Multivariate Data Analysis for Chemometrics

Documentation for package ‘mdatools’ version 0.15.0

Help Pages

-- A --

arr2int	Convert a vector of integers to a compact interval string
as.data.frame.ddsimcares	Creates a data frame from DD-SIMCA classification results.
as.matrix.classres	as.matrix method for classification results
as.matrix.ddsimcares	Creates a matrix from DD-SIMCA classification results.
as.matrix.ldecomp	as.matrix method for ldecomp object
as.matrix.plsdares	as.matrix method for PLS-DA results
as.matrix.plsres	as.matrix method for PLS results
as.matrix.regcoeffs	as.matrix method for regression coefficients class
as.matrix.regres	as.matrix method for regression results
as.matrix.simcamres	as.matrix method for SIMCAM results
as.matrix.simcares	as.matrix method for SIMCA classification results
asjson	S3 implementation of asjson() method
asjson.ddsimca	Converts object with DD-SIMCA model to JSON string compatible with web-application.
asjson.pca	Converts object with PCA model to JSON string compatible with web-application.
asjson.pls	Converts object with PLS model to JSON string compatible with web-application.
asvector	S3 implementation of as.vector() method
asvector.pca	Converts object with PCA model to numeric vector compatible with web-application.
asvector.pls	Converts object with PLS model to numeric vector compatible with web-application.

-- C --

capitalize	Capitalize text or vector with text values
carbs	Raman spectra of carbonhydrates
categorize	Categorize PCA results
categorize.pca	Categorize PCA results based on orthogonal and score distances.
categorize.pls	Categorize data rows based on PLS results and critical limits for total distance.
chisq.crit	Calculates critical limits for distance values using Chi-square distribution
chisq.prob	Calculate probabilities for distance values using Chi-square distribution
clamp.dof	Round and clamp degrees of freedom to valid range [1, 250]
classify	Creates classification outcomes for given PCA result objects and distance parameters.
classify.plsda	PLS-DA classification
classify.simca	SIMCA classification
classmodel.processRefValues	Check reference class values and convert it to a factor if necessary
classres	Results of classification
classres.getPerformance	Calculation of classification performance parameters
cleanLabels	Clean text labels from extra elements so they are compatible with JSON
confint.regcoeffs	Confidence intervals for regression coefficients
constraint	Class for MCR-ALS constraint
constraintAngle	Method for angle constraint
constraintClosure	Method for closure constraint
constraintNonNegativity	Method for non-negativity constraint
constraintNorm	Method for normalization constraint
constraints.list	Shows information about all implemented constraints
constraintUnimod	Method for unimodality constraint
create_categories	Create a factor with categories (regular, extreme, outlier)
crossval	Generate sequence of indices for cross-validation
crossval.getParams	Define parameters based on 'cv' value
crossval.regmodel	Cross-validation of a regression model
crossval.simca	Cross-validation of a SIMCA model
crossval.str	String with description of cross-validation method

-- D --

dd.crit	Calculates critical limits for distance values using Data Driven moments approach
ddmoments.param	Calculates critical limits for distance values using Data Driven moments approach
ddrobust.param	Calculates critical limits for distance values using Data Driven robust approach
ddsimca	Data Driven SIMCA
ddsimca.fromjson	Converts JSON string created in mda.tools/ddsimca app to 'ddsimca' object
ddsimca.readJSON	Reads DD-SIMCA model from JSON file made in web-application (mda.tools/ddsimca).
ddsimcares	Results of DD-SIMCA one-class classification

-- E --

ellipse	Create ellipse on the current plot
employ.constraint	Applies constraint to a dataset
employ.prep	Applies a list with preprocessing methods to a dataset
extractArray	Extract numeric array from JSON string
extractBlock	Extracts a JSON subset in main JSON structure
extractPrep	Extracts JSON related to preprocessing model
extractStringArray	Extract string array from JSON string
extractValue	Extract single value from JSON string

-- F --

fprintf

Imitation of fprintf() function

-- G --

genhash	Generates unique pseudo-hash number based on current time and date
getCalibrationData	Calibration data
getCalibrationData.pca	Returns matrix with original calibration data
getCalibrationData.simcam	Get calibration data
getConfidenceEllipse	Compute confidence ellipse for a set of points
getConfusionMatrix	Confusion matrix for classification results
getConfusionMatrix.classres	Confusion matrix for classification results
getConvexHull	Compute coordinates of a closed convex hull for data points
getDataLabels	Create a vector with labels for plot series
getImplementedConstraints	Shows a list with implemented constraints
getImplementedPrepMethods	Shows a list with implemented preprocessing methods
getLabelsAsIndices	Create labels as column or row indices
getLabelsAsValues	Create labels from data values
getMainTitle	Get main title
getPlotColors	Define colors for plot series
getProbabilities	Get class belonging probability
getProbabilities.pca	Probabilities for residual distances
getProbabilities.simca	Probabilities of class belonging for PCA/SIMCA results
getPureVariables	Identifies pure variables
getRegcoeffs	Get regression coefficients
getRegcoeffs.regmodel	Regression coefficients for PLS model
getRes	Return list with valid results
getSelectedComponents	Get selected components
getSelectivityRatio	Selectivity ratio
getSelectivityRatio.pls	Selectivity ratio for PLS model
getVariance.mcr	Compute explained variance for MCR case
getVIPScores	VIP scores
getVIPScores.pls	VIP scores for PLS model

-- H --

hotelling.crit	Calculate critical limits for distance values using Hotelling T2 distribution
hotelling.prob	Calculate probabilities for distance values and given parameters using Hotelling T2 distribution

-- I --

imshow	show image data as an image
ipls	Variable selection with interval PLS
ipls.backward	Runs the backward iPLS algorithm
ipls.forward	Runs the forward iPLS algorithm

-- J --

jm.crit	Calculate critical limits for distance values using Jackson-Mudholkar approach
jm.prob	Calculate probabilities for distance values and given parameters using Hotelling T2 distribution

-- L --

ldecomp	Class for storing and visualising linear decomposition of dataset (X = TP' + E)
ldecomp.getDistances	Compute score and residual distances
ldecomp.getLimitsCoordinates	Compute coordinates of lines or curves with critical limits
ldecomp.getLimParams	Compute parameters for critical limits based on calibration results
ldecomp.getQLimits	Compute critical limits for orthogonal distances (Q)
ldecomp.getT2Limits	Compute critical limits for score distances (T2)
ldecomp.getVariances	Compute explained variance
ldecomp.plotDistances	Distance plot for a set of ldecomp objects
ldecomp.plotResiduals	Residuals distance plot for a set of ldecomp objects (legacy, use 'ldecomp.plotDistances' instead).

-- M --

mcr	General class for Multivariate Curve Resolution model
mcrals	Multivariate curve resolution using Alternating Least Squares
mcrals.cal	MCR-ALS calibration
mcrals.fcnnls	Fast combinatorial non-negative least squares
mcrals.nnls	Non-negative least squares
mcrals.ols	Ordinary least squares
mcrpure	Multivariate curve resolution based on pure variables
mda.cbind	A wrapper for cbind() method with proper set of attributes
mda.data2im	Convert data matrix to an image
mda.df2mat	Convert data frame to a matrix
mda.exclcols	Exclude/hide columns in a dataset
mda.exclrows	Exclude/hide rows in a dataset
mda.getattr	Get data attributes
mda.getexclind	Get indices of excluded rows or columns
mda.im2data	Convert image to data matrix
mda.inclcols	Include/unhide the excluded columns
mda.inclrows	include/unhide the excluded rows
mda.purge	Removes excluded (hidden) rows and columns from data
mda.purgeCols	Removes excluded (hidden) columns from data
mda.purgeRows	Removes excluded (hidden) rows from data
mda.rbind	A wrapper for rbind() method with proper set of attributes
mda.setattr	Set data attributes
mda.setimbg	Remove background pixels from image data
mda.show	Wrapper for show() method
mda.subset	A wrapper for subset() method with proper set of attributes
mda.t	A wrapper for t() method with proper set of attributes
mdaplot	Plotting function for a single set of objects
mdaplot.areColors	Check color values
mdaplot.formatValues	Format vector with numeric values
mdaplot.getColors	Color values for plot elements
mdaplot.getXAxisLim	Calculate limits for x-axis.
mdaplot.getXTickLabels	Prepare xticklabels for plot
mdaplot.getXTicks	Prepare xticks for plot
mdaplot.getYAxisLim	Calculate limits for y-axis.
mdaplot.getYTickLabels	Prepare yticklabels for plot
mdaplot.getYTicks	Prepare yticks for plot
mdaplot.plotAxes	Create axes plane
mdaplot.prepareColors	Prepare colors based on palette and opacity value
mdaplot.showColorbar	Plot colorbar
mdaplot.showLines	Plot lines
mdaplotg	Plotting function for several plot series
mdaplotg.getLegend	Create and return vector with legend values
mdaplotg.getXLim	Compute x-axis limits for mdaplotg
mdaplotg.getYLim	Compute y-axis limits for mdaplotg
mdaplotg.prepareData	Prepare data for mdaplotg
mdaplotg.processParam	Check mdaplotg parameters and replicate them if necessary
mdaplotg.showLegend	Show legend for mdaplotg
mdaplotyy	Create line plot with double y-axis
mdatools	Package for Multivariate Data Analysis (Chemometrics)

-- P --

paste1	Paste values together with no separator and collapse into a single string
pca	Principal Component Analysis
pca.cal	PCA model calibration
pca.fromjson	Converts JSON string created in mda.tools/pca app to 'pca' object
pca.getB	Low-dimensional approximation of data matrix X
pca.mvreplace	Replace missing values in data
pca.nipals	NIPALS based PCA algorithm
pca.readJSON	Reads PCA model from JSON file made in web-application (mda.tools/pca).
pca.run	Runs one of the selected PCA methods
pca.svd	Singular Values Decomposition based PCA algorithm
pca.syncResAliases	Sync calres/testres aliases with the canonical res[["cal"]]/res[["test"]] fields.
pcares	Results of PCA decomposition
pellets	Image data
people	People data
pinv	Pseudo-inverse matrix
plot.classres	Plot function for classification results
plot.ddsimca	Model overview plot for DD-SIMCA
plot.ddsimcares	Plot method for DD-SIMCA results.
plot.ipls	Overview plot for iPLS results
plot.mcr	Plot summary for MCR model
plot.pca	Model overview plot for PCA
plot.pcares	Plot method for PCA results object
plot.pls	Model overview plot for PLS
plot.plsda	Model overview plot for PLS-DA
plot.plsdares	Overview plot for PLS-DA results
plot.plsres	Overview plot for PLS results
plot.randtest	Plot for randomization test results
plot.regcoeffs	Regression coefficients plot
plot.regres	Plot method for regression results
plot.simca	Model overview plot for SIMCA
plot.simcam	Model overview plot for SIMCAM
plot.simcamres	Model overview plot for SIMCAM results
plotAcceptance	Acceptance plot for DDSIMCA model and results (generic function)
plotAcceptance.ddsimca	Acceptance plot for DD-SIMCA model.
plotAcceptance.ddsimcares	Acceptance plot for DD-SIMCA results object.
plotAliens	Aliens plot for DD-SIMCA results (generic function)
plotAliens.ddsimcares	Aliens plot for DD-SIMCA results.
plotBars	Show plot series as bars
plotBiplot	Biplot
plotBiplot.pca	PCA biplot
plotConfidenceEllipse	Add confidence ellipse for groups of points on scatter plot
plotContributions	Plot resolved contributions
plotContributions.mcr	Show plot with resolved contributions
plotConvexHull	Add convex hull for groups of points on scatter plot
plotCooman	Cooman's plot
plotCooman.simcam	Cooman's plot for SIMCAM model
plotCooman.simcamres	Cooman's plot for SIMCAM results
plotCorr	Correlation plot
plotCorr.randtest	Correlation plot for randomization test results
plotCumVariance	Variance plot
plotCumVariance.ldecomp	Cumulative explained variance plot
plotCumVariance.mcr	Show plot with cumulative explained variance
plotCumVariance.pca	Cumulative explained variance plot for PCA model
plotDensity	Show plot series as density plot (using hex binning)
plotDiscriminationPower	Discrimination power plot
plotDiscriminationPower.simcam	Discrimination power plot for SIMCAM model
plotDistances	Distance plot for model and results (generic function)
plotDistances.ddsimca	Show with distance values (score, orthogonal or full) vs object indices for calibration and PV-set results.
plotDistances.ddsimcares	Show with distance values (score, orthogonal or full) vs object indices for DD-SIMCA results.
plotDistances.ldecomp	Distance plot
plotDistances.pca	Distance plot for PCA model
plotDistDoF	Degrees of freedom plot for both distances
plotEigenvalues	Eigenvalues plot
plotEigenvalues.pca	Eigenvalues plot for PCA model
plotErrorbars	Show plot series as error bars
plotExtreme	Shows extreme plot for PCA and DD-SIMCA models
plotExtreme.ddsimca	A shortcut to 'plotExtremes.ddsimca'.
plotExtreme.ddsimcares	Extremes plot (shortcut to 'plotExtremes.ddsimcares').
plotExtreme.pca	A shortcut to ''plotExtremes.pca'.
plotExtremes	Shows extreme plot for PCA and DD-SIMCA models
plotExtremes.ddsimca	Extreme plot
plotExtremes.ddsimcares	Extremes plot.
plotExtremes.pca	Extreme plot
plotFoM	Show plot with figure of merit vs. number of components (generic function).
plotFoM.ddsimcares	Figure of merit plot.
plotFoMs	Show plot with several figures of merit vs. number of components (generic function).
plotFoMs.ddsimcares	Figures of merit plot (multiple FoMs).
plotHist	Statistic histogram
plotHist.randtest	Histogram plot for randomization test results
plotHotellingEllipse	Hotelling ellipse
plotLines	Show plot series as set of lines
plotLoadings	Loadings plot
plotLoadings.pca	Loadings plot for PCA model
plotMisclassified	Misclassification ratio plot
plotMisclassified.classmodel	Misclassified ratio plot for classification model
plotMisclassified.classres	Misclassified ratio plot for classification results
plotModelDistance	Model distance plot
plotModelDistance.simcam	Model distance plot for SIMCAM model
plotModellingPower	Modelling power plot
plotPerformance	Classification performance plot
plotPerformance.classmodel	Performance plot for classification model
plotPerformance.classres	Performance plot for classification results
plotPointsShape	Add confidence ellipse or convex hull for group of points
plotPredictions	Predictions plot
plotPredictions.classmodel	Predictions plot for classification model
plotPredictions.classres	Prediction plot for classification results
plotPredictions.regmodel	Predictions plot for regression model
plotPredictions.regres	Predictions plot for regression results
plotPredictions.simcam	Predictions plot for SIMCAM model
plotPredictions.simcamres	Prediction plot for SIMCAM results
plotProbabilities	Plot for class belonging probability
plotProbabilities.classres	Plot for class belonging probability
plotPurity	Plot purity values
plotPurity.mcrpure	Purity values plot
plotPuritySpectra	Plot purity spectra
plotPuritySpectra.mcrpure	Purity spectra plot
plotQDoF	Degrees of freedom plot for orthogonal distance (Nq)
plotRegcoeffs	Regression coefficients plot
plotRegcoeffs.regmodel	Regression coefficient plot for regression model
plotRegressionLine	Add regression line for data points
plotResiduals	Residuals plot
plotResiduals.ldecomp	Residuals distance plot for a set of ldecomp objects (legacy, use 'plotDistances.ldecomp' instead).
plotResiduals.pca	Residuals distance plot for PCA model (legacy, use 'plotResiduals' instead).
plotResiduals.regres	Residuals plot for regression results
plotRMSE	RMSE plot
plotRMSE.ipls	RMSE development plot
plotRMSE.regmodel	RMSE plot for regression model
plotRMSE.regres	RMSE plot for regression results
plotRMSERatio	Plot for ratio RMSEC/RMSECV vs RMSECV
plotRMSERatio.regmodel	RMSECV/RMSEC ratio plot for regression model
plotScatter	Show plot series as set of points
plotScores	Scores plot
plotScores.ldecomp	Scores plot
plotScores.pca	Scores plot for PCA model
plotSelection	Selected intervals plot
plotSelection.ipls	iPLS performance plot
plotSelectivityArea	Selectivity vs sensitivity plot for DD-SIMCA results (generic function)
plotSelectivityArea.ddsimcares	Selectivity area plot (similar to ROC curve).
plotSelectivityRatio	Selectivity ratio plot
plotSelectivityRatio.pls	Selectivity ratio plot for PLS model
plotSensitivity	Sensitivity plot
plotSensitivity.classmodel	Sensitivity plot for classification model
plotSensitivity.classres	Sensitivity plot for classification results
plotSensitivity.ddsimca	Sensitivity plot.
plotSensitivity.ddsimcares	Sensitivity plot.
plotseries	Create plot series object based on data, plot type and parameters
plotSpecificity	Specificity plot
plotSpecificity.classmodel	Specificity plot for classification model
plotSpecificity.classres	Specificity plot for classification results
plotSpectra	Plot resolved spectra
plotSpectra.mcr	Show plot with resolved spectra
plotT2DoF	Degrees of freedom plot for score distance (Nh)
plotVariance	Variance plot
plotVariance.ldecomp	Explained variance plot
plotVariance.mcr	Show plot with explained variance
plotVariance.pca	Explained variance plot for PCA model
plotVariance.pls	Variance plot for PLS
plotVariance.plsres	Explained X variance plot for PLS results
plotVIPScores	VIP scores plot
plotVIPScores.pls	VIP scores plot for PLS model
plotWeights	Plot for PLS weights
plotWeights.pls	Weights plot for PLS
plotXCumVariance	X cumulative variance plot
plotXCumVariance.pls	Cumulative explained X variance plot for PLS
plotXCumVariance.plsres	Explained cumulative X variance plot for PLS results
plotXLoadings	X loadings plot
plotXLoadings.pls	X loadings plot for PLS
plotXResiduals	X residuals plot
plotXResiduals.pls	Residual distance plot for decomposition of X data
plotXResiduals.plsres	X residuals plot for PLS results
plotXScores	X scores plot
plotXScores.pls	X scores plot for PLS
plotXScores.plsres	X scores plot for PLS results
plotXVariance	X variance plot
plotXVariance.pls	Explained X variance plot for PLS
plotXVariance.plsres	Explained X variance plot for PLS results
plotXYLoadings	XY loadings plot
plotXYLoadings.pls	XY loadings plot for PLS
plotXYResiduals	Plot for XY-residuals
plotXYResiduals.pls	Residual XY-distance plot
plotXYResiduals.plsres	Residual distance plot
plotXYScores	XY scores plot
plotXYScores.pls	XY scores plot for PLS
plotXYScores.plsres	XY scores plot for PLS results
plotYCumVariance	Y cumulative variance plot
plotYCumVariance.pls	Cumulative explained Y variance plot for PLS
plotYCumVariance.plsres	Explained cumulative Y variance plot for PLS results
plotYResiduals	Y residuals plot
plotYResiduals.plsres	Y residuals plot for PLS results
plotYResiduals.regmodel	Y residuals plot for regression model
plotYVariance	Y variance plot
plotYVariance.pls	Explained Y variance plot for PLS
plotYVariance.plsres	Explained Y variance plot for PLS results
pls	Partial Least Squares regression
pls.cal	PLS model calibration
pls.fromjson	Converts JSON string created in mda.tools/pls app to 'pls' object
pls.getLimitsCoordinates	Compute coordinates of lines or curves with critical limits
pls.getpredictions	Compute predictions for response values
pls.getxdecomp	Compute object with decomposition of x-values
pls.getxscores	Compute matrix with X-scores
pls.getydecomp	Compute object with decomposition of y-values
pls.getyscores	Compute and orthogonalize matrix with Y-scores
pls.getZLimits	Compute critical limits for orthogonal distances (Q)
pls.readJSON	Reads PLS model from JSON file made in web-application (mda.tools/pls).
pls.run	Runs selected PLS algorithm
pls.simpls	SIMPLS algorithm
pls.syncResAliases	Sync result aliases (calres, cvres, testres) from canonical res list
plsda	Partial Least Squares Discriminant Analysis
plsdares	PLS-DA results
plsres	PLS results
predict.ddsimca	DD-SIMCA predictions
predict.mcrals	MCR ALS predictions
predict.mcrpure	MCR predictions
predict.pca	PCA predictions
predict.pls	PLS predictions
predict.plsda	PLS-DA predictions
predict.simca	SIMCA predictions
predict.simcam	SIMCA multiple classes predictions
prep	Class for preprocessing object/item.
prep.alsbasecorr	Baseline correction using asymmetric least squares
prep.alsbasecorr.asjson	Converts preprocessing item from 'prep.alsbasecorr' method to JSON elements
prep.alsbasecorr.fromjson	Converts JSON elements to preprocessing item for 'prep.alsbasecorr' method
prep.apply	Applies a list with preprocessing methods to a dataset
prep.asjson	Converts preprocessing model to JSON elements.
prep.autoscale	Autoscale values
prep.center	Centering data columns.
prep.center.asjson	Converts preprocessing item from 'prep.center' method to JSON elements
prep.center.fromjson	Converts JSON elements to preprocessing item for 'prep.center' method
prep.center.params	Precomputes parameters for centering
prep.emsc	Applies Extended Multiplicative Scatter Correction to data rows
prep.emsc.asjson	Converts preprocessing item from 'prep.emsc' method to JSON elements
prep.emsc.fromjson	Converts JSON elements to preprocessing item for 'prep.emsc' method
prep.emsc.params	Precomputes parameters for EMSC
prep.fit	Fits preprocessing model
prep.fromjson	Converts JSON string to preprocessing model
prep.generic	Generic function for preprocessing
prep.list	Shows information about all implemented preprocessing methods.
prep.msc	Multiplicative Scatter Correction transformation
prep.norm	Normalization
prep.norm.asjson	Converts preprocessing item from 'prep.norm' method to JSON elements
prep.norm.fromjson	Converts JSON elements to preprocessing item for 'prep.norm' method
prep.norm.params	Precomputes parameters for normalization
prep.ref2km	Kubelka-Munk transformation
prep.savgol	Savitzky-Golay filter
prep.savgol.asjson	Converts preprocessing item from 'prep.savgol' method to JSON elements
prep.savgol.fromjson	Converts JSON elements to preprocessing item for 'prep.savgol' method
prep.savgol.params	Precomputes parameters for Savitzky-Golay
prep.scale	Scaling data columns.
prep.scale.asjson	Converts preprocessing item from 'prep.scale' method to JSON elements
prep.scale.fromjson	Converts JSON elements to preprocessing item for 'prep.scale' method
prep.scale.params	Precomputes parameters for scaling
prep.snv	Standard Normal Variate transformation
prep.spikes	Remove spikes from Raman spectra
prep.spikes.asjson	Converts preprocessing item from 'prep.spikes' method to JSON elements
prep.spikes.fromjson	Converts JSON elements to preprocessing item for 'prep.spikes' method
prep.transform	Transformation
prep.varsel	Variable selection
prep.varsel.asjson	Converts preprocessing item from 'prep.varsel' method to JSON elements
prep.varsel.fromjson	Converts JSON elements to preprocessing item for 'prep.varsel' method
preparePlotData	Take dataset and prepare them for plot
prepCalData	Prepares calibration data
print.classres	Print information about classification result object
print.ddsimca	Print method for DD-SIMCA model object
print.ddsimcares	Print method for DD-SIMCA results
print.ipls	Print method for iPLS
print.ldecomp	Print method for linear decomposition
print.mcrals	Print method for mcrals object
print.mcrpure	Print method for mcrpure object
print.pca	Print method for PCA model object
print.pcares	Print method for PCA results object
print.pls	Print method for PLS model object
print.plsda	Print method for PLS-DA model object
print.plsdares	Print method for PLS-DA results object
print.plsres	print method for PLS results object
print.prepmodel	Print the information about methods in the preprocessing model.
print.randtest	Print method for randtest object
print.regcoeffs	print method for regression coefficients class
print.regmodel	Print method for regression model object
print.regres	print method for regression results object
print.simca	Print method for SIMCA model object
print.simcam	Print method for SIMCAM model object
print.simcamres	Print method for SIMCAM results object
print.simcares	Print method for SIMCA results object
processLimType	Make correction to limit types names.
processMembers	Computes classification outcomes for target class members.
processStrangers	Computes classification outcomes for members of non-target classes.

-- R --

randtest	Randomization test for PLS regression
readJSON	Reads models from JSON file made in web-application (mda.tools).
regcoeffs	Regression coefficients
regcoeffs.getStats	Distribution statistics for regression coefficients
regres	Regression results
regres.bias	Prediction bias
regres.err	Error of prediction
regres.r2	Determination coefficient
regres.rmse	RMSE
regres.slope	Slope
regress.addattrs	Add names and attributes to matrix with statistics
repmat	Replicate matrix x

-- S --

selectCompNum	Select optimal number of components for a model
selectCompNum.pca	Select optimal number of components for PCA model
selectCompNum.pls	Select optimal number of components for PLS model
selratio	Selectivity ratio calculation
setDistanceLimits	Set residual distance limits
setDistanceLimits.pca	Compute and set statistical limits for Q and T2 residual distances.
setDistanceLimits.pls	Compute and set statistical limits for residual distances.
setParams	Set model parameters other than number of components (generic function)
setParams.ddsimca	Set default parameters for the DD-SIMCA model.
showDistanceLimits	Show residual distance limits
showLabels	Show labels on plot
showPredictions	Predictions
showPredictions.classres	Show predicted class values
simca	SIMCA one-class classification
simcam	SIMCA multiclass classification
simcam.getPerformanceStats	Performance statistics for SIMCAM model
simcamres	Results of SIMCA multiclass classification
simcares	Results of SIMCA one-class classification
simdata	Spectral data of polyaromatic hydrocarbons mixing
splitExcludedData	Split the excluded part of data
splitPlotData	Split dataset to x and y values depending on plot type
summary.classres	Summary statistics about classification result object
summary.ddsimca	Summary method for DD-SIMCA model object
summary.ddsimcares	Summary method for DD-SIMCA results.
summary.ipls	Summary for iPLS results
summary.ldecomp	Summary statistics for linear decomposition
summary.mcrals	Summary method for mcrals object
summary.mcrpure	Summary method for mcrpure object
summary.pca	Summary method for PCA model object
summary.pcares	Summary method for PCA results object
summary.pls	Summary method for PLS model object
summary.plsda	Summary method for PLS-DA model object
summary.plsdares	Summary method for PLS-DA results object
summary.plsres	summary method for PLS results object
summary.prepmodel	Show summary of the preprocessing model.
summary.randtest	Summary method for randtest object
summary.regcoeffs	Summary method for regcoeffs object
summary.regmodel	Summary method for regression model object
summary.regres	summary method for regression results object
summary.simca	Summary method for SIMCA model object
summary.simcam	Summary method for SIMCAM model object
summary.simcamres	Summary method for SIMCAM results object
summary.simcares	Summary method for SIMCA results object

-- U --

unmix.mcrpure

Unmix spectral data using pure variables estimated before

-- V --

vipscores

VIP scores for PLS model

-- W --

writeCSV	Method to write outcomes of any result object to CSV file
writeCSV.ddsimcares	Save DD-SIMCA results to CSV file
writeCSV.pcares	Save PCA results to CSV file
writeCSV.plsres	Save PLS results to CSV file
writeJSON	Save model as JSON file
writeJSON.ddsimca	Saves DD-SIMCA model as JSON file compatible with web-application (https://mda.tools/ddsimca).
writeJSON.pca	Saves PCA model as JSON file compatible with web-application (https://mda.tools/pca).
writeJSON.pls	Saves PLS model as JSON file compatible with web-application (https://mda.tools/pls).
writeJSON.prepmodel	Saves preprocessing model to JSON file which can be loaded to web-application (mda.tools/prep).