regression model |
... |
other parameters |
Shows a plot with VIP scores values for given number of components and response variable
## S3 method for class 'pls' plotVIPScores(obj, ny = 1, ncomp = obj$ncomp.selected, type = "l", ...)## S3 method for class 'pls' plotVIPScores(obj, ny = 1, ncomp = obj$ncomp.selected, type = "l", ...)
obj |
a PLS model (object of class |
ny |
which response to plot the values for (if y is multivariate), can be a vector. |
ncomp |
number of components to count |
type |
type of the plot |
... |
other plot parameters (see |
See vipscores for more details.
Plot for PLS weights
plotWeights(obj, ...)plotWeights(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for weight plot
Shows plot with X loading values for selected components.
## S3 method for class 'pls' plotWeights( obj, comp = 1, type = (if (nrow(obj$weights) < 20) "h" else "l"), show.axes = TRUE, show.legend = TRUE, ... )## S3 method for class 'pls' plotWeights( obj, comp = 1, type = (if (nrow(obj$weights) < 20) "h" else "l"), show.axes = TRUE, show.legend = TRUE, ... )
obj |
a PLS model (object of class |
comp |
which components to show the plot for (one or vector with several values) |
type |
type of the plot |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
show.legend |
logical, show or not a legend |
... |
other plot parameters (see |
See examples in help for pls function.
X cumulative variance plot
plotXCumVariance(obj, ...)plotXCumVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting cumulative explained variance for decomposition of x data
Shows plot with cumulative explained X variance vs. number of components.
## S3 method for class 'pls' plotXCumVariance(obj, type = "b", main = "Cumulative variance (X)", ...)## S3 method for class 'pls' plotXCumVariance(obj, type = "b", main = "Cumulative variance (X)", ...)
obj |
a PLS model (object of class |
type |
type of the plot("b", "l" or "h") |
main |
title for the plot |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with cumulative explained X variance vs. number of components.
## S3 method for class 'plsres' plotXCumVariance(obj, main = "Cumulative variance (X)", ...)## S3 method for class 'plsres' plotXCumVariance(obj, main = "Cumulative variance (X)", ...)
obj |
PLS results (object of class |
main |
main plot title |
... |
other plot parameters (see |
See examples in help for plsres function.
X loadings plot
plotXLoadings(obj, ...)plotXLoadings(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting loadings values for decomposition of x data
Shows plot with X loading values for selected components.
## S3 method for class 'pls' plotXLoadings( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.legend = TRUE, ... )## S3 method for class 'pls' plotXLoadings( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, type = "p", show.axes = TRUE, show.legend = TRUE, ... )
obj |
a PLS model (object of class |
comp |
which components to show the plot for (one or vector with several values) |
type |
type of the plot |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
show.legend |
logical, show or not legend on the plot (when it is available) |
... |
other plot parameters (see |
See examples in help for pls function.
X residuals plot
plotXResiduals(obj, ...)plotXResiduals(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting x residuals for classification or regression model or results
Shows a plot with orthogonal distance vs score distance for PLS decomposition of X data.
## S3 method for class 'pls' plotXResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("X-distances (ncomp = %d)", ncomp), cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", res = obj$res, ... )## S3 method for class 'pls' plotXResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("X-distances (ncomp = %d)", ncomp), cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", res = obj$res, ... )
obj |
a PLS model (object of class |
ncomp |
how many components to use (by default optimal value selected for the model will be used) |
norm |
logical, normalize distance values or not (see details) |
log |
logical, apply log tranformation to the distances or not (see details) |
main |
title for the plot |
cgroup |
color grouping of plot points (works only if one result object is available) |
xlim |
limits for x-axis |
ylim |
limits for y-axis |
show.limits |
vector with two logical values defining if limits for extreme and/or outliers must be shown |
lim.col |
vector with two values - line color for extreme and outlier limits |
lim.lwd |
vector with two values - line width for extreme and outlier limits |
lim.lty |
vector with two values - line type for extreme and outlier limits |
show.legend |
logical, show or not a legend on the plot (needed if several result objects are available) |
legend.position |
position of legend (if shown) |
res |
list with result objects to show the plot for (by defaul, model results are used) |
... |
other plot parameters (see |
The function is almost identical to plotResiduals.pca.
Shows a plot with Q residuals vs. Hotelling T2 values for PLS decomposition of x data.
## S3 method for class 'plsres' plotXResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("X-distances (ncomp = %d)", ncomp), ... )## S3 method for class 'plsres' plotXResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("X-distances (ncomp = %d)", ncomp), ... )
obj |
PLS results (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
norm |
logical, normalize distance values or not (see details) |
log |
logical, apply log tranformation to the distances or not (see details) |
main |
main title for the plot |
... |
other plot parameters (see |
See examples in help for plsres function.
X scores plot
plotXScores(obj, ...)plotXScores(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting scores values for decomposition of x data
Shows plot with X scores values for selected components.
## S3 method for class 'pls' plotXScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, show.axes = TRUE, main = "Scores (X)", res = obj$res, ... )## S3 method for class 'pls' plotXScores( obj, comp = if (obj$ncomp > 1) c(1, 2) else 1, show.axes = TRUE, main = "Scores (X)", res = obj$res, ... )
obj |
a PLS model (object of class |
comp |
which components to show the plot for (one or vector with several values) |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
main |
main plot title |
res |
list with result objects to show the plot for (by defaul, model results are used) |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with scores values for PLS decomposition of x data.
## S3 method for class 'plsres' plotXScores(obj, comp = c(1, 2), main = "Scores (X)", ...)## S3 method for class 'plsres' plotXScores(obj, comp = c(1, 2), main = "Scores (X)", ...)
obj |
PLS results (object of class |
comp |
which components to show the plot for (one or vector with several values) |
main |
main plot title |
... |
other plot parameters (see |
See examples in help for plsres function.
X variance plot
plotXVariance(obj, ...)plotXVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting explained variance for decomposition of x data
Shows plot with explained X variance vs. number of components.
## S3 method for class 'pls' plotXVariance(obj, type = "b", main = "Variance (X)", ...)## S3 method for class 'pls' plotXVariance(obj, type = "b", main = "Variance (X)", ...)
obj |
a PLS model (object of class |
type |
type of the plot("b", "l" or "h") |
main |
title for the plot |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with explained X variance vs. number of components.
## S3 method for class 'plsres' plotXVariance(obj, main = "Variance (X)", ...)## S3 method for class 'plsres' plotXVariance(obj, main = "Variance (X)", ...)
obj |
PLS results (object of class |
main |
main plot title |
... |
other plot parameters (see |
See examples in help for plsres function.
X loadings plot
plotXYLoadings(obj, ...)plotXYLoadings(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting loadings values for decomposition of x and y data
Shows plot with X and Y loading values for selected components.
## S3 method for class 'pls' plotXYLoadings(obj, comp = c(1, 2), show.axes = TRUE, ...)## S3 method for class 'pls' plotXYLoadings(obj, comp = c(1, 2), show.axes = TRUE, ...)
obj |
a PLS model (object of class |
comp |
which components to show the plot for (one or vector with several values) |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
... |
other plot parameters (see |
See examples in help for pls function.
Plot for XY-residuals
plotXYResiduals(obj, ...)plotXYResiduals(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for XY-residuals plot
Shows a plot with full X-distance (f) vs. orthogonal Y-distance (z) for PLS model results.
## S3 method for class 'pls' plotXYResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("XY-distances (ncomp = %d)", ncomp), cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", res = obj$res, ... )## S3 method for class 'pls' plotXYResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, main = sprintf("XY-distances (ncomp = %d)", ncomp), cgroup = NULL, xlim = NULL, ylim = NULL, show.limits = c(TRUE, TRUE), lim.col = c("darkgray", "darkgray"), lim.lwd = c(1, 1), lim.lty = c(2, 3), show.legend = TRUE, legend.position = "topright", res = obj$res, ... )
obj |
a PLS model (object of class |
ncomp |
how many components to use (by default optimal value selected for the model will be used) |
norm |
logical, normalize distance values or not (see details) |
log |
logical, apply log tranformation to the distances or not (see details) |
main |
title for the plot |
cgroup |
color grouping of plot points (works only if one result object is available) |
xlim |
limits for x-axis |
ylim |
limits for y-axis |
show.limits |
vector with two logical values defining if limits for extreme and/or outliers must be shown |
lim.col |
vector with two values - line color for extreme and outlier limits |
lim.lwd |
vector with two values - line width for extreme and outlier limits |
lim.lty |
vector with two values - line type for extreme and outlier limits |
show.legend |
logical, show or not a legend on the plot (needed if several result objects are available) |
legend.position |
position of legend (if shown) |
res |
list with result objects to show the plot for (by defaul, model results are used) |
... |
other plot parameters (see |
The function presents a way to identify extreme objects and outliers based on both full distance for X-decomposition (known as f) and squared residual distance for Y-decomposition (z). The approach has been proposed in [1].
The plot is available only if data driven methods (classic or robust) have been used for computing of critical limits.
1. Rodionova O. Ye., Pomerantsev A. L. Detection of Outliers in Projection-Based Modeling. Analytical Chemistry (2020, in publish). doi: 10.1021/acs.analchem.9b04611
Shows a plot with orthogonal (Q, q) vs. score (T2, h) distances for data objects.
## S3 method for class 'plsres' plotXYResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, show.labels = FALSE, labels = "names", show.plot = TRUE, ... )## S3 method for class 'plsres' plotXYResiduals( obj, ncomp = obj$ncomp.selected, norm = TRUE, log = FALSE, show.labels = FALSE, labels = "names", show.plot = TRUE, ... )
obj |
object of |
ncomp |
number of components to show the plot for (if NULL, selected by model value will be used). |
norm |
logical, normalize distance values or not (see details) |
log |
logical, apply log tranformation to the distances or not (see details) |
show.labels |
logical, show or not labels for the plot objects |
labels |
what to show as labels if necessary |
show.plot |
logical, shall plot be created or just plot series object is needed |
... |
most of graphical parameters from |
XY scores plot
plotXYScores(obj, ...)plotXYScores(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting scores values for decomposition of x and y data
Shows plot with X vs. Y scores values for selected component.
## S3 method for class 'pls' plotXYScores(obj, ncomp = 1, show.axes = TRUE, res = obj$res, ...)## S3 method for class 'pls' plotXYScores(obj, ncomp = 1, show.axes = TRUE, res = obj$res, ...)
obj |
a PLS model (object of class |
ncomp |
which component to show the plot for |
show.axes |
logical, show or not a axes lines crossing origin (0,0) |
res |
list with result objects to show the plot for (by defaul, model results are used) |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with X vs. Y scores values for PLS results.
## S3 method for class 'plsres' plotXYScores(obj, ncomp = 1, show.plot = TRUE, ...)## S3 method for class 'plsres' plotXYScores(obj, ncomp = 1, show.plot = TRUE, ...)
obj |
PLS results (object of class |
ncomp |
which component to show the plot for |
show.plot |
logical, show plot or just return plot data |
... |
other plot parameters (see |
See examples in help for plsres function.
Y cumulative variance plot
plotYCumVariance(obj, ...)plotYCumVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting cumulative explained variance for decomposition of y data
Shows plot with cumulative explained Y variance vs. number of components.
## S3 method for class 'pls' plotYCumVariance(obj, type = "b", main = "Cumulative variance (Y)", ...)## S3 method for class 'pls' plotYCumVariance(obj, type = "b", main = "Cumulative variance (Y)", ...)
obj |
a PLS model (object of class |
type |
type of the plot("b", "l" or "h") |
main |
title for the plot |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with cumulative explained Y variance vs. number of components.
## S3 method for class 'plsres' plotYCumVariance(obj, main = "Cumulative variance (Y)", ...)## S3 method for class 'plsres' plotYCumVariance(obj, main = "Cumulative variance (Y)", ...)
obj |
PLS results (object of class |
main |
main plot title |
... |
other plot parameters (see |
See examples in help for plsres function.
Y residuals plot
plotYResiduals(obj, ...)plotYResiduals(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting y residuals for classification or regression model or results
Shows a plot with Y residuals vs reference Y values for selected component.
## S3 method for class 'plsres' plotYResiduals(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'plsres' plotYResiduals(obj, ncomp = obj$ncomp.selected, ...)
obj |
PLS results (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
... |
other plot parameters (see |
Proxy for plotResiduals.regres function.
Shows plot with y residuals (predicted vs. reference values) for selected components.
## S3 method for class 'regmodel' plotYResiduals( obj, ncomp = obj$ncomp.selected, ny = 1, show.lines = c(NA, 0), res = obj$res, ... )## S3 method for class 'regmodel' plotYResiduals( obj, ncomp = obj$ncomp.selected, ny = 1, show.lines = c(NA, 0), res = obj$res, ... )
obj |
a regression model (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
ny |
number of response variable to make the plot for (if y is multivariate) |
show.lines |
allows to show the horizonta line at 0 level |
res |
list with result objects |
... |
other plot parameters (see |
Y variance plot
plotYVariance(obj, ...)plotYVariance(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for plotting explained variance for decomposition of y data
Shows plot with explained Y variance vs. number of components.
## S3 method for class 'pls' plotYVariance(obj, type = "b", main = "Variance (Y)", ...)## S3 method for class 'pls' plotYVariance(obj, type = "b", main = "Variance (Y)", ...)
obj |
a PLS model (object of class |
type |
type of the plot("b", "l" or "h") |
main |
title for the plot |
... |
other plot parameters (see |
See examples in help for pls function.
Shows plot with explained Y variance vs. number of components.
## S3 method for class 'plsres' plotYVariance(obj, main = "Variance (Y)", ...)## S3 method for class 'plsres' plotYVariance(obj, main = "Variance (Y)", ...)
obj |
PLS results (object of class |
main |
main plot title |
... |
other plot parameters (see |
See examples in help for plsres function.
pls is used to calibrate, validate and use of partial least squares (PLS)
regression model.
pls( x, y, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, cv = NULL, exclcols = NULL, exclrows = NULL, x.test = NULL, y.test = NULL, method = "simpls", info = "", ncomp.selcrit = "min", lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, cv.scope = "local" )pls( x, y, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, cv = NULL, exclcols = NULL, exclrows = NULL, x.test = NULL, y.test = NULL, method = "simpls", info = "", ncomp.selcrit = "min", lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, cv.scope = "local" )
x |
matrix with predictors. |
y |
matrix with responses. |
ncomp |
maximum number of components to calculate. |
center |
logical, center or not predictors and response values. |
scale |
logical, scale (standardize) or not predictors and response values. |
cv |
cross-validation settings (see details). |
exclcols |
columns of x to be excluded from calculations (numbers, names or vector with logical values) |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
x.test |
matrix with predictors for test set. |
y.test |
matrix with responses for test set. |
method |
algorithm for computing PLS model (only 'simpls' is supported so far) |
info |
short text with information about the model. |
ncomp.selcrit |
criterion for selecting optimal number of components ( |
lim.type |
which method to use for calculation of critical limits for residual distances (see details) |
alpha |
significance level for extreme limits for T2 and Q disances. |
gamma |
significance level for outlier limits for T2 and Q distances. |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
So far only SIMPLS method [1] is available. Implementation works both with one and multiple response variables.
Like in pca, pls uses number of components (ncomp) as a minimum of
number of objects - 1, number of x variables and the default or provided value. Regression
coefficients, predictions and other results are calculated for each set of components from 1
to ncomp: 1, 1:2, 1:3, etc. The optimal number of components, (ncomp.selected),
is found using first local minumum, but can be also forced to user defined value using function
(selectCompNum.pls). The selected optimal number of components is used for all
default operations - predictions, plots, etc.
Cross-validation settings, cv, can be a number or a list. If cv is a number, it
will be used as a number of segments for random cross-validation (if cv = 1, full
cross-validation will be preformed). If it is a list, the following syntax can be used:
cv = list("rand", nseg, nrep) for random repeated cross-validation with nseg
segments and nrep repetitions or cv = list("ven", nseg) for systematic splits
to nseg segments ('venetian blinds').
Calculation of confidence intervals and p-values for regression coefficients can by done
based on Jack-Knifing resampling. This is done automatically if cross-validation is used.
However it is recommended to use at least 10 segments for stable JK result. See help for
regcoeffs objects for more details.
Returns an object of pls class with following fields:
ncomp |
number of components included to the model. |
ncomp.selected |
selected (optimal) number of components. |
xcenter |
vector with values used to center the predictors (x). |
ycenter |
vector with values used to center the responses (y). |
xscale |
vector with values used to scale the predictors (x). |
yscale |
vector with values used to scale the responses (y). |
xloadings |
matrix with loading values for x decomposition. |
yloadings |
matrix with loading values for y decomposition. |
xeigenvals |
vector with eigenvalues of components (variance of x-scores). |
yeigenvals |
vector with eigenvalues of components (variance of y-scores). |
weights |
matrix with PLS weights. |
coeffs |
object of class |
info |
information about the model, provided by user when build the model. |
cv |
information cross-validation method used (if any). |
res |
a list with result objects (e.g. calibration, cv, etc.) |
Sergey Kucheryavskiy ([email protected])
1. S. de Jong, Chemometrics and Intelligent Laboratory Systems 18 (1993) 251-263. 2. Tarja Rajalahti et al. Chemometrics and Laboratory Systems, 95 (2009), 35-48. 3. Il-Gyo Chong, Chi-Hyuck Jun. Chemometrics and Laboratory Systems, 78 (2005), 103-112.
Main methods for pls objects:
print |
prints information about a pls object. |
summary.pls |
shows performance statistics for the model. |
plot.pls |
shows plot overview of the model. |
pls.simpls |
implementation of SIMPLS algorithm. |
predict.pls |
applies PLS model to a new data. |
selectCompNum.pls |
set number of optimal components in the model. |
setDistanceLimits.pls |
allows to change parameters for critical limits. |
categorize.pls |
categorize data rows similar to
categorize.pca. |
selratio |
computes matrix with selectivity ratio values. |
vipscores |
computes matrix with VIP scores values. |
Plotting methods for pls objects:
plotXScores.pls |
shows scores plot for x decomposition. |
plotXYScores.pls |
shows scores plot for x and y decomposition. |
plotXLoadings.pls |
shows loadings plot for x decomposition. |
plotXYLoadings.pls |
shows loadings plot for x and y decomposition. |
plotXVariance.pls |
shows explained variance plot for x decomposition. |
plotYVariance.pls |
shows explained variance plot for y decomposition. |
plotXCumVariance.pls |
shows cumulative explained variance plot for y decomposition. |
plotYCumVariance.pls |
shows cumulative explained variance plot for y decomposition. |
plotXResiduals.pls |
shows distance/residuals plot for x decomposition. |
plotXYResiduals.pls |
shows joint distance plot for x and y decomposition. |
plotWeights.pls |
shows plot with weights. |
plotSelectivityRatio.pls |
shows plot with selectivity ratio values. |
plotVIPScores.pls |
shows plot with VIP scores values. |
Methods inherited from regmodel object (parent class for pls):
plotPredictions.regmodel |
shows predicted vs. measured plot. |
plotRMSE.regmodel |
shows RMSE plot. |
plotRMSERatio.regmodel |
shows plot for ratio RMSECV/RMSEC values. |
plotYResiduals.regmodel |
shows residuals plot for y values. |
getRegcoeffs.regmodel |
returns matrix with regression coefficients. |
Most of the methods for plotting data (except loadings and regression coefficients) are also
available for PLS results (plsres) objects. There is also a randomization test
for PLS-regression (randtest) and implementation of interval PLS algorithm
for variable selection (ipls)
### Examples of using PLS model class library(mdatools) ## 1. Make a PLS model for concentration of first component ## using full-cross validation and automatic detection of ## optimal number of components and show an overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 8, cv = 1) summary(model) plot(model) ## 2. Make a PLS model for concentration of first component ## using test set and 10 segment cross-validation and show overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] x.t = simdata$spectra.t y.t = simdata$conc.t[, 1] model = pls(x, y, ncomp = 8, cv = 10, x.test = x.t, y.test = y.t) model = selectCompNum(model, 2) summary(model) plot(model) ## 3. Make a PLS model for concentration of first component ## using only test set validation and show overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] x.t = simdata$spectra.t y.t = simdata$conc.t[, 1] model = pls(x, y, ncomp = 6, x.test = x.t, y.test = y.t) model = selectCompNum(model, 2) summary(model) plot(model) ## 4. Show variance and error plots for a PLS model par(mfrow = c(2, 2)) plotXCumVariance(model, type = 'h') plotYCumVariance(model, type = 'b', show.labels = TRUE, legend.position = 'bottomright') plotRMSE(model) plotRMSE(model, type = 'h', show.labels = TRUE) par(mfrow = c(1, 1)) ## 5. Show scores plots for a PLS model par(mfrow = c(2, 2)) plotXScores(model) plotXScores(model, comp = c(1, 3), show.labels = TRUE) plotXYScores(model) plotXYScores(model, comp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) ## 6. Show loadings and coefficients plots for a PLS model par(mfrow = c(2, 2)) plotXLoadings(model) plotXLoadings(model, comp = c(1, 2), type = 'l') plotXYLoadings(model, comp = c(1, 2), legend.position = 'topleft') plotRegcoeffs(model) par(mfrow = c(1, 1)) ## 7. Show predictions and residuals plots for a PLS model par(mfrow = c(2, 2)) plotXResiduals(model, show.label = TRUE) plotYResiduals(model, show.label = TRUE) plotPredictions(model) plotPredictions(model, ncomp = 4, xlab = 'C, reference', ylab = 'C, predictions') par(mfrow = c(1, 1)) ## 8. Selectivity ratio and VIP scores plots par(mfrow = c(2, 2)) plotSelectivityRatio(model) plotSelectivityRatio(model, ncomp = 1) par(mfrow = c(1, 1)) ## 9. Variable selection with selectivity ratio selratio = getSelectivityRatio(model) selvar = !(selratio < 8) xsel = x[, selvar] modelsel = pls(xsel, y, ncomp = 6, cv = 1) modelsel = selectCompNum(modelsel, 3) summary(model) summary(modelsel) ## 10. Calculate average spectrum and show the selected variables i = 1:ncol(x) ms = apply(x, 2, mean) par(mfrow = c(2, 2)) plot(i, ms, type = 'p', pch = 16, col = 'red', main = 'Original variables') plotPredictions(model) plot(i, ms, type = 'p', pch = 16, col = 'lightgray', main = 'Selected variables') points(i[selvar], ms[selvar], col = 'red', pch = 16) plotPredictions(modelsel) par(mfrow = c(1, 1))### Examples of using PLS model class library(mdatools) ## 1. Make a PLS model for concentration of first component ## using full-cross validation and automatic detection of ## optimal number of components and show an overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 8, cv = 1) summary(model) plot(model) ## 2. Make a PLS model for concentration of first component ## using test set and 10 segment cross-validation and show overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] x.t = simdata$spectra.t y.t = simdata$conc.t[, 1] model = pls(x, y, ncomp = 8, cv = 10, x.test = x.t, y.test = y.t) model = selectCompNum(model, 2) summary(model) plot(model) ## 3. Make a PLS model for concentration of first component ## using only test set validation and show overview data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] x.t = simdata$spectra.t y.t = simdata$conc.t[, 1] model = pls(x, y, ncomp = 6, x.test = x.t, y.test = y.t) model = selectCompNum(model, 2) summary(model) plot(model) ## 4. Show variance and error plots for a PLS model par(mfrow = c(2, 2)) plotXCumVariance(model, type = 'h') plotYCumVariance(model, type = 'b', show.labels = TRUE, legend.position = 'bottomright') plotRMSE(model) plotRMSE(model, type = 'h', show.labels = TRUE) par(mfrow = c(1, 1)) ## 5. Show scores plots for a PLS model par(mfrow = c(2, 2)) plotXScores(model) plotXScores(model, comp = c(1, 3), show.labels = TRUE) plotXYScores(model) plotXYScores(model, comp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) ## 6. Show loadings and coefficients plots for a PLS model par(mfrow = c(2, 2)) plotXLoadings(model) plotXLoadings(model, comp = c(1, 2), type = 'l') plotXYLoadings(model, comp = c(1, 2), legend.position = 'topleft') plotRegcoeffs(model) par(mfrow = c(1, 1)) ## 7. Show predictions and residuals plots for a PLS model par(mfrow = c(2, 2)) plotXResiduals(model, show.label = TRUE) plotYResiduals(model, show.label = TRUE) plotPredictions(model) plotPredictions(model, ncomp = 4, xlab = 'C, reference', ylab = 'C, predictions') par(mfrow = c(1, 1)) ## 8. Selectivity ratio and VIP scores plots par(mfrow = c(2, 2)) plotSelectivityRatio(model) plotSelectivityRatio(model, ncomp = 1) par(mfrow = c(1, 1)) ## 9. Variable selection with selectivity ratio selratio = getSelectivityRatio(model) selvar = !(selratio < 8) xsel = x[, selvar] modelsel = pls(xsel, y, ncomp = 6, cv = 1) modelsel = selectCompNum(modelsel, 3) summary(model) summary(modelsel) ## 10. Calculate average spectrum and show the selected variables i = 1:ncol(x) ms = apply(x, 2, mean) par(mfrow = c(2, 2)) plot(i, ms, type = 'p', pch = 16, col = 'red', main = 'Original variables') plotPredictions(model) plot(i, ms, type = 'p', pch = 16, col = 'lightgray', main = 'Selected variables') points(i[selvar], ms[selvar], col = 'red', pch = 16) plotPredictions(modelsel) par(mfrow = c(1, 1))
Calibrates (builds) a PLS model for given data and parameters
pls.cal(x, y, ncomp, center, scale, method = "simpls", cv = FALSE)pls.cal(x, y, ncomp, center, scale, method = "simpls", cv = FALSE)
x |
a matrix with x values (predictors) |
y |
a matrix with y values (responses) |
ncomp |
number of components to calculate |
center |
logical, do mean centering or not |
scale |
logical, do standardization or not |
method |
algorithm for computing PLS model (only 'simpls' is supported so far) |
cv |
logical, is model calibrated during cross-validation or not (or cv settings for calibration) |
model an object with calibrated PLS model
Compute coordinates of lines or curves with critical limits
pls.getLimitsCoordinates(Qlim, T2lim, Zlim, nobj, ncomp, norm, log)pls.getLimitsCoordinates(Qlim, T2lim, Zlim, nobj, ncomp, norm, log)
Qlim |
matrix with critical limits for orthogonal distances (X) |
T2lim |
matrix with critical limits for score distances (X) |
Zlim |
matrix with critical limits for orthogonal distances (Y) |
nobj |
number of objects to compute the limits for |
ncomp |
number of components for computing the coordinates |
norm |
logical, shall distance values be normalized or not |
log |
logical, shall log transformation be applied or not |
list with two matrices (x and y coordinates of corresponding limits)
Compute predictions for response values
pls.getpredictions( x, coeffs, ycenter, yscale, ynames = NULL, y.attrs = NULL, objnames = NULL, compnames = NULL )pls.getpredictions( x, coeffs, ycenter, yscale, ynames = NULL, y.attrs = NULL, objnames = NULL, compnames = NULL )
x |
matrix with predictors, already preprocessed (e.g. mean centered) and cleaned |
coeffs |
array with regression coefficients |
ycenter |
'ycenter' property of PLS model |
yscale |
'yscale' property of PLS model |
ynames |
vector with names of the responses |
y.attrs |
list with response attributes (e.g. from reference values if any) |
objnames |
vector with names of objects (rows of x) |
compnames |
vector with names used for components |
array with predicted y-values
Compute object with decomposition of x-values
pls.getxdecomp( x, xscores, xloadings, xeigenvals, xnames = NULL, x.attrs = NULL, objnames = NULL, compnames = NULL )pls.getxdecomp( x, xscores, xloadings, xeigenvals, xnames = NULL, x.attrs = NULL, objnames = NULL, compnames = NULL )
x |
matrix with predictors, already preprocessed (e.g. mean centered) and cleaned |
xscores |
matrix with X-scores |
xloadings |
matrix with X-loadings |
xeigenvals |
matrix with eigenvalues for X |
xnames |
vector with names of the predictors |
x.attrs |
list with preditors attributes |
objnames |
vector with names of objects (rows of x) |
compnames |
vector with names used for components |
array 'ldecomp' object for x-values
Compute matrix with X-scores
pls.getxscores(x, weights, xloadings)pls.getxscores(x, weights, xloadings)
x |
matrix with predictors, already preprocessed and cleaned |
weights |
matrix with PLS weights |
xloadings |
matrix with X-loadings |
matrix with X-scores
Compute object with decomposition of y-values
pls.getydecomp( y, yscores, xscores, yloadings, yeigenvals, ynames = NULL, y.attrs = NULL, x.attrs = NULL, objnames = NULL, compnames = NULL )pls.getydecomp( y, yscores, xscores, yloadings, yeigenvals, ynames = NULL, y.attrs = NULL, x.attrs = NULL, objnames = NULL, compnames = NULL )
y |
matrix with responses, already preprocessed (e.g. mean centered) and cleaned |
yscores |
matrix with Y-scores |
xscores |
matrix with X-scores |
yloadings |
matrix with Y-loadings |
yeigenvals |
matrix with eigenvalues for Y |
ynames |
vector with names of the responses |
y.attrs |
list with response attributes (e.g. from reference values if any) |
x.attrs |
list with preditors attributes |
objnames |
vector with names of objects (rows of x) |
compnames |
vector with names used for components |
array 'ldecomp' object for y-values (or NULL if y is not provided)
Compute and orthogonalize matrix with Y-scores
pls.getyscores(y, yloadings, xscores)pls.getyscores(y, yloadings, xscores)
y |
matrix with response values, already preprocessed and cleaned |
yloadings |
matrix with Y-loadings |
xscores |
matrix with X-scores (needed for orthogonalization) |
matrix with Y-scores
Compute critical limits for orthogonal distances (Q)
pls.getZLimits(lim.type, alpha, gamma, params)pls.getZLimits(lim.type, alpha, gamma, params)
lim.type |
which method to use for calculation of critical limits for residuals |
alpha |
significance level for extreme limits. |
gamma |
significance level for outlier limits. |
params |
distribution parameters returned by ldecomp.getLimParams |
Runs selected PLS algorithm
pls.run(x, y, ncomp = min(nrow(x) - 1, ncol(x)), method = "simpls", cv = FALSE)pls.run(x, y, ncomp = min(nrow(x) - 1, ncol(x)), method = "simpls", cv = FALSE)
x |
a matrix with x values (predictors from calibration set) |
y |
a matrix with y values (responses from calibration set) |
ncomp |
how many components to compute |
method |
algorithm for computing PLS model |
cv |
logical, is this for CV or not |
SIMPLS algorithm for calibration of PLS model
pls.simpls(x, y, ncomp, cv = FALSE)pls.simpls(x, y, ncomp, cv = FALSE)
x |
a matrix with x values (predictors) |
y |
a matrix with y values (responses) |
ncomp |
number of components to calculate |
cv |
logical, is model calibrated during cross-validation or not |
a list with computed regression coefficients, loadings and scores for x and y matrices, and weights.
[1]. S. de Jong. SIMPLS: An Alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 1993 (251-263).
SIMPLS algorithm for calibration of PLS model (old version)
pls.simplsold(x, y, ncomp, cv = FALSE)pls.simplsold(x, y, ncomp, cv = FALSE)
x |
a matrix with x values (predictors) |
y |
a matrix with y values (responses) |
ncomp |
number of components to calculate |
cv |
logical, is model calibrated during cross-validation or not |
a list with computed regression coefficients, loadings and scores for x and y matrices, and weights.
[1]. S. de Jong. SIMPLS: An Alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 1993 (251-263).
plsda is used to calibrate, validate and use of partial least squares discrimination
analysis (PLS-DA) model.
plsda( x, c, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, cv = NULL, exclcols = NULL, exclrows = NULL, x.test = NULL, c.test = NULL, method = "simpls", lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, info = "", ncomp.selcrit = "min", classname = NULL, cv.scope = "local" )plsda( x, c, ncomp = min(nrow(x) - 1, ncol(x), 20), center = TRUE, scale = FALSE, cv = NULL, exclcols = NULL, exclrows = NULL, x.test = NULL, c.test = NULL, method = "simpls", lim.type = "ddmoments", alpha = 0.05, gamma = 0.01, info = "", ncomp.selcrit = "min", classname = NULL, cv.scope = "local" )
x |
matrix with predictors. |
c |
vector with class membership (should be either a factor with class names/numbers in case of multiple classes or a vector with logical values in case of one class model). |
ncomp |
maximum number of components to calculate. |
center |
logical, center or not predictors and response values. |
scale |
logical, scale (standardize) or not predictors and response values. |
cv |
cross-validation settings (see details). |
exclcols |
columns of x to be excluded from calculations (numbers, names or vector with logical values) |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
x.test |
matrix with predictors for test set. |
c.test |
vector with reference class values for test set (same format as calibration values). |
method |
method for calculating PLS model. |
lim.type |
which method to use for calculation of critical limits for residual distances (see details) |
alpha |
significance level for extreme limits for T2 and Q disances. |
gamma |
significance level for outlier limits for T2 and Q distances. |
info |
short text with information about the model. |
ncomp.selcrit |
criterion for selecting optimal number of components ( |
classname |
name (label) of class in case if PLS-DA is used for one-class discrimination model. In this case it is expected that parameter 'c' will be a vector with logical values. |
cv.scope |
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set. |
The plsda class is based on pls with extra functions and plots covering
classification functionality. All plots for pls can be used. E.g. of you want to see the
real predicted values (y in PLS) instead of classes use plotPredictions.pls(model) instead
of plotPredictions(model).
Cross-validation settings, cv, can be a number or a list. If cv is a number, it
will be used as a number of segments for random cross-validation (if cv = 1, full
cross-validation will be preformed). If it is a list, the following syntax can be used:
cv = list('rand', nseg, nrep) for random repeated cross-validation with nseg
segments and nrep repetitions or cv = list('ven', nseg) for systematic splits
to nseg segments ('venetian blinds').
Calculation of confidence intervals and p-values for regression coefficients are available
only by jack-knifing so far. See help for regcoeffs objects for details.
Returns an object of plsda class with following fields (most inherited from class
pls):
ncomp |
number of components included to the model. |
ncomp.selected |
selected (optimal) number of components. |
xloadings |
matrix with loading values for x decomposition. |
yloadings |
matrix with loading values for y (c) decomposition. |
weights |
matrix with PLS weights. |
coeffs |
matrix with regression coefficients calculated for each component. |
info |
information about the model, provided by user when build the model. |
calres |
an object of class |
testres |
an object of class |
cvres |
an object of class |
Sergey Kucheryavskiy ([email protected])
Specific methods for plsda class:
print.plsda |
prints information about a pls object. |
summary.plsda |
shows performance statistics for the model. |
plot.plsda |
shows plot overview of the model. |
predict.plsda |
applies PLS-DA model to a new data. |
Methods, inherited from classmodel class:
plotPredictions.classmodel |
shows plot with predicted values. |
plotSensitivity.classmodel |
shows sensitivity plot. |
plotSpecificity.classmodel |
shows specificity plot. |
plotMisclassified.classmodel |
shows misclassified ratio plot. |
See also methods for class pls.
### Examples for PLS-DA model class library(mdatools) ## 1. Make a PLS-DA model with full cross-validation and show model overview # make a calibration set from iris data (3 classes) # use names of classes as class vector x.cal = iris[seq(1, nrow(iris), 2), 1:4] c.cal = iris[seq(1, nrow(iris), 2), 5] model = plsda(x.cal, c.cal, ncomp = 3, cv = 1, info = 'IRIS data example') model = selectCompNum(model, 1) # show summary and basic model plots # misclassification will be shown only for first class summary(model) plot(model) # summary and model plots for second class summary(model, nc = 2) plot(model, nc = 2) # summary and model plot for specific class and number of components summary(model, nc = 3, ncomp = 3) plot(model, nc = 3, ncomp = 3) ## 2. Show performance plots for a model par(mfrow = c(2, 2)) plotSpecificity(model) plotSensitivity(model) plotMisclassified(model) plotMisclassified(model, nc = 2) par(mfrow = c(1, 1)) ## 3. Show both class and y values predictions par(mfrow = c(2, 2)) plotPredictions(model) plotPredictions(model, res = "cal", ncomp = 2, nc = 2) plotPredictions(structure(model, class = "regmodel")) plotPredictions(structure(model, class = "regmodel"), ncomp = 2, ny = 2) par(mfrow = c(1, 1)) ## 4. All plots from ordinary PLS can be used, e.g.: par(mfrow = c(2, 2)) plotXYScores(model) plotYVariance(model) plotXResiduals(model) plotRegcoeffs(model, ny = 2) par(mfrow = c(1, 1))### Examples for PLS-DA model class library(mdatools) ## 1. Make a PLS-DA model with full cross-validation and show model overview # make a calibration set from iris data (3 classes) # use names of classes as class vector x.cal = iris[seq(1, nrow(iris), 2), 1:4] c.cal = iris[seq(1, nrow(iris), 2), 5] model = plsda(x.cal, c.cal, ncomp = 3, cv = 1, info = 'IRIS data example') model = selectCompNum(model, 1) # show summary and basic model plots # misclassification will be shown only for first class summary(model) plot(model) # summary and model plots for second class summary(model, nc = 2) plot(model, nc = 2) # summary and model plot for specific class and number of components summary(model, nc = 3, ncomp = 3) plot(model, nc = 3, ncomp = 3) ## 2. Show performance plots for a model par(mfrow = c(2, 2)) plotSpecificity(model) plotSensitivity(model) plotMisclassified(model) plotMisclassified(model, nc = 2) par(mfrow = c(1, 1)) ## 3. Show both class and y values predictions par(mfrow = c(2, 2)) plotPredictions(model) plotPredictions(model, res = "cal", ncomp = 2, nc = 2) plotPredictions(structure(model, class = "regmodel")) plotPredictions(structure(model, class = "regmodel"), ncomp = 2, ny = 2) par(mfrow = c(1, 1)) ## 4. All plots from ordinary PLS can be used, e.g.: par(mfrow = c(2, 2)) plotXYScores(model) plotYVariance(model) plotXResiduals(model) plotRegcoeffs(model, ny = 2) par(mfrow = c(1, 1))
plsdares is used to store and visualize results of applying a PLS-DA model to a new data.
plsdares(plsres, cres)plsdares(plsres, cres)
plsres |
PLS results for the data. |
cres |
Classification results for the data. |
Do not use plsdares manually, the object is created automatically when one applies a
PLS-DA model to a new data set, e.g. when calibrate and validate a PLS-DA model (all calibration
and validation results in PLS-DA model are stored as objects of plsdares class) or use
function predict.plsda.
The object gives access to all PLS-DA results as well as to the plotting methods for
visualisation of the results. The plsidares class also inherits all properties and methods
of classres and plsres classes.
If no reference values provided, classification statistics will not be calculated and performance plots will not be available.
Returns an object of plsdares class with fields, inherited from classres
and plsres.
Methods for plsda objects:
print.plsda |
shows information about the object. |
summary.plsda |
shows statistics for results of classification. |
plot.plsda |
shows plots for overview of the results. |
Methods, inherited from classres class:
showPredictions.classres |
show table with predicted values. |
plotPredictions.classres |
makes plot with predicted values. |
plotSensitivity.classres |
makes plot with sensitivity vs. components values. |
plotSpecificity.classres |
makes plot with specificity vs. components values. |
plotPerformance.classres |
makes plot with both specificity and sensitivity values. |
Methods for plsres objects:
print |
prints information about a plsres object. |
summary.plsres |
shows performance statistics for the results. |
plot.plsres |
shows plot overview of the results. |
plotXScores.plsres |
shows scores plot for x decomposition. |
plotXYScores.plsres |
shows scores plot for x and y decomposition. |
plotXVariance.plsres |
shows explained variance plot for x decomposition. |
plotYVariance.plsres |
shows explained variance plot for y decomposition. |
plotXCumVariance.plsres |
shows cumulative explained variance plot for y decomposition. |
plotYCumVariance.plsres |
shows cumulative explained variance plot for y decomposition. |
plotXResiduals.plsres |
shows T2 vs. Q plot for x decomposition. |
plotYResiduals.plsres |
shows residuals plot for y values. |
Methods inherited from regres class (parent class for plsres):
plotPredictions.regres |
shows predicted vs. measured plot. |
plotRMSE.regres |
shows RMSE plot. |
See also plsda - a class for PLS-DA models, predict.plsda applying
PLS-DA model for a new dataset.
### Examples for PLS-DA results class library(mdatools) ## 1. Make a PLS-DA model with full cross-validation, get ## calibration results and show overview # make a calibration set from iris data (3 classes) # use names of classes as class vector x.cal = iris[seq(1, nrow(iris), 2), 1:4] c.cal = iris[seq(1, nrow(iris), 2), 5] model = plsda(x.cal, c.cal, ncomp = 3, cv = 1, info = 'IRIS data example') model = selectCompNum(model, 1) res = model$calres # show summary and basic plots for calibration results summary(res) plot(res) ## 2. Apply the calibrated PLS-DA model to a new dataset # make a new data x.new = iris[seq(2, nrow(iris), 2), 1:4] c.new = iris[seq(2, nrow(iris), 2), 5] res = predict(model, x.new, c.new) summary(res) plot(res) ## 3. Show performance plots for the results par(mfrow = c(2, 2)) plotSpecificity(res) plotSensitivity(res) plotMisclassified(res) plotMisclassified(res, nc = 2) par(mfrow = c(1, 1)) ## 3. Show both class and y values predictions par(mfrow = c(2, 2)) plotPredictions(res) plotPredictions(res, ncomp = 2, nc = 2) plotPredictions(structure(res, class = "regres")) plotPredictions(structure(res, class = "regres"), ncomp = 2, ny = 2) par(mfrow = c(1, 1)) ## 4. All plots from ordinary PLS results can be used, e.g.: par(mfrow = c(2, 2)) plotXYScores(res) plotYVariance(res, type = 'h') plotXVariance(res, type = 'h') plotXResiduals(res) par(mfrow = c(1, 1))### Examples for PLS-DA results class library(mdatools) ## 1. Make a PLS-DA model with full cross-validation, get ## calibration results and show overview # make a calibration set from iris data (3 classes) # use names of classes as class vector x.cal = iris[seq(1, nrow(iris), 2), 1:4] c.cal = iris[seq(1, nrow(iris), 2), 5] model = plsda(x.cal, c.cal, ncomp = 3, cv = 1, info = 'IRIS data example') model = selectCompNum(model, 1) res = model$calres # show summary and basic plots for calibration results summary(res) plot(res) ## 2. Apply the calibrated PLS-DA model to a new dataset # make a new data x.new = iris[seq(2, nrow(iris), 2), 1:4] c.new = iris[seq(2, nrow(iris), 2), 5] res = predict(model, x.new, c.new) summary(res) plot(res) ## 3. Show performance plots for the results par(mfrow = c(2, 2)) plotSpecificity(res) plotSensitivity(res) plotMisclassified(res) plotMisclassified(res, nc = 2) par(mfrow = c(1, 1)) ## 3. Show both class and y values predictions par(mfrow = c(2, 2)) plotPredictions(res) plotPredictions(res, ncomp = 2, nc = 2) plotPredictions(structure(res, class = "regres")) plotPredictions(structure(res, class = "regres"), ncomp = 2, ny = 2) par(mfrow = c(1, 1)) ## 4. All plots from ordinary PLS results can be used, e.g.: par(mfrow = c(2, 2)) plotXYScores(res) plotYVariance(res, type = 'h') plotXVariance(res, type = 'h') plotXResiduals(res) par(mfrow = c(1, 1))
plsres is used to store and visualize results of applying a PLS model to a new data.
plsres( y.pred, y.ref = NULL, ncomp.selected = dim(y.pred)[2], xdecomp = NULL, ydecomp = NULL, info = "" )plsres( y.pred, y.ref = NULL, ncomp.selected = dim(y.pred)[2], xdecomp = NULL, ydecomp = NULL, info = "" )
y.pred |
predicted y values. |
y.ref |
reference (measured) y values. |
ncomp.selected |
selected (optimal) number of components. |
xdecomp |
PLS decomposition of X data (object of class |
ydecomp |
PLS decomposition of Y data (object of class |
info |
information about the object. |
Do not use plsres manually, the object is created automatically when one applies a PLS
model to a new data set, e.g. when calibrate and validate a PLS model (all calibration and
validation results in PLS model are stored as objects of plsres class) or use function
predict.pls.
The object gives access to all PLS results as well as to the plotting methods for visualisation
of the results. The plsres class also inherits all properties and methods of regres
- general class for regression results.
If no reference values provided, regression statistics will not be calculated and most of the plots not available. The class is also used for cross-validation results, in this case some of the values and methods are not available (e.g. scores and scores plot, etc.).
All plots are based on mdaplot function, so most of its options can be used (e.g.
color grouping, etc.).
RPD is ratio of standard deviation of response values to standard error of prediction (SDy/SEP).
Returns an object of plsres class with following fields:
ncomp |
number of components included to the model. |
ncomp.selected |
selected (optimal) number of components. |
y.ref |
a matrix with reference values for responses. |
y.pred |
a matrix with predicted values for responses. |
rmse |
a matrix with root mean squared error values for each response and component. |
slope |
a matrix with slope values for each response and component. |
r2 |
a matrix with determination coefficients for each response and component. |
bias |
a matrix with bias values for each response and component. |
sep |
a matrix with standard error values for each response and component. |
rpd |
a matrix with RPD values for each response and component. |
xdecomp |
decomposition of predictors (object of class |
ydecomp |
decomposition of responses (object of class |
info |
information about the object. |
Methods for plsres objects:
print |
prints information about a plsres object. |
summary.plsres |
shows performance statistics for the results. |
plot.plsres |
shows plot overview of the results. |
plotXScores.plsres |
shows scores plot for x decomposition. |
plotXYScores.plsres |
shows scores plot for x and y decomposition. |
plotXVariance.plsres |
shows explained variance plot for x decomposition. |
plotYVariance.plsres |
shows explained variance plot for y decomposition. |
plotXCumVariance.plsres |
shows cumulative explained variance plot for y decomposition. |
plotYCumVariance.plsres |
shows cumulative explained variance plot for y decomposition. |
plotXResiduals.plsres |
shows T2 vs. Q plot for x decomposition. |
plotYResiduals.plsres |
shows residuals plot for y values. |
Methods inherited from regres class (parent class for plsres):
plotPredictions.regres |
shows predicted vs. measured plot. |
plotRMSE.regres |
shows RMSE plot. |
See also pls - a class for PLS models.
### Examples of using PLS result class library(mdatools) ## 1. Make a PLS model for concentration of first component ## using full-cross validation and get calibration results data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 8, cv = 1) model = selectCompNum(model, 2) res = model$calres summary(res) plot(res) ## 2. Make a PLS model for concentration of first component ## and apply model to a new dataset data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 6, cv = 1) model = selectCompNum(model, 2) x.new = simdata$spectra.t y.new = simdata$conc.t[, 1] res = predict(model, x.new, y.new) summary(res) plot(res) ## 3. Show variance and error plots for PLS results par(mfrow = c(2, 2)) plotXCumVariance(res, type = 'h') plotYCumVariance(res, type = 'b', show.labels = TRUE, legend.position = 'bottomright') plotRMSE(res) plotRMSE(res, type = 'h', show.labels = TRUE) par(mfrow = c(1, 1)) ## 4. Show scores plots for PLS results ## (for results plot we can use color grouping) par(mfrow = c(2, 2)) plotXScores(res) plotXScores(res, show.labels = TRUE, cgroup = y.new) plotXYScores(res) plotXYScores(res, comp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) ## 5. Show predictions and residuals plots for PLS results par(mfrow = c(2, 2)) plotXResiduals(res, show.label = TRUE, cgroup = y.new) plotYResiduals(res, show.label = TRUE) plotPredictions(res) plotPredictions(res, ncomp = 4, xlab = 'C, reference', ylab = 'C, predictions') par(mfrow = c(1, 1))### Examples of using PLS result class library(mdatools) ## 1. Make a PLS model for concentration of first component ## using full-cross validation and get calibration results data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 8, cv = 1) model = selectCompNum(model, 2) res = model$calres summary(res) plot(res) ## 2. Make a PLS model for concentration of first component ## and apply model to a new dataset data(simdata) x = simdata$spectra.c y = simdata$conc.c[, 1] model = pls(x, y, ncomp = 6, cv = 1) model = selectCompNum(model, 2) x.new = simdata$spectra.t y.new = simdata$conc.t[, 1] res = predict(model, x.new, y.new) summary(res) plot(res) ## 3. Show variance and error plots for PLS results par(mfrow = c(2, 2)) plotXCumVariance(res, type = 'h') plotYCumVariance(res, type = 'b', show.labels = TRUE, legend.position = 'bottomright') plotRMSE(res) plotRMSE(res, type = 'h', show.labels = TRUE) par(mfrow = c(1, 1)) ## 4. Show scores plots for PLS results ## (for results plot we can use color grouping) par(mfrow = c(2, 2)) plotXScores(res) plotXScores(res, show.labels = TRUE, cgroup = y.new) plotXYScores(res) plotXYScores(res, comp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) ## 5. Show predictions and residuals plots for PLS results par(mfrow = c(2, 2)) plotXResiduals(res, show.label = TRUE, cgroup = y.new) plotYResiduals(res, show.label = TRUE) plotPredictions(res) plotPredictions(res, ncomp = 4, xlab = 'C, reference', ylab = 'C, predictions') par(mfrow = c(1, 1))
Applies MCR-ALS model to a new set of spectra and returns matrix with contributions.
## S3 method for class 'mcrals' predict(object, x, ...)## S3 method for class 'mcrals' predict(object, x, ...)
object |
an MCR model (object of class |
x |
spectral values (matrix or data frame). |
... |
other arguments. |
Matrix with contributions
Applies MCR model to a new set of spectra and returns matrix with contributions.
## S3 method for class 'mcrpure' predict(object, x, ...)## S3 method for class 'mcrpure' predict(object, x, ...)
object |
an MCR model (object of class |
x |
spectral values (matrix or data frame). |
... |
other arguments. |
Matrix with contributions
Applies PCA model to a new data set.
## S3 method for class 'pca' predict(object, x, ...)## S3 method for class 'pca' predict(object, x, ...)
object |
a PCA model (object of class |
x |
data values (matrix or data frame). |
... |
other arguments. |
PCA results (an object of class pcares)
Applies PLS model to a new data set
## S3 method for class 'pls' predict(object, x, y = NULL, cv = FALSE, ...)## S3 method for class 'pls' predict(object, x, y = NULL, cv = FALSE, ...)
object |
a PLS model (object of class |
x |
a matrix with x values (predictors) |
y |
a matrix with reference y values (responses) |
cv |
logical, shall predictions be made for cross-validation procedure or not |
... |
other arguments |
See examples in help for pls function.
PLS results (an object of class plsres)
Applies PLS-DA model to a new data set
## S3 method for class 'plsda' predict(object, x, c.ref = NULL, ...)## S3 method for class 'plsda' predict(object, x, c.ref = NULL, ...)
object |
a PLS-DA model (object of class |
x |
a matrix with x values (predictors) |
c.ref |
a vector with reference class values (should be a factor) |
... |
other arguments |
See examples in help for plsda function.
PLS-DA results (an object of class plsdares)
Applies SIMCA model to a new data set
## S3 method for class 'simca' predict(object, x, c.ref = NULL, cal = FALSE, ...)## S3 method for class 'simca' predict(object, x, c.ref = NULL, cal = FALSE, ...)
object |
a SIMCA model (object of class |
x |
a matrix with x values (predictors) |
c.ref |
a vector with reference class names (same as class names for models) |
cal |
logical, are predictions for calibration set or not |
... |
other arguments |
See examples in help for simca function.
SIMCA results (an object of class simcares)
Applies SIMCAM model (SIMCA for multiple classes) to a new data set
## S3 method for class 'simcam' predict(object, x, c.ref = NULL, ...)## S3 method for class 'simcam' predict(object, x, c.ref = NULL, ...)
object |
a SIMCAM model (object of class |
x |
a matrix with x values (predictors) |
c.ref |
a vector with reference class names (same as class names in models) |
... |
other arguments |
See examples in help for simcam function.
SIMCAM results (an object of class simcamres)
Class for preprocessing object
prep(name, params = NULL, method = NULL)prep(name, params = NULL, method = NULL)
name |
short text with name for the preprocessing method. |
params |
a list with parameters for the method (if NULL - default parameters will be used). |
method |
method to call when applying the preprocessing, provide it only for user defined methods. |
Use this class to create a list with a sequence of preprocessing methods to keep them together
in right order and with defined parameters. The list/object can be provided as an extra argument
to any modelling function (e.g. pca, pls, etc), so the optimal model parameters and
the optimal preprocessing will be stored together and can be applied to a raw data by using
method predict.
For your own preprocessing method you need to create a function, which takes matrix with values (dataset) as the first argument, does something and then return a matrix with the same dimension and same attributes as the result. The method can have any number of optional parameters.
See Bookdown tutorial for details.
Baseline correction using asymetric least squares
prep.alsbasecorr(data, plambda = 5, p = 0.1, max.niter = 10)prep.alsbasecorr(data, plambda = 5, p = 0.1, max.niter = 10)
data |
matrix with spectra (rows correspond to individual spectra) |
plambda |
power of the penalty parameter (e.g. if plambda = 5, lambda = 10^5) |
p |
assymetry ratio (should be between 0 and 1) |
max.niter |
maximum number of iterations |
The function implements baseline correction algorithm based on Whittaker smoother. The method was first shown in [1]. The function has two main parameters - power of a penalty parameter (usually varies betwen 2 and 9) and the ratio of assymetry (usually between 0.1 and 0.001). The choice of the parameters depends on how broad the disturbances of the baseline are and how narrow the original spectral peaks are.
preprocessed spectra.
# take spectra from carbs dataset data(carbs) spectra = mda.t(carbs$S) # apply the correction pspectra = prep.alsbasecorr(spectra, plambda = 3, p = 0.01) # show the original and the corrected spectra individually par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg(list( original = mda.subset(spectra, i), corrected = mda.subset(pspectra, i) ), type = "l", col = c("black", "red"), lwd = c(2, 1), main = rownames(spectra)[i]) }# take spectra from carbs dataset data(carbs) spectra = mda.t(carbs$S) # apply the correction pspectra = prep.alsbasecorr(spectra, plambda = 3, p = 0.01) # show the original and the corrected spectra individually par(mfrow = c(3, 1)) for (i in 1:3) { mdaplotg(list( original = mda.subset(spectra, i), corrected = mda.subset(pspectra, i) ), type = "l", col = c("black", "red"), lwd = c(2, 1), main = rownames(spectra)[i]) }
Autoscale (mean center and standardize) values in columns of data matrix.
The use of 'max.cov' allows to avoid overestimation of inert variables, which vary very little. Note, that the 'max.cov' value is already in percent, e.g. if 'max.cov = 0.1' it will compare the coefficient of variation of every variable with 0.1 want to use this option simply keep 'max.cov = 0'.
prep.autoscale(data, center = TRUE, scale = FALSE, max.cov = 0)prep.autoscale(data, center = TRUE, scale = FALSE, max.cov = 0)
data |
a matrix with data values |
center |
a logical value or vector with numbers for centering |
scale |
a logical value or vector with numbers for weighting |
max.cov |
columns that have coefficient of variation (in percent) below or equal to 'max.cov' will not be scaled |
data matrix with processed values
Generic function for preprocessing
prep.generic(x, f, ...)prep.generic(x, f, ...)
x |
data matrix to be preprocessed |
f |
function for preprocessing |
... |
arguments for the function f |
Shows information about all implemented preprocessing methods.
prep.list()prep.list()
Applies Multiplicative Scatter Correction (MSC) transformation to data matrix (spectra)
prep.msc(data, mspectrum = NULL)prep.msc(data, mspectrum = NULL)
data |
a matrix with data values (spectra) |
mspectrum |
mean spectrum (if NULL will be calculated from |
MSC is used to remove scatter effects (baseline offset and slope) from spectral data, e.g. NIR spectra.
@examples
### Apply MSC to spectra from simdata
library(mdatools) data(simdata)
spectra = simdata$spectra.c cspectra = prep.msc(spectra)
par(mfrow = c(2, 1)) mdaplot(spectra, type = "l", main = "Before MSC") mdaplot(cspectra, type = "l", main = "After MSC")
preprocessed spectra (calculated mean spectrum is assigned as attribut 'mspectrum')
Normalizes signals (rows of data matrix).
prep.norm(data, type = "area", col.ind = NULL, ref.spectrum = NULL)prep.norm(data, type = "area", col.ind = NULL, ref.spectrum = NULL)
data |
a matrix with data values |
type |
type of normalization |
col.ind |
indices of columns (can be either integer or logical valuws) for normalization to internal standard peak. |
ref.spectrum |
reference spectrum for PQN normalization, if not provided a mean spectrum for data is used |
The "area", "length", "sum" types do preprocessing to unit area (sum of
absolute values), length or sum of all values in every row of data matrix. Type "snv"
does the Standard Normal Variate normalization, similar to prep.snv. Type
"is" does the normalization to internal standard peak, whose position is defined by
parameter 'col.ind'. If the position is a single value, the rows are normalized to the height
of this peak. If 'col.ind' points on several adjucent vales, the rows are normalized to the area
under the peak - sum of the intensities.
The "pqn" is Probabilistic Quotient Normalization as described in [1]. In this case you also
need to provide a reference spectrum (e.g. mean or median of spectra for some reference samples). If
reference spectrum is not provided it will be computed as mean of the spectra to be
preprocessed (parameter data).
data matrix with normalized values
1. F. Dieterle, A. Ross, H. Senn. Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1 H NMR Metabonomics. Anal. Chem. 2006, 78, 4281–4290.
Applies Kubelka-Munk (km) transformation to data matrix (spectra)
prep.ref2km(data)prep.ref2km(data)
data |
a matrix with spectra values (absolute reflectance values) |
Kubelka-Munk is useful preprocessing method for diffuse reflection spectra (e.g. taken for powders or rough surface). It transforms the reflectance spectra R to K/M units as follows: (1 - R)^2 / 2R
preprocessed spectra.
Applies Savytzky-Golay filter to the rows of data matrix
prep.savgol(data, width = 3, porder = 1, dorder = 0)prep.savgol(data, width = 3, porder = 1, dorder = 0)
data |
a matrix with data values |
width |
width of the filter window |
porder |
order of polynomial used for smoothing |
dorder |
order of derivative to take (0 - no derivative) |
The function implements algorithm described in [1] which handles the edge points correctly and does not require to cut the spectra.
1. Peter A. Gorry. General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Anal. Chem. 1990, 62, 6, 570–573, https://doi.org/10.1021/ac00205a007.
Applies Standard Normal Variate (SNV) transformation to the rows of data matrix
prep.snv(data)prep.snv(data)
data |
a matrix with data values |
SNV is a simple preprocessing to remove scatter effects (baseline offset and slope) from spectral data, e.g. NIR spectra.
@examples
### Apply SNV to spectra from simdata
library(mdatools) data(simdata)
spectra = simdata$spectra.c wavelength = simdata$wavelength
cspectra = prep.snv(spectra)
par(mfrow = c(2, 1)) mdaplot(cbind(wavelength, t(spectra)), type = 'l', main = 'Before SNV') mdaplot(cbind(wavelength, t(cspectra)), type = 'l', main = 'After SNV')
data matrix with processed values
Transforms values from using any mathematical function (e.g. log).
prep.transform(data, fun, ...)prep.transform(data, fun, ...)
data |
a matrix with data values |
fun |
reference to a transformation function, e.g. 'log' or 'function(x) x^2'. |
... |
optional parameters for the transformation function |
data matrix with transformed values
# generate a matrix with two columns y <- cbind(rnorm(100, 10, 1), rnorm(100, 20, 2)) # apply log transformation py1 = prep.transform(y, log) # apply power transformation py2 = prep.transform(y, function(x) x^-1.25) # show distributions par(mfrow = c(2, 3)) for (i in 1:2) { hist(y[, i], main = paste0("Original values, column #", i)) hist(py1[, i], main = paste0("Log-transformed, column #", i)) hist(py2[, i], main = paste0("Power-transformed, column #", i)) }# generate a matrix with two columns y <- cbind(rnorm(100, 10, 1), rnorm(100, 20, 2)) # apply log transformation py1 = prep.transform(y, log) # apply power transformation py2 = prep.transform(y, function(x) x^-1.25) # show distributions par(mfrow = c(2, 3)) for (i in 1:2) { hist(y[, i], main = paste0("Original values, column #", i)) hist(py1[, i], main = paste0("Log-transformed, column #", i)) hist(py2[, i], main = paste0("Power-transformed, column #", i)) }
Returns dataset with selected variables
prep.varsel(data, var.ind)prep.varsel(data, var.ind)
data |
a matrix with data values |
var.ind |
indices of variables (columns) to select, can bet either numeric or logical |
data matrix with the selected variables (columns)
The function checks that 'data' contains correct numeric values, check for mandatory attributes (row and column names, x- and y-axis values and names, etc.) and add them if necessary.
Another things is to remove hidden columns and split the rest to visible and hidden values (if excluded rows are present).
preparePlotData(data)preparePlotData(data)
data |
dataset (vector, matrix or data frame) |
Prepares calibration data
prepCalData(x, exclrows = NULL, exclcols = NULL, min.nrows = 1, min.ncols = 2)prepCalData(x, exclrows = NULL, exclcols = NULL, min.nrows = 1, min.ncols = 2)
x |
matrix or data frame with values (calibration set) |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
exclcols |
columns to be excluded from calculations (numbers, names or vector with logical values) |
min.nrows |
smallest number of rows which must be in the dataset |
min.ncols |
smallest number of columns which must be in the dataset |
Generic print function for classification results. Prints information about major fields
of the object.
## S3 method for class 'classres' print(x, str = "Classification results (class classres)\nMajor fields:", ...)## S3 method for class 'classres' print(x, str = "Classification results (class classres)\nMajor fields:", ...)
x |
classification results (object of class |
str |
User specified text (e.g. to be used for particular method, like PLS-DA, etc). |
... |
other arguments |
Prints information about the iPLS object structure
## S3 method for class 'ipls' print(x, ...)## S3 method for class 'ipls' print(x, ...)
x |
a iPLS (object of class |
... |
other arguments |
Generic print function for linear decomposition. Prints information about
the ldecomp object.
## S3 method for class 'ldecomp' print(x, str = NULL, ...)## S3 method for class 'ldecomp' print(x, str = NULL, ...)
x |
object of class |
str |
user specified text to show as a description of the object |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'mcrals' print(x, ...)## S3 method for class 'mcrals' print(x, ...)
x |
|
... |
other arguments |
Prints information about the object structure
## S3 method for class 'mcrpure' print(x, ...)## S3 method for class 'mcrpure' print(x, ...)
x |
|
... |
other arguments |
Prints information about the object structure
## S3 method for class 'pca' print(x, ...)## S3 method for class 'pca' print(x, ...)
x |
a PCA model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'pcares' print(x, ...)## S3 method for class 'pcares' print(x, ...)
x |
PCA results (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'pls' print(x, ...)## S3 method for class 'pls' print(x, ...)
x |
a PLS model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'plsda' print(x, ...)## S3 method for class 'plsda' print(x, ...)
x |
a PLS-DA model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'plsdares' print(x, ...)## S3 method for class 'plsdares' print(x, ...)
x |
PLS-DA results (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'plsres' print(x, ...)## S3 method for class 'plsres' print(x, ...)
x |
PLS results (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'randtest' print(x, ...)## S3 method for class 'randtest' print(x, ...)
x |
a randomization test results (object of class |
... |
other arguments |
prints regression coeffocoent values for given response number and amount of components
## S3 method for class 'regcoeffs' print(x, ...)## S3 method for class 'regcoeffs' print(x, ...)
x |
regression coefficients object (class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'regmodel' print(x, ...)## S3 method for class 'regmodel' print(x, ...)
x |
a regression model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'regres' print(x, ...)## S3 method for class 'regres' print(x, ...)
x |
regression results (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'simca' print(x, ...)## S3 method for class 'simca' print(x, ...)
x |
a SIMCA model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'simcam' print(x, ...)## S3 method for class 'simcam' print(x, ...)
x |
a SIMCAM model (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'simcamres' print(x, ...)## S3 method for class 'simcamres' print(x, ...)
x |
SIMCAM results (object of class |
... |
other arguments |
Prints information about the object structure
## S3 method for class 'simcares' print(x, ...)## S3 method for class 'simcares' print(x, ...)
x |
SIMCA results (object of class |
... |
other arguments |
randtest is used to carry out randomization/permutation test for a PLS regression model
randtest( x, y, ncomp = 15, center = TRUE, scale = FALSE, nperm = 1000, sig.level = 0.05, silent = TRUE, exclcols = NULL, exclrows = NULL )randtest( x, y, ncomp = 15, center = TRUE, scale = FALSE, nperm = 1000, sig.level = 0.05, silent = TRUE, exclcols = NULL, exclrows = NULL )
x |
matrix with predictors. |
y |
vector or one-column matrix with response. |
ncomp |
maximum number of components to test. |
center |
logical, center or not predictors and response values. |
scale |
logical, scale (standardize) or not predictors and response values. |
nperm |
number of permutations. |
sig.level |
significance level. |
silent |
logical, show or not test progress. |
exclcols |
columns of x to be excluded from calculations (numbers, names or vector with logical values) |
exclrows |
rows to be excluded from calculations (numbers, names or vector with logical values) |
The class implements a method for selection of optimal number of components in PLS1 regression
based on the randomization test [1]. The basic idea is that for each component from 1 to
ncomp a statistic T, which is a covariance between t-score (X score, derived from a PLS
model) and the reference Y values, is calculated. By repeating this for randomly permuted
Y-values a distribution of the statistic is obtained. A parameter alpha is computed to
show how often the statistic T, calculated for permuted Y-values, is the same or higher than
the same statistic, calculated for original data without permutations.
If a component is important, then the covariance for unpermuted data should be larger than the
covariance for permuted data and therefore the value for alpha will be quie small (there
is still a small chance to get similar covariance). This makes alpha very similar to
p-value in a statistical test.
The randtest procedure calculates alpha for each component, the values can be observed
using summary or plot functions. There are also several function, allowing e.g.
to show distribution of statistics and the critical value for each component.
Returns an object of randtest class with following fields:
nperm |
number of permutations used for the test. |
stat |
statistic values calculated for each component. |
alpha |
alpha values calculated for each component. |
statperm |
matrix with statistic values for each permutation. |
corrperm |
matrix with correlation between predicted and reference y-vales for each permutation. |
ncomp.selected |
suggested number of components. |
S. Wiklund et al. Journal of Chemometrics 21 (2007) 427-439.
Methods for randtest objects:
print.randtest |
prints information about a randtest object. |
summary.randtest |
shows summary statistics for the test. |
plot.randtest |
shows bar plot for alpha values. |
plotHist.randtest |
shows distribution of statistic plot. |
plotCorr.randtest |
shows determination coefficient plot. |
### Examples of using the test ## Get the spectral data from Simdata set and apply SNV transformation data(simdata) y = simdata$conc.c[, 3] x = simdata$spectra.c x = prep.snv(x) ## Run the test and show summary ## (normally use higher nperm values > 1000) r = randtest(x, y, ncomp = 4, nperm = 200, silent = FALSE) summary(r) ## Show plots par( mfrow = c(3, 2)) plot(r) plotHist(r, ncomp = 3) plotHist(r, ncomp = 4) plotCorr(r, 3) plotCorr(r, 4) par( mfrow = c(1, 1))### Examples of using the test ## Get the spectral data from Simdata set and apply SNV transformation data(simdata) y = simdata$conc.c[, 3] x = simdata$spectra.c x = prep.snv(x) ## Run the test and show summary ## (normally use higher nperm values > 1000) r = randtest(x, y, ncomp = 4, nperm = 200, silent = FALSE) summary(r) ## Show plots par( mfrow = c(3, 2)) plot(r) plotHist(r, ncomp = 3) plotHist(r, ncomp = 4) plotCorr(r, 3) plotCorr(r, 4) par( mfrow = c(1, 1))
class for storing and visualisation of regression coefficients for regression models
regcoeffs(coeffs, ci.coeffs = NULL, use.mean = TRUE)regcoeffs(coeffs, ci.coeffs = NULL, use.mean = TRUE)
coeffs |
array (npred x ncomp x nresp) with regression coefficients |
ci.coeffs |
array (npred x ncomp x nresp x cv) with regression coefficients for computing confidence intervals (e.g. from cross-validation) using Jack-Knifing method |
use.mean |
logical, tells how to compute standard error for regression coefficients. If |
a list (object of regcoeffs class) with fields, including:
values |
an array (nvar x ncomp x ny) with regression coefficients |
se |
an array (nvar x ncomp x ny) with standard errors for the coefficients |
t.values |
an array (nvar x ncomp x ny) with t-values for the coefficients |
p.values |
an array (nvar x ncomp x ny) with p-values for coefficients |
last three fields are available if parameter ci.coeffs was provided.
Check also confint.regcoeffs, summary.regcoeffs and
plot.regcoeffs.
calculates standard error, t-values and p-values for regression coefficients based on Jack-Knifing method.
regcoeffs.getStats(coeffs, ci.coeffs = NULL, use.mean = TRUE)regcoeffs.getStats(coeffs, ci.coeffs = NULL, use.mean = TRUE)
coeffs |
array (npred x ncomp x nresp) with regression coefficients |
ci.coeffs |
array (npred x ncomp x nresp x cv) with regression coefficients for computing confidence intervals (e.g. from cross-validation) using Jack-Knifing method |
use.mean |
logical, tells how to compute standard error for regression coefficients. If |
a list with statistics three arrays: srandard error, t-values and p-values computed for each regression coefficient.
Class for storing and visualisation of regression predictions
regres(y.pred, y.ref = NULL, ncomp.selected = 1)regres(y.pred, y.ref = NULL, ncomp.selected = 1)
y.pred |
vector or matrix with y predicted values |
y.ref |
vector with reference (measured) y values |
ncomp.selected |
if y.pred calculated for different components, which to use as default |
a list (object of regres class) with fields, including:
y.pred |
a matrix with predicted values |
y.ref |
a vector with reference (measured) values |
ncomp.selected |
selected column/number of components for predictions |
rmse |
root mean squared error for predicted vs measured values |
slope |
slope for predicted vs measured values |
r2 |
coefficient of determination for predicted vs measured values |
bias |
bias for predicted vs measured values |
rpd |
RPD values |
Calculates matrix with bias (average prediction error) for every response and components
regres.bias(err)regres.bias(err)
err |
vector with difference between reference and predicted y-values |
Calculates array of differences between predicted and reference values.
regres.err(y.pred, y.ref)regres.err(y.pred, y.ref)
y.pred |
matrix with predicted values |
y.ref |
vector with reference values |
Calculates matrix with coeffient of determination for every response and components
regres.r2(err, ytot)regres.r2(err, ytot)
err |
vector with difference between reference and predicted y-values |
ytot |
total variance for y-values |
Calculates matrix with root mean squared error of prediction for every response and components.
regres.rmse(err)regres.rmse(err)
err |
vector with difference between reference and predicted y-values |
Calculates matrix with slope of predicted and measured values for every response and components.
regres.slope(y.pred, y.ref)regres.slope(y.pred, y.ref)
y.pred |
matrix with predicted values |
y.ref |
vector with reference values |
Add names and attributes to matrix with statistics
regress.addattrs(stat, attrs, name)regress.addattrs(stat, attrs, name)
stat |
matrix with statistics |
attrs |
attributes from error matrix |
name |
name of statistic |
Replicate matric x
repmat(x, nrows, ncols = nrows)repmat(x, nrows, ncols = nrows)
x |
original matrix |
nrows |
number of times replicate matrix row wise |
ncols |
number of times replicate matrix columns wise |
Generic function for selecting number of components for multivariate models (e.g. PCA, PLS, ...)
selectCompNum(obj, ncomp = NULL, ...)selectCompNum(obj, ncomp = NULL, ...)
obj |
a model object |
ncomp |
number of components to select |
... |
other arguments |
Allows user to select optimal number of components for a PCA model
## S3 method for class 'pca' selectCompNum(obj, ncomp, ...)## S3 method for class 'pca' selectCompNum(obj, ncomp, ...)
obj |
PCA model (object of class |
ncomp |
number of components to select |
... |
other parameters if any |
the same model with selected number of components
Allows user to select optimal number of components for PLS model
## S3 method for class 'pls' selectCompNum(obj, ncomp = NULL, selcrit = obj$ncomp.selcrit, ...)## S3 method for class 'pls' selectCompNum(obj, ncomp = NULL, selcrit = obj$ncomp.selcrit, ...)
obj |
PLS model (object of class |
ncomp |
number of components to select |
selcrit |
criterion for selecting optimal number of components ( |
... |
other parameters if any |
The method sets ncomp.selected parameter for the model and return it back. The parameter
points out to the optimal number of components in the model. You can either specify it manually,
as argument ncomp, or use one of the algorithms for automatic selection.
Automatic selection by default based on cross-validation statistics. If no cross-validation results are found in the model, the method will use test set validation results. If they are not available as well, the model will use calibration results and give a warning as in this case the selected number of components will lead to overfitted model.
There are two algorithms for automatic selection you can chose between: either first local minimum of RMSE (‘selcrit="min"') or Wold’s rule ('selcrit="wold"').
The first local minimum criterion finds at which component, A, error of prediction starts raising and selects (A - 1) as the optimal number. The Wold's criterion finds which component A does not make error smaller at least by 5 as the optimal number.
If model is PLS2 model (has several response variables) the method computes optimal number of components for each response and returns the smallest value. For example, if for the first response 2 components give the smallest error and for the second response this number is 3, A = 2 will be selected as a final result.
It is not recommended to use automatic selection for real applications, always investigate your model (via RMSE, Y-variance plot, regression coefficients) to make correct decision.
See examples in help for pls function.
the same model with selected number of components
Calculates selectivity ratio for each component and response variable in the PLS model
selratio(obj, ncomp = obj$ncomp.selected)selratio(obj, ncomp = obj$ncomp.selected)
obj |
a PLS model (object of class |
ncomp |
number of components to count |
array nvar x ncomp x ny with selectivity ratio values
[1] Tarja Rajalahti et al. Chemometrics and Laboratory Systems, 95 (2009), pp. 35-48.
Calculates and set critical limits for residuals of PCA model
setDistanceLimits(obj, ...)setDistanceLimits(obj, ...)
obj |
a model object |
... |
other parameters |
Computes statisticsl limits for orthogonal and score distances (based on calibration set) and assign the calculated values as model properties.
## S3 method for class 'pca' setDistanceLimits( obj, lim.type = obj$lim.type, alpha = obj$alpha, gamma = obj$gamma, ... )## S3 method for class 'pca' setDistanceLimits( obj, lim.type = obj$lim.type, alpha = obj$alpha, gamma = obj$gamma, ... )
obj |
object with PCA model |
lim.type |
type of limits ("jm", "chisq", "ddmoments", "ddrobust") |
alpha |
significance level for detection of extreme objects |
gamma |
significance level for detection of outliers (for data driven approach) |
... |
other arguments |
The limits can be accessed as fields of model objects: $Qlim and $T2lim. Each
is a matrix with four rows and ncomp columns. First row contains critical limits for
extremes, second row - for outliers, third row contains mean value for corresponding distance
(or its robust estimate in case of lim.type = "ddrobust") and last row contains the
degrees of freedom.
Object models with the two fields updated.
Computes statisticsl limits for orthogonal and score distances (x-decomposition) and orthogonal distance (y-decomposition) based on calibration set and assign the calculated values as model properties.
## S3 method for class 'pls' setDistanceLimits( obj, lim.type = obj$lim.type, alpha = obj$alpha, gamma = obj$gamma, ... )## S3 method for class 'pls' setDistanceLimits( obj, lim.type = obj$lim.type, alpha = obj$alpha, gamma = obj$gamma, ... )
obj |
object with PLS model |
lim.type |
type of limits ("jm", "chisq", "ddmoments", "ddrobust") |
alpha |
significance level for detection of extreme objects |
gamma |
significance level for detection of outliers (for data driven approach) |
... |
other arguments |
The limits can be accessed as fields of model objects: $Qlim, $T2lim, and
$Zlim. Each is a matrix with four rows and ncomp columns. In case of limits
for x-decomposition, first row contains critical limits for extremes, second row - for outliers,
third row contains mean value for corresponding distances (or its robust estimate in case of
lim.type = "ddrobust") and last row contains the degrees of freedom.
Object models with the three fields updated.
Calculates and set critical limits for residuals of PCA model
showDistanceLimits(obj, ...)showDistanceLimits(obj, ...)
obj |
a model object |
... |
other parameters |
Show labels on plot
showLabels( ps, show.excluded = FALSE, pos = 3, cex = 0.65, col = "darkgray", force.x.values = NULL, bwd = 0.8 )showLabels( ps, show.excluded = FALSE, pos = 3, cex = 0.65, col = "darkgray", force.x.values = NULL, bwd = 0.8 )
ps |
'plotseries' object |
show.excluded |
logical, are excluded rows also shown on the plot |
pos |
position of the labels relative to the data points |
cex |
size of the labels text |
col |
color of the labels text |
force.x.values |
vector with forced x-values (or NULL) |
bwd |
bar width in case of bar plot |
Predictions
showPredictions(obj, ...)showPredictions(obj, ...)
obj |
a model or result object |
... |
other arguments |
Generic function for showing predicted values for classification or regression model or results
Shows a table with predicted class values for classification result.
## S3 method for class 'classres' showPredictions(obj, ncomp = obj$ncomp.selected, ...)## S3 method for class 'classres' showPredictions(obj, ncomp = obj$ncomp.selected, ...)
obj |
object with classification results (e.g. |
ncomp |
number of components to show the predictions for (NULL - use selected for a model). |
... |
other parameters |
The function prints a matrix where every column is a class and every row is an data object. The matrix has either -1 (does not belong to the class) or +1 (belongs to the class) values.
simca is used to make SIMCA (Soft Independent Modelling of Class Analogies) model for
one-class classification.
simca( x, classname, ncomp = min(nrow(x) - 1, ncol(x) - 1, 20), x.test = NULL, c.test = NULL, cv = NULL, ... )simca( x, classname, ncomp = min(nrow(x) - 1, ncol(x) - 1, 20), x.test = NULL, c.test = NULL, cv = NULL, ... )
x |
a numerical matrix with data values. |
classname |
short text (up to 20 symbols) with class name. |
ncomp |
maximum number of components to calculate. |
x.test |
a numerical matrix with test data. |
c.test |
a vector with classes of test data objects (can be text with names of classes or logical). |
cv |
cross-validation settings (see details). |
... |
any other parameters suitable for |
SIMCA is in fact PCA model with additional functionality, so simca class inherits most
of the functionality of pca class. It uses critical limits calculated for Q and T2
residuals calculated for PCA model for making classification decistion.
Cross-validation settings, cv, can be a number or a list. If cv is a number, it
will be used as a number of segments for random cross-validation (if cv = 1, full
cross-validation will be preformed). If it is a list, the following syntax can be used:
cv = list('rand', nseg, nrep) for random repeated cross-validation with nseg
segments and nrep repetitions or cv = list('ven', nseg) for systematic splits
to nseg segments ('venetian blinds').
Returns an object of simca class with following fields:
classname |
a short text with class name. |
calres |
an object of class |
testres |
an object of class |
cvres |
an object of class |
Fields, inherited from pca class:
ncomp |
number of components included to the model. |
ncomp.selected |
selected (optimal) number of components. |
loadings |
matrix with loading values (nvar x ncomp). |
eigenvals |
vector with eigenvalues for all existent components. |
expvar |
vector with explained variance for each component (in percent). |
cumexpvar |
vector with cumulative explained variance for each component (in percent). |
T2lim |
statistical limit for T2 distance. |
Qlim |
statistical limit for Q residuals. |
info |
information about the model, provided by user when build the model. |
Sergey Kucheryavskiy ([email protected])
S. Wold, M. Sjostrom. "SIMCA: A method for analyzing chemical data in terms of similarity and analogy" in B.R. Kowalski (ed.), Chemometrics Theory and Application, American Chemical Society Symposium Series 52, Wash., D.C., American Chemical Society, p. 243-282.
Methods for simca objects:
print.simca |
shows information about the object. |
summary.simca |
shows summary statistics for the model. |
plot.simca |
makes an overview of SIMCA model with four plots. |
predict.simca |
applies SIMCA model to a new data. |
Methods, inherited from classmodel class:
plotPredictions.classmodel |
shows plot with predicted values. |
plotSensitivity.classmodel |
shows sensitivity plot. |
plotSpecificity.classmodel |
shows specificity plot. |
plotMisclassified.classmodel |
shows misclassified ratio plot. |
Methods, inherited from pca class:
selectCompNum.pca |
set number of optimal components in the model |
plotScores.pca |
shows scores plot. |
plotLoadings.pca |
shows loadings plot. |
plotVariance.pca |
shows explained variance plot. |
plotCumVariance.pca |
shows cumulative explained variance plot. |
plotResiduals.pca |
shows Q vs. T2 residuals plot. |
## make a SIMCA model for Iris setosa class with full cross-validation library(mdatools) data = iris[, 1:4] class = iris[, 5] # take first 20 objects of setosa as calibration set se = data[1:20, ] # make SIMCA model and apply to test set model = simca(se, "setosa", cv = 1) model = selectCompNum(model, 1) # show infromation, summary and plot overview print(model) summary(model) plot(model) # show predictions par(mfrow = c(2, 1)) plotPredictions(model, show.labels = TRUE) plotPredictions(model, res = "cal", ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) # show performance, modelling power and residuals for ncomp = 2 par(mfrow = c(2, 2)) plotSensitivity(model) plotMisclassified(model) plotLoadings(model, comp = c(1, 2), show.labels = TRUE) plotResiduals(model, ncomp = 2) par(mfrow = c(1, 1))## make a SIMCA model for Iris setosa class with full cross-validation library(mdatools) data = iris[, 1:4] class = iris[, 5] # take first 20 objects of setosa as calibration set se = data[1:20, ] # make SIMCA model and apply to test set model = simca(se, "setosa", cv = 1) model = selectCompNum(model, 1) # show infromation, summary and plot overview print(model) summary(model) plot(model) # show predictions par(mfrow = c(2, 1)) plotPredictions(model, show.labels = TRUE) plotPredictions(model, res = "cal", ncomp = 2, show.labels = TRUE) par(mfrow = c(1, 1)) # show performance, modelling power and residuals for ncomp = 2 par(mfrow = c(2, 2)) plotSensitivity(model) plotMisclassified(model) plotLoadings(model, comp = c(1, 2), show.labels = TRUE) plotResiduals(model, ncomp = 2) par(mfrow = c(1, 1))
simcam is used to combine several one-class SIMCA models for multiclass classification.
simcam(models, info = "")simcam(models, info = "")
models |
list with SIMCA models ( |
info |
optional text with information about the the object. |
Besides the possibility for multiclass classification, SIMCAM also provides tools for investigation of relationship among individual models (classes), such as discrimination power of variables, Cooman's plot, model distance, etc.
When create simcam object, the calibration data from all individual SIMCA models is
extracted and combined for making predictions and calculate performance of the multi-class model.
The results are stored in $calres field of the model object.
Returns an object of simcam class with following fields:
models |
a list with provided SIMCA models. |
dispower |
an array with discrimination power of variables for each pair of individual models. |
moddist |
a matrix with distance between each each pair of individual models. |
classnames |
vector with names of individual classes. |
nclasses |
number of classes in the object. |
info |
information provided by user when create the object. |
calres |
an object of class |
Methods for simca objects:
print.simcam |
shows information about the object. |
summary.simcam |
shows summary statistics for the models. |
plot.simcam |
makes an overview of SIMCAM model with two plots. |
predict.simcam |
applies SIMCAM model to a new data. |
plotModelDistance.simcam |
shows plot with distance between individual models. |
plotDiscriminationPower.simcam |
shows plot with discrimination power. |
plotCooman.simcam |
shows Cooman's plot for calibration data. |
Methods, inherited from classmodel class:
plotPredictions.classmodel |
shows plot with predicted values. |
plotSensitivity.classmodel |
shows sensitivity plot. |
plotSpecificity.classmodel |
shows specificity plot. |
plotMisclassified.classmodel |
shows misclassified ratio plot. |
Since SIMCAM objects and results are calculated only for optimal number of components, there is no sense to show such plots like sensitivity or specificity vs. number of components. However they are available as for any other classification model.
## make a multiclass SIMCA model for Iris data library(mdatools) # split data caldata = iris[seq(1, nrow(iris), 2), 1:4] x.se = caldata[1:25, ] x.ve = caldata[26:50, ] x.vi = caldata[51:75, ] x.test = iris[seq(2, nrow(iris), 2), 1:4] c.test = iris[seq(2, nrow(iris), 2), 5] # create individual models m.se = simca(x.se, classname = "setosa") m.se = selectCompNum(m.se, 1) m.vi = simca(x.vi, classname = "virginica") m.vi = selectCompNum(m.vi, 2) m.ve = simca(x.ve, classname = "versicolor") m.ve = selectCompNum(m.ve, 1) # combine models into SIMCAM objects, show statistics and plots m = simcam(list(m.se, m.vi, m.ve), info = "simcam model for Iris data") summary(m) # show predictions and residuals for calibration data par(mfrow = c(2, 2)) plotPredictions(m) plotCooman(m, nc = c(1, 2)) plotModelDistance(m, nc = 1) plotDiscriminationPower(m, nc = c(1, 2)) par(mfrow = c(1, 1)) # apply the SIMCAM model to test set and show statistics and plots res = predict(m, x.test, c.test) summary(res) plotPredictions(res)## make a multiclass SIMCA model for Iris data library(mdatools) # split data caldata = iris[seq(1, nrow(iris), 2), 1:4] x.se = caldata[1:25, ] x.ve = caldata[26:50, ] x.vi = caldata[51:75, ] x.test = iris[seq(2, nrow(iris), 2), 1:4] c.test = iris[seq(2, nrow(iris), 2), 5] # create individual models m.se = simca(x.se, classname = "setosa") m.se = selectCompNum(m.se, 1) m.vi = simca(x.vi, classname = "virginica") m.vi = selectCompNum(m.vi, 2) m.ve = simca(x.ve, classname = "versicolor") m.ve = selectCompNum(m.ve, 1) # combine models into SIMCAM objects, show statistics and plots m = simcam(list(m.se, m.vi, m.ve), info = "simcam model for Iris data") summary(m) # show predictions and residuals for calibration data par(mfrow = c(2, 2)) plotPredictions(m) plotCooman(m, nc = c(1, 2)) plotModelDistance(m, nc = 1) plotDiscriminationPower(m, nc = c(1, 2)) par(mfrow = c(1, 1)) # apply the SIMCAM model to test set and show statistics and plots res = predict(m, x.test, c.test) summary(res) plotPredictions(res)
Calculates discrimination power and distance between individual SIMCA models.
simcam.getPerformanceStats(models, classnames)simcam.getPerformanceStats(models, classnames)
models |
list with SIMCA models (as provided to simcam class) |
classnames |
names of the classes for each model |
simcamres is used to store results for SIMCA multiclass classification.
simcamres(cres, pred.res)simcamres(cres, pred.res)
cres |
results of classification (class |
pred.res |
list with prediction results from each model (pcares objects) |
Class simcamres inherits all properties and methods of class classres, plus
store values necessary to visualise prediction decisions (e.g. Cooman's plot or Residuals plot).
In cotrast to simcares here only values for optimal (selected) number of components in
each individual SIMCA models are presented.
There is no need to create a simcamres object manually, it is created automatically when
make a SIMCAM model (see simcam) or apply the model to a new data (see
predict.simcam). The object can be used to show summary and plots for the results.
Returns an object (list) of class simcamres with the same fields as classres
plus extra fields for Q and T2 values and limits:
c.pred |
predicted class values. |
c.ref |
reference (true) class values if provided. |
T2 |
matrix with T2 values for each object and class. |
Q |
matrix with Q values for each object and class. |
T2lim |
vector with T2 statistical limits for each class. |
Qlim |
vector with Q statistical limits for each class. |
The following fields are available only if reference values were provided.
tp |
number of true positives. |
fp |
nmber of false positives. |
fn |
number of false negatives. |
specificity |
specificity of predictions. |
sensitivity |
sensitivity of predictions. |
Methods for simcamres objects:
print.simcamres |
shows information about the object. |
summary.simcamres |
shows statistics for results of classification. |
plotCooman.simcamres |
makes Cooman's plot. |
Methods, inherited from classres class:
showPredictions.classres |
show table with predicted values. |
plotPredictions.classres |
makes plot with predicted values. |
Check also simcam.
## see examples for simcam method.## see examples for simcam method.
@description
simcares is used to store results for SIMCA one-class classification.
simcares(class.res, pca.res = NULL)simcares(class.res, pca.res = NULL)
class.res |
results of classification (class |
pca.res |
results of PCA decomposition of data (class |
Class simcares inherits all properties and methods of class pcares, and
has additional properties and functions for representing of classification results, inherited
from class classres.
There is no need to create a simcares object manually, it is created automatically when
build a SIMCA model (see simca) or apply the model to a new data (see
predict.simca). The object can be used to show summary and plots for the results.
Returns an object (list) of class simcares with the same fields as pcares
plus extra fields, inherited from classres:
c.pred |
predicted class values (+1 or -1). |
c.ref |
reference (true) class values if provided. |
The following fields are available only if reference values were provided.
tp |
number of true positives. |
fp |
nmber of false positives. |
fn |
number of false negatives. |
specificity |
specificity of predictions. |
sensitivity |
sensitivity of predictions. |
Methods for simcares objects:
print.simcares |
shows information about the object. |
summary.simcares |
shows statistics for results of classification. |
Methods, inherited from classres class:
showPredictions.classres |
show table with predicted values. |
plotPredictions.classres |
predicted classes plot. |
plotSensitivity.classres |
sensitivity plot. |
plotSpecificity.classres |
specificity plot. |
plotPerformance.classres |
performance plot. |
Methods, inherited from ldecomp class:
plotResiduals.ldecomp |
makes Q2 vs. T2 residuals plot. |
plotScores.ldecomp |
makes scores plot. |
plotVariance.ldecomp |
makes explained variance plot. |
plotCumVariance.ldecomp |
makes cumulative explained variance plot. |
## make a SIMCA model for Iris setosa class and show results for calibration set library(mdatools) data = iris[, 1:4] class = iris[, 5] # take first 30 objects of setosa as calibration set se = data[1:30, ] # make SIMCA model and apply to test set model = simca(se, 'Se') model = selectCompNum(model, 1) # show infromation and summary print(model$calres) summary(model$calres) # show plots layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE)) plotPredictions(model$calres, show.labels = TRUE) plotResiduals(model$calres, show.labels = TRUE) plotPerformance(model$calres, show.labels = TRUE, legend.position = 'bottomright') layout(1, 1, 1) # show predictions table showPredictions(model$calres)## make a SIMCA model for Iris setosa class and show results for calibration set library(mdatools) data = iris[, 1:4] class = iris[, 5] # take first 30 objects of setosa as calibration set se = data[1:30, ] # make SIMCA model and apply to test set model = simca(se, 'Se') model = selectCompNum(model, 1) # show infromation and summary print(model$calres) summary(model$calres) # show plots layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE)) plotPredictions(model$calres, show.labels = TRUE) plotResiduals(model$calres, show.labels = TRUE) plotPerformance(model$calres, show.labels = TRUE, legend.position = 'bottomright') layout(1, 1, 1) # show predictions table showPredictions(model$calres)
Simdata contains training and test set with spectra and concentration values of polyaromatic hydrocarbons mixings.
data(simdata)data(simdata)
The data is a list with following fields:
$spectra.c |
a matrix (100x150) with spectral values for the training set. |
$spectra.t |
a matrix (100x150) with spectral values for the test set. |
$conc.c |
a matrix (100x3) with concentration of components for the training set. |
$conc.t |
a matrix (100x3) with concentration of components for the test set. |
$wavelength |
a vector with spectra wavelength in nm. |
This is a simulated data containing UV/Vis spectra of three component (polyaromatic hydrocarbons) mixings - C1, C2 and C3. The spectral range is betwen 210 and 360 nm. The spectra were simulated as a linear combination of pure component spectra plus 5% of random noise. The concentration range is (in moles): C1 [0, 1], C2 [0, 0.5], C3 [0, 0.1].
There are 100 mixings in a training set and 50 mixings in a test set. The data can be used for multivariate regression examples.
Split the excluded part of data
splitExcludedData(data, type)splitExcludedData(data, type)
data |
matrix with hidden data values |
type |
type of plot |
Split dataset to x and y values depending on plot type
splitPlotData(data, type)splitPlotData(data, type)
data |
matrix with data values (visible or hidden) |
type |
type of plot |
Generic summary function for classification results. Prints performance values for the
results.
## S3 method for class 'classres' summary( object, ncomp = object$ncomp.selected, nc = seq_len(object$nclasses), ... )## S3 method for class 'classres' summary( object, ncomp = object$ncomp.selected, nc = seq_len(object$nclasses), ... )
object |
classification results (object of class |
ncomp |
which number of components to make the plot for (use NULL to show results for all available). |
nc |
vector with class numbers to show the summary for. |
... |
other arguments |
Shows statistics and algorithm parameters for iPLS results.
## S3 method for class 'ipls' summary(object, glob.ncomp = object$gm$ncomp.selected, ...)## S3 method for class 'ipls' summary(object, glob.ncomp = object$gm$ncomp.selected, ...)
object |
a iPLS (object of class |
glob.ncomp |
number of components for global PLS model with all intervals. |
... |
other arguments. |
The method shows information on the algorithm parameters as well as a table with selected or excluded interval. The table has the following columns: 'step' showing on which iteration an interval was selected or excluded, 'start and 'end' show variable indices for the interval, 'nComp' is a number of components used in a model, 'RMSE' is RMSECV for the model and 'R2' is coefficient of determination for the same model.
Generic summary function for linear decomposition. Prints statistic about
the decomposition.
## S3 method for class 'ldecomp' summary(object, str = NULL, ...)## S3 method for class 'ldecomp' summary(object, str = NULL, ...)
object |
object of class |
str |
user specified text to show as a description of the object |
... |
other arguments |
Shows some statistics (explained variance, etc) for the case.
## S3 method for class 'mcrals' summary(object, ...)## S3 method for class 'mcrals' summary(object, ...)
object |
|
... |
other arguments |
Shows some statistics (explained variance, etc) for the case.
## S3 method for class 'mcrpure' summary(object, ...)## S3 method for class 'mcrpure' summary(object, ...)
object |
|
... |
other arguments |
Shows some statistics (explained variance, eigenvalues) for the model.
## S3 method for class 'pca' summary(object, ...)## S3 method for class 'pca' summary(object, ...)
object |
a PCA model (object of class |
... |
other arguments |
Shows some statistics (explained variance, eigenvalues) about the results.
## S3 method for class 'pcares' summary(object, ...)## S3 method for class 'pcares' summary(object, ...)
object |
PCA results (object of class |
... |
other arguments |
Shows performance statistics for the model.
## S3 method for class 'pls' summary( object, ncomp = object$ncomp.selected, ny = seq_len(nrow(object$yloadings)), ... )## S3 method for class 'pls' summary( object, ncomp = object$ncomp.selected, ny = seq_len(nrow(object$yloadings)), ... )
object |
a PLS model (object of class |
ncomp |
how many components to count. |
ny |
which y variables to show the summary for (can be a vector) |
... |
other arguments |
Shows some statistics for the model.
## S3 method for class 'plsda' summary( object, ncomp = object$ncomp.selected, nc = seq_len(object$nclasses), ... )## S3 method for class 'plsda' summary( object, ncomp = object$ncomp.selected, nc = seq_len(object$nclasses), ... )
object |
a PLS-DA model (object of class |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
nc |
which class to show the summary for (if NULL, will be shown for all) |
... |
other arguments |
Shows performance statistics for the results.
## S3 method for class 'plsdares' summary(object, nc = seq_len(object$nclasses), ...)## S3 method for class 'plsdares' summary(object, nc = seq_len(object$nclasses), ...)
object |
PLS-DA results (object of class |
nc |
which class to show the summary for (if NULL, will be shown for all) |
... |
other arguments |
Shows performance statistics for the results.
## S3 method for class 'plsres' summary(object, ny = seq_len(object$nresp), ncomp = NULL, ...)## S3 method for class 'plsres' summary(object, ny = seq_len(object$nresp), ncomp = NULL, ...)
object |
PLS results (object of class |
ny |
for which response variable show the summary for |
ncomp |
how many components to use (if NULL - user selected optimal value will be used) |
... |
other arguments |
Shows summary for randomization test results.
## S3 method for class 'randtest' summary(object, ...)## S3 method for class 'randtest' summary(object, ...)
object |
randomization test results (object of class |
... |
other arguments |
Shows estimated coefficients and statistics (if available).
## S3 method for class 'regcoeffs' summary(object, ncomp = 1, ny = 1, alpha = 0.05, ...)## S3 method for class 'regcoeffs' summary(object, ncomp = 1, ny = 1, alpha = 0.05, ...)
object |
object of class |
ncomp |
how many components to use |
ny |
which y variable to show the summary for |
alpha |
significance level for confidence interval (if statistics available) |
... |
other arguments |
Statistcs are shown if Jack-Knifing was used when model is calibrated.
Shows performance statistics for the model.
## S3 method for class 'regmodel' summary( object, ncomp = object$ncomp.selected, ny = seq_len(object$res$cal$nresp), res = object$res, ... )## S3 method for class 'regmodel' summary( object, ncomp = object$ncomp.selected, ny = seq_len(object$res$cal$nresp), res = object$res, ... )
object |
a regression model (object of class |
ncomp |
number of components to show summary for |
ny |
which y variables to show the summary for (can be a vector) |
res |
list of results to show summary for |
... |
other arguments |
Shows performance statistics for the regression results.
## S3 method for class 'regres' summary(object, ncomp = object$ncomp.selected, ny = seq_len(object$nresp), ...)## S3 method for class 'regres' summary(object, ncomp = object$ncomp.selected, ny = seq_len(object$nresp), ...)
object |
regression results (object of class |
ncomp |
model complexity to show the summary for (if NULL - shows for all available values) |
ny |
for which response variable show the summary for (one value or a vector) |
... |
other arguments |
Shows performance statistics for the model.
## S3 method for class 'simca' summary(object, ncomp = object$ncomp.selected, res = object$res, ...)## S3 method for class 'simca' summary(object, ncomp = object$ncomp.selected, res = object$res, ...)
object |
a SIMCA model (object of class |
ncomp |
number of components to show summary for |
res |
list of result objects to show summary for |
... |
other arguments |
Shows performance statistics for the model.
## S3 method for class 'simcam' summary(object, nc = seq_len(object$nclasses), ...)## S3 method for class 'simcam' summary(object, nc = seq_len(object$nclasses), ...)
object |
a SIMCAM model (object of class |
nc |
number of class to show summary for (can be vector) |
... |
other arguments |
Shows performance statistics for the results.
## S3 method for class 'simcamres' summary(object, nc = seq_len(object$nclasses), ...)## S3 method for class 'simcamres' summary(object, nc = seq_len(object$nclasses), ...)
object |
SIMCAM results (object of class |
nc |
number of class to show summary for (can be vector) |
... |
other arguments |
Shows performance statistics for the results.
## S3 method for class 'simcares' summary(object, ...)## S3 method for class 'simcares' summary(object, ...)
object |
SIMCA results (object of class |
... |
other arguments |
Unmix spectral data using pure variables estimated before
unmix.mcrpure(obj, x)unmix.mcrpure(obj, x)
obj |
|
x |
matrix with spectra |
Returns a list with resolved spectra and contributions (matrices).
Calculates VIP (Variable Importance in Projection) scores for predictors for given number of components and response variable.
vipscores(obj, ncomp = obj$ncomp.selected)vipscores(obj, ncomp = obj$ncomp.selected)
obj |
a PLS model (object of class |
ncomp |
number of components to count |
May take some time in case of large number of predictors Returns results as a column-vector,
with all necessary attributes inherited (e.g. xaxis.values, excluded variables, etc.). If you
want to make a plot use for example: mdaplot(mda.t(v), type = "l"), where v is
a vector with computed VIP scores. Or just try plotVIPScores.pls.
matrix nvar x ny with VIP score values (columns correspond to responses).
[1] Il-Gyo Chong, Chi-Hyuck Jun. Chemometrics and Laboratory Systems, 78 (2005), pp. 103-112.