# Multivariate Data Analysis for Chemometrics

## Documentation for package ‘mdatools’ version 0.16.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A C D E F G H I J L M P R S U V W

## -- 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 --

data3w | Simulated 3-way data
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.parafac | DD-SIMCA with PARAFAC decomposition for 3-way data
ddsimca.readJSON | Reads DD-SIMCA model from JSON file made in web-application (mda.tools/ddsimca).
ddsimca.tucker | DD-SIMCA with Tucker3 decomposition for 3-way data
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
plotDistDoF.pca | 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
plotFactors | Plot factors for a 3-way decomposition model
plotFactors.ddsimca3w | Plot factor (loading) curves for a 3-way model.
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)
plotQDoF.pca | 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)
plotT2DoF.pca | 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.ddsimca3w | Predictions for a 3-way DD-SIMCA model
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.
setParams.ddsimca3w | Update significance levels for a 3-way 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).