| as.matrix method for classification results | as.matrix.classres |
| as.matrix method for ldecomp object | as.matrix.ldecomp |
| as.matrix method for PLS-DA results | as.matrix.plsdares |
| as.matrix method for PLS results | as.matrix.plsres |
| as.matrix method for regression coefficients class | as.matrix.regcoeffs |
| as.matrix method for regression results | as.matrix.regres |
| as.matrix method for SIMCAM results | as.matrix.simcamres |
| as.matrix method for SIMCA classification results | as.matrix.simcares |
| Capitalize text or vector with text values | capitalize |
| Raman spectra of carbonhydrates | carbs |
| Categorize PCA results | categorize |
| Categorize PCA results based on orthogonal and score distances. | categorize.pca |
| Categorize data rows based on PLS results and critical limits for total distance. | categorize.pls |
| Calculates critical limits for distance values using Chi-square distribution | chisq.crit |
| Calculate probabilities for distance values using Chi-square distribution | chisq.prob |
| PLS-DA classification | classify.plsda |
| SIMCA classification | classify.simca |
| Check reference class values and convert it to a factor if necessary | classmodel.processRefValues |
| Results of classification | classres |
| Calculation of classification performance parameters | classres.getPerformance |
| Confidence intervals for regression coefficients | confint.regcoeffs |
| Class for MCR-ALS constraint | constraint |
| Method for angle constraint | constraintAngle |
| Method for closure constraint | constraintClosure |
| Method for non-negativity constraint | constraintNonNegativity |
| Method for normalization constraint | constraintNorm |
| Shows information about all implemented constraints | constraints.list |
| Method for unimodality constraint | constraintUnimod |
| Generate sequence of indices for cross-validation | crossval |
| Define parameters based on 'cv' value | crossval.getParams |
| Cross-validation of a regression model | crossval.regmodel |
| Cross-validation of a SIMCA model | crossval.simca |
| String with description of cross-validation method | crossval.str |
| Calculates critical limits for distance values using Data Driven moments approach | dd.crit |
| Calculates critical limits for distance values using Data Driven moments approach | ddmoments.param |
| Calculates critical limits for distance values using Data Driven robust approach | ddrobust.param |
| Create ellipse on the current plot | ellipse |
| Applies constraint to a dataset | employ.constraint |
| Applies a list with preprocessing methods to a dataset | employ.prep |
| Imitation of fprinf() function | fprintf |
| Calibration data | getCalibrationData |
| Returns matrix with original calibration data | getCalibrationData.pca |
| Get calibration data | getCalibrationData.simcam |
| Compute confidence ellipse for a set of points | getConfidenceEllipse |
| Confusion matrix for classification results | getConfusionMatrix |
| Confusion matrix for classification results | getConfusionMatrix.classres |
| Compute coordinates of a closed convex hull for data points | getConvexHull |
| Create a vector with labels for plot series | getDataLabels |
| Shows a list with implemented constraints | getImplementedConstraints |
| Shows a list with implemented preprocessing methods | getImplementedPrepMethods |
| Create labels as column or row indices | getLabelsAsIndices |
| Create labels from data values | getLabelsAsValues |
| Get main title | getMainTitle |
| Define colors for plot series | getPlotColors |
| Get class belonging probability | getProbabilities |
| Probabilities for residual distances | getProbabilities.pca |
| Probabilities of class belonging for PCA/SIMCA results | getProbabilities.simca |
| Identifies pure variables | getPureVariables |
| Get regression coefficients | getRegcoeffs |
| Regression coefficients for PLS model' | getRegcoeffs.regmodel |
| Return list with valid results | getRes |
| Get selected components | getSelectedComponents |
| Selectivity ratio | getSelectivityRatio |
| Selectivity ratio for PLS model | getSelectivityRatio.pls |
| Compute explained variance for MCR case | getVariance.mcr |
| VIP scores | getVIPScores |
| VIP scores for PLS model | getVIPScores.pls |
| Calculate critical limits for distance values using Hotelling T2 distribution | hotelling.crit |
| Calculate probabilities for distance values and given parameters using Hotelling T2 distribution | hotelling.prob |
| show image data as an image | imshow |
| Variable selection with interval PLS | ipls |
| Runs the backward iPLS algorithm | ipls.backward |
| Runs the forward iPLS algorithm | ipls.forward |
| Calculate critical limits for distance values using Jackson-Mudholkar approach | jm.crit |
| Calculate probabilities for distance values and given parameters using Hotelling T2 distribution | jm.prob |
| Class for storing and visualising linear decomposition of dataset (X = TP' + E) | ldecomp |
| Compute score and residual distances | ldecomp.getDistances |
| Compute coordinates of lines or curves with critical limits | ldecomp.getLimitsCoordinates |
| Compute parameters for critical limits based on calibration results | ldecomp.getLimParams |
| Compute critical limits for orthogonal distances (Q) | ldecomp.getQLimits |
| Compute critical limits for score distances (T2) | ldecomp.getT2Limits |
| Compute explained variance | ldecomp.getVariances |
| Residuals distance plot for a set of ldecomp objects | ldecomp.plotResiduals |
| General class for Multivariate Curve Resolution model | mcr |
| Multivariate curve resolution using Alternating Least Squares | mcrals |
| Identifies pure variables | mcrals.cal |
| Fast combinatorial non-negative least squares | mcrals.fcnnls |
| Non-negative least squares | mcrals.nnls |
| Ordinary least squares | mcrals.ols |
| Multivariate curve resolution based on pure variables | mcrpure |
| A wrapper for cbind() method with proper set of attributes | mda.cbind |
| Convert data matrix to an image | mda.data2im |
| Convert data frame to a matrix | mda.df2mat |
| Exclude/hide columns in a dataset | mda.exclcols |
| Exclude/hide rows in a dataset | mda.exclrows |
| Get data attributes | mda.getattr |
| Get indices of excluded rows or columns | mda.getexclind |
| Convert image to data matrix | mda.im2data |
| Include/unhide the excluded columns | mda.inclcols |
| include/unhide the excluded rows | mda.inclrows |
| Removes excluded (hidden) rows and colmns from data | mda.purge |
| Removes excluded (hidden) colmns from data | mda.purgeCols |
| Removes excluded (hidden) rows from data | mda.purgeRows |
| A wrapper for rbind() method with proper set of attributes | mda.rbind |
| Set data attributes | mda.setattr |
| Remove background pixels from image data | mda.setimbg |
| Wrapper for show() method | mda.show |
| A wrapper for subset() method with proper set of attributed | mda.subset |
| A wrapper for t() method with proper set of attributes | mda.t |
| Plotting function for a single set of objects | mdaplot |
| Check color values | mdaplot.areColors |
| Format vector with numeric values | mdaplot.formatValues |
| Color values for plot elements | mdaplot.getColors |
| Calculate limits for x-axis. | mdaplot.getXAxisLim |
| Prepare xticklabels for plot | mdaplot.getXTickLabels |
| Prepare xticks for plot | mdaplot.getXTicks |
| Calculate limits for y-axis. | mdaplot.getYAxisLim |
| Prepare yticklabels for plot | mdaplot.getYTickLabels |
| Prepare yticks for plot | mdaplot.getYTicks |
| Create axes plane | mdaplot.plotAxes |
| Prepare colors based on palette and opacity value | mdaplot.prepareColors |
| Plot colorbar | mdaplot.showColorbar |
| Plot lines | mdaplot.showLines |
| Plotting function for several plot series | mdaplotg |
| Create and return vector with legend values | mdaplotg.getLegend |
| Compute x-axis limits for mdaplotg | mdaplotg.getXLim |
| Compute y-axis limits for mdaplotg | mdaplotg.getYLim |
| Prepare data for mdaplotg | mdaplotg.prepareData |
| Check mdaplotg parameters and replicate them if necessary | mdaplotg.processParam |
| Show legend for mdaplotg | mdaplotg.showLegend |
| Create line plot with double y-axis | mdaplotyy |
| Package for Multivariate Data Analysis (Chemometrics) | mdatools |
| Principal Component Analysis | pca |
| PCA model calibration | pca.cal |
| Low-dimensional approximation of data matrix X | pca.getB |
| Replace missing values in data | pca.mvreplace |
| NIPALS based PCA algorithm | pca.nipals |
| Runs one of the selected PCA methods | pca.run |
| Singular Values Decomposition based PCA algorithm | pca.svd |
| Results of PCA decomposition | pcares |
| Image data | pellets |
| People data | people |
| Pseudo-inverse matrix | pinv |
| Plot function for classification results | plot.classres |
| Overview plot for iPLS results | plot.ipls |
| Plot summary for MCR model | plot.mcr |
| Model overview plot for PCA | plot.pca |
| Plot method for PCA results object | plot.pcares |
| Model overview plot for PLS | plot.pls |
| Model overview plot for PLS-DA | plot.plsda |
| Overview plot for PLS-DA results | plot.plsdares |
| Overview plot for PLS results | plot.plsres |
| Plot for randomization test results | plot.randtest |
| Regression coefficients plot | plot.regcoeffs |
| Plot method for regression results | plot.regres |
| Model overview plot for SIMCA | plot.simca |
| Model overview plot for SIMCAM | plot.simcam |
| Model overview plot for SIMCAM results | plot.simcamres |
| Show plot series as bars | plotBars |
| Biplot | plotBiplot |
| PCA biplot | plotBiplot.pca |
| Add confidence ellipse for groups of points on scatter plot | plotConfidenceEllipse |
| Plot resolved contributions | plotContributions |
| Show plot with resolved contributions | plotContributions.mcr |
| Add convex hull for groups of points on scatter plot | plotConvexHull |
| Cooman's plot | plotCooman |
| Cooman's plot for SIMCAM model | plotCooman.simcam |
| Cooman's plot for SIMCAM results | plotCooman.simcamres |
| Correlation plot | plotCorr |
| Correlation plot for randomization test results | plotCorr.randtest |
| Variance plot | plotCumVariance |
| Cumulative explained variance plot | plotCumVariance.ldecomp |
| Show plot with cumulative explained variance | plotCumVariance.mcr |
| Cumulative explained variance plot for PCA model | plotCumVariance.pca |
| Show plot series as density plot (using hex binning) | plotDensity |
| Discrimination power plot | plotDiscriminationPower |
| Discrimination power plot for SIMCAM model | plotDiscriminationPower.simcam |
| Degrees of freedom plot for both distances | plotDistDoF |
| Show plot series as error bars | plotErrorbars |
| Shows extreme plot for SIMCA model | plotExtreme |
| Extreme plot | plotExtreme.pca |
| Statistic histogram | plotHist |
| Histogram plot for randomization test results | plotHist.randtest |
| Hotelling ellipse | plotHotellingEllipse |
| Show plot series as set of lines | plotLines |
| Loadings plot | plotLoadings |
| Loadings plot for PCA model | plotLoadings.pca |
| Misclassification ratio plot | plotMisclassified |
| Misclassified ratio plot for classification model | plotMisclassified.classmodel |
| Misclassified ratio plot for classification results | plotMisclassified.classres |
| Model distance plot | plotModelDistance |
| Model distance plot for SIMCAM model | plotModelDistance.simcam |
| Modelling power plot | plotModellingPower |
| Classification performance plot | plotPerformance |
| Performance plot for classification model | plotPerformance.classmodel |
| Performance plot for classification results | plotPerformance.classres |
| Add confidence ellipse or convex hull for group of points | plotPointsShape |
| Predictions plot | plotPredictions |
| Predictions plot for classification model | plotPredictions.classmodel |
| Prediction plot for classification results | plotPredictions.classres |
| Predictions plot for regression model | plotPredictions.regmodel |
| Predictions plot for regression results | plotPredictions.regres |
| Predictions plot for SIMCAM model | plotPredictions.simcam |
| Prediction plot for SIMCAM results | plotPredictions.simcamres |
| Plot for class belonging probability | plotProbabilities |
| Plot for class belonging probability | plotProbabilities.classres |
| Plot purity values | plotPurity |
| Purity values plot | plotPurity.mcrpure |
| Plot purity spectra | plotPuritySpectra |
| Purity spectra plot | plotPuritySpectra.mcrpure |
| Degrees of freedom plot for orthogonal distance (Nh) | plotQDoF |
| Regression coefficients plot | plotRegcoeffs |
| Regression coefficient plot for regression model | plotRegcoeffs.regmodel |
| Add regression line for data points | plotRegressionLine |
| Residuals plot | plotResiduals |
| Residual distance plot | plotResiduals.ldecomp |
| Residuals distance plot for PCA model | plotResiduals.pca |
| Residuals plot for regression results | plotResiduals.regres |
| RMSE plot | plotRMSE |
| RMSE development plot | plotRMSE.ipls |
| RMSE plot for regression model | plotRMSE.regmodel |
| RMSE plot for regression results | plotRMSE.regres |
| Plot for ratio RMSEC/RMSECV vs RMSECV | plotRMSERatio |
| RMSECV/RMSEC ratio plot for regression model | plotRMSERatio.regmodel |
| Show plot series as set of points | plotScatter |
| Scores plot | plotScores |
| Scores plot | plotScores.ldecomp |
| Scores plot for PCA model | plotScores.pca |
| Selected intervals plot | plotSelection |
| iPLS performance plot | plotSelection.ipls |
| Selectivity ratio plot | plotSelectivityRatio |
| Selectivity ratio plot for PLS model | plotSelectivityRatio.pls |
| Sensitivity plot | plotSensitivity |
| Sensitivity plot for classification model | plotSensitivity.classmodel |
| Sensitivity plot for classification results | plotSensitivity.classres |
| Create plot series object based on data, plot type and parameters | plotseries |
| Specificity plot | plotSpecificity |
| Specificity plot for classification model | plotSpecificity.classmodel |
| Specificity plot for classification results | plotSpecificity.classres |
| Plot resolved spectra | plotSpectra |
| Show plot with resolved spectra | plotSpectra.mcr |
| Degrees of freedom plot for score distance (Nh) | plotT2DoF |
| Variance plot | plotVariance |
| Explained variance plot | plotVariance.ldecomp |
| Show plot with explained variance | plotVariance.mcr |
| Explained variance plot for PCA model | plotVariance.pca |
| Variance plot for PLS | plotVariance.pls |
| Explained X variance plot for PLS results | plotVariance.plsres |
| VIP scores plot | plotVIPScores |
| VIP scores plot for PLS model | plotVIPScores.pls |
| Plot for PLS weights | plotWeights |
| X loadings plot for PLS | plotWeights.pls |
| X cumulative variance plot | plotXCumVariance |
| Cumulative explained X variance plot for PLS | plotXCumVariance.pls |
| Explained cumulative X variance plot for PLS results | plotXCumVariance.plsres |
| X loadings plot | plotXLoadings |
| X loadings plot for PLS | plotXLoadings.pls |
| X residuals plot | plotXResiduals |
| Residual distance plot for decomposition of X data | plotXResiduals.pls |
| X residuals plot for PLS results | plotXResiduals.plsres |
| X scores plot | plotXScores |
| X scores plot for PLS | plotXScores.pls |
| X scores plot for PLS results | plotXScores.plsres |
| X variance plot | plotXVariance |
| Explained X variance plot for PLS | plotXVariance.pls |
| Explained X variance plot for PLS results | plotXVariance.plsres |
| X loadings plot | plotXYLoadings |
| XY loadings plot for PLS | plotXYLoadings.pls |
| Plot for XY-residuals | plotXYResiduals |
| Residual XY-distance plot | plotXYResiduals.pls |
| Residual distance plot | plotXYResiduals.plsres |
| XY scores plot | plotXYScores |
| XY scores plot for PLS | plotXYScores.pls |
| XY scores plot for PLS results | plotXYScores.plsres |
| Y cumulative variance plot | plotYCumVariance |
| Cumulative explained Y variance plot for PLS | plotYCumVariance.pls |
| Explained cumulative Y variance plot for PLS results | plotYCumVariance.plsres |
| Y residuals plot | plotYResiduals |
| Y residuals plot for PLS results | plotYResiduals.plsres |
| Y residuals plot for regression model | plotYResiduals.regmodel |
| Y variance plot | plotYVariance |
| Explained Y variance plot for PLS | plotYVariance.pls |
| Explained Y variance plot for PLS results | plotYVariance.plsres |
| Partial Least Squares regression | pls |
| PLS model calibration | pls.cal |
| Compute coordinates of lines or curves with critical limits | pls.getLimitsCoordinates |
| Compute predictions for response values | pls.getpredictions |
| Compute object with decomposition of x-values | pls.getxdecomp |
| Compute matrix with X-scores | pls.getxscores |
| Compute object with decomposition of y-values | pls.getydecomp |
| Compute and orthogonalize matrix with Y-scores | pls.getyscores |
| Compute critical limits for orthogonal distances (Q) | pls.getZLimits |
| Runs selected PLS algorithm | pls.run |
| SIMPLS algorithm | pls.simpls |
| SIMPLS algorithm (old implementation) | pls.simplsold |
| Partial Least Squares Discriminant Analysis | plsda |
| PLS-DA results | plsdares |
| PLS results | plsres |
| MCR ALS predictions | predict.mcrals |
| MCR predictions | predict.mcrpure |
| PCA predictions | predict.pca |
| PLS predictions | predict.pls |
| PLS-DA predictions | predict.plsda |
| SIMCA predictions | predict.simca |
| SIMCA multiple classes predictions | predict.simcam |
| Class for preprocessing object | prep |
| Baseline correction using asymetric least squares | prep.alsbasecorr |
| Autoscale values | prep.autoscale |
| Generic function for preprocessing | prep.generic |
| Shows information about all implemented preprocessing methods. | prep.list |
| Multiplicative Scatter Correction transformation | prep.msc |
| Normalization | prep.norm |
| Kubelka-Munk transformation | prep.ref2km |
| Savytzky-Golay filter | prep.savgol |
| Standard Normal Variate transformation | prep.snv |
| Transformation | prep.transform |
| Variable selection | prep.varsel |
| Take dataset and prepare them for plot | preparePlotData |
| Prepares calibration data | prepCalData |
| Print information about classification result object | print.classres |
| Print method for iPLS | print.ipls |
| Print method for linear decomposition | print.ldecomp |
| Print method for mcrpure object | print.mcrals |
| Print method for mcrpure object | print.mcrpure |
| Print method for PCA model object | print.pca |
| Print method for PCA results object | print.pcares |
| Print method for PLS model object | print.pls |
| Print method for PLS-DA model object | print.plsda |
| Print method for PLS-DA results object | print.plsdares |
| print method for PLS results object | print.plsres |
| Print method for randtest object | print.randtest |
| print method for regression coefficients class | print.regcoeffs |
| Print method for PLS model object | print.regmodel |
| print method for regression results object | print.regres |
| Print method for SIMCA model object | print.simca |
| Print method for SIMCAM model object | print.simcam |
| Print method for SIMCAM results object | print.simcamres |
| Print method for SIMCA results object | print.simcares |
| Randomization test for PLS regression | randtest |
| Regression coefficients | regcoeffs |
| Distribution statistics for regression coeffificents | regcoeffs.getStats |
| Regression results | regres |
| Prediction bias | regres.bias |
| Error of prediction | regres.err |
| Determination coefficient | regres.r2 |
| RMSE | regres.rmse |
| Slope | regres.slope |
| Add names and attributes to matrix with statistics | regress.addattrs |
| Replicate matric x | repmat |
| Select optimal number of components for a model | selectCompNum |
| Select optimal number of components for PCA model | selectCompNum.pca |
| Select optimal number of components for PLS model | selectCompNum.pls |
| Selectivity ratio calculation | selratio |
| Set residual distance limits | setDistanceLimits |
| Compute and set statistical limits for Q and T2 residual distances. | setDistanceLimits.pca |
| Compute and set statistical limits for residual distances. | setDistanceLimits.pls |
| Show residual distance limits | showDistanceLimits |
| Show labels on plot | showLabels |
| Predictions | showPredictions |
| Show predicted class values | showPredictions.classres |
| SIMCA one-class classification | simca |
| SIMCA multiclass classification | simcam |
| Performance statistics for SIMCAM model | simcam.getPerformanceStats |
| Results of SIMCA multiclass classification | simcamres |
| Results of SIMCA one-class classification | simcares |
| Spectral data of polyaromatic hydrocarbons mixing | simdata |
| Split the excluded part of data | splitExcludedData |
| Split dataset to x and y values depending on plot type | splitPlotData |
| Summary statistics about classification result object | summary.classres |
| Summary for iPLS results | summary.ipls |
| Summary statistics for linear decomposition | summary.ldecomp |
| Summary method for mcrals object | summary.mcrals |
| Summary method for mcrpure object | summary.mcrpure |
| Summary method for PCA model object | summary.pca |
| Summary method for PCA results object | summary.pcares |
| Summary method for PLS model object | summary.pls |
| Summary method for PLS-DA model object | summary.plsda |
| Summary method for PLS-DA results object | summary.plsdares |
| summary method for PLS results object | summary.plsres |
| Summary method for randtest object | summary.randtest |
| Summary method for regcoeffs object | summary.regcoeffs |
| Summary method for regression model object | summary.regmodel |
| summary method for regression results object | summary.regres |
| Summary method for SIMCA model object | summary.simca |
| Summary method for SIMCAM model object | summary.simcam |
| Summary method for SIMCAM results object | summary.simcamres |
| Summary method for SIMCA results object | summary.simcares |
| Unmix spectral data using pure variables estimated before | unmix.mcrpure |
| VIP scores for PLS model | vipscores |
