# Multivariate Data Analysis for Chemometrics

## Documentation for package ‘mdatools’ version 0.9.4

- DESCRIPTION file.

## Help Pages

A B C E G I L M P R S

## -- A --

as.matrix.classres | as.matrix method for 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

## -- B --

bars | Show bars on axes

## -- C --

classify.plsda | PLS-DA classification
classres | Results of classification
crossval | Generate sequence of indices for cross-validation
crossval.str | String with description of cross-validation method

## -- E --

erfinv | Inverse error function
errorbars | Show error bars on a plot

## -- G --

getB | Low-dimensional approximation of data matrix X
getCalibrationData | Calibration data
getCalibrationData.pca | Get calibration data
getCalibrationData.simcam | Get calibration data
getClassificationPerformance | Calculation of classification performance parameters
getConfusionMatrix | Confusion matrix for classification results
getConfusionMatrix.classres | Confusion matrix for classification results
getMainTitle | Get main title
getProbabilities | Get class belonging probability
getProbabilities.simca | Probability of class belonging for PCA/SIMCA results
getRegcoeffs | Get regression coefficients
getRegcoeffs.pls | Regression coefficients for PLS model'
getSelectedComponents | Get selected components
getSelectedComponents.classres | Get selected components
getSelectivityRatio | Selectivity ratio
getSelectivityRatio.pls | Selectivity ratio for PLS model
getVIPScores | VIP scores
getVIPScores.pls | VIP scores for PLS model

## -- 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

## -- L --

ldecomp | Linear decomposition of data
ldecomp.getDistances | Residuals distances for linear decomposition
ldecomp.getVariances | Explained variance for linear decomposition
ldecomp.plotLimits | Shows lines with critical limits on residuals plot

## -- M --

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.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 attributed
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.getAxesLim | Calculate axes limits
mdaplot.getColors | Color values for plot elements
mdaplot.plotAxes | Create axes plane
mdaplot.showColorbar | Plot colorbar
mdaplot.showGrid | Plot grid
mdaplot.showLabels | Plot labels Shows labels for data elements (points, bars) on a plot.
mdaplot.showLegend | Plot legend
mdaplot.showLines | Plot lines
mdaplot.showRegressionLine | Regression line for data points
mdaplotg | Plotting function for several sets of objects
mdatools | Package for Multivariate Data Analysis (Chemometrics)

## -- P --

pca | Principal Component Analysis
pca.cal | PCA model calibration
pca.crossval | Cross-validation of a PCA model
pca.mvreplace | Replace missing values in data
pca.nipals | NIPALS based PCA algorithm
pca.run | Runs one of the selected PCA methods
pca.svd | Singular Values Decomposition based PCA algorithm
pcares | Results of PCA decomposition
pellets | Image data
people | People data
pinv | Pseudo-inverse matrix
plot.classres | Plot function for classification results
plot.ipls | Overview plot for iPLS results
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
plotBiplot | Biplot
plotBiplot.pca | PCA biplot
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 for linear decomposition
plotCumVariance.pca | Cumulative explained variance plot for PCA
plotDiscriminationPower | Discrimination power plot
plotDiscriminationPower.simcam | Discrimination power plot for SIMCAM model
plotExtreme | Shows extreme plot for SIMCA model
plotExtreme.simca | Shows extreme plot for SIMCA model
plotHist | Statistic histogram
plotHist.randtest | Histogram plot for randomization test results
plotLoadings | Loadings plot
plotLoadings.pca | Loadings plot for PCA
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 | Modelling distance plot for SIMCAM model
plotModellingPower | Modelling power plot
plotModellingPower.simca | Modelling power plot for SIMCA model
plotModellingPower.simcam | Modelling power plot for SIMCAM model
plotPerformance | Classification performance plot
plotPerformance.classmodel | Performance plot for classification model
plotPerformance.classres | Performance plot for classification results
plotPredictions | Predictions plot
plotPredictions.classmodel | Predictions plot for classification model
plotPredictions.classres | Prediction plot for classification results
plotPredictions.pls | Predictions plot for PLS
plotPredictions.plsres | Predictions plot for PLS results
plotPredictions.regres | Predictions plot for regression results
plotProbabilities | Plot for class belonging probability
plotProbabilities.classres | Plot for class belonging probability
plotRegcoeffs | Regression coefficients plot
plotRegcoeffs.pls | Regression coefficient plot for PLS
plotResiduals | Residuals plot
plotResiduals.ldecomp | Residuals plot for linear decomposition
plotResiduals.pca | Residuals plot for PCA
plotResiduals.pcares | Residuals plot for PCA results
plotResiduals.simcam | Residuals plot for SIMCAM model
plotResiduals.simcamres | Residuals plot for SIMCAM results
plotResiduals.simcares | Residuals plot for SIMCA results
plotRMSE | RMSE plot
plotRMSE.ipls | RMSE development plot
plotRMSE.pls | RMSE plot for PLS
plotRMSE.regres | RMSE plot for regression results
plotScores | Scores plot
plotScores.ldecomp | Scores plot for linear decomposition
plotScores.pca | Scores plot for PCA
plotSelection | Selected intervals plot
plotSelection.ipls | iPLS performance plot
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
plotSpecificity | Specificity plot
plotSpecificity.classmodel | Specificity plot for classification model
plotSpecificity.classres | Specificity plot for classification results
plotVariance | Variance plot
plotVariance.ldecomp | Explained variance plot for linear decomposition
plotVariance.pca | Explained variance plot for PCA
plotVariance.pls | Variance plot for PLS
plotVIPScores | VIP scores plot
plotVIPScores.pls | VIP scores plot for PLS model
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 | X residuals plot for PLS
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 | X loadings plot
plotXYLoadings.pls | XY loadings plot for PLS
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.pls | Y residuals plot for PLS
plotYResiduals.regres | Residuals plot for regression results
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.calculateSelectivityRatio | Selectivity ratio calculation
pls.calculateVIPScores | VIP scores calculation for PLS model
pls.crossval | Cross-validation of a PLS model
pls.run | Runs selected PLS algorithm
pls.simpls | SIMPLS algorithm
plsda | Partial Least Squares Discriminant Analysis
plsda.cal | Calibrate PLS-DA model
plsda.crossval | Cross-validation of a PLS-DA model
plsdares | PLS-DA results
plsres | PLS results
predict.pca | PCA predictions
predict.pls | PLS predictions
predict.plsda | PLS-DA predictions
predict.simca | SIMCA predictions
predict.simcam | SIMCA multiple classes predictions
prep.autoscale | Autoscale values
prep.msc | Multiplicative Scatter Correction transformation
prep.norm | Normalization
prep.savgol | Savytzky-Golay filter
prep.snv | Standard Normal Variate transformation
print.classres | Print information about classification result object
print.ipls | Print method for iPLS
print.ldecomp | Print method for linear decomposition
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.randtest | Print method for randtest object
print.regcoeffs | print method for regression coefficients class
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

## -- R --

randtest | Randomization test for PLS regression
regcoeffs | Regression coefficients
regcoeffs.getStat | Confidence intervals and p-values for regression coeffificents
regres | Regression results
regres.bias | Prediction bias
regres.r2 | Determination coefficient
regres.rmse | RMSE
regres.slope | Slope
reslim.chisq | Calculates critical limits or statistic values for Q-residuals using Chi-squared distribution
reslim.dd | Statistical limits for Q and T2 residuals using Data Driven approach
reslim.hotelling | Calculates critical limits for T2-residuals using Hotelling T2 distribution
reslim.jm | Calculates critical limits for Q-residuals using classic JM approach

## -- 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
setResLimits | Set residual limits for PCA model
setResLimits.pca | Set statistical limits for Q and T2 residuals for PCA model
showPredictions | Predictions
showPredictions.classres | Show predicted class values
simca | SIMCA one-class classification
simca.classify | SIMCA classification
simca.crossval | Cross-validation of a SIMCA model
simcam | SIMCA multiclass classification
simcam.getPerformanceStatistics | Performance statistics for SIMCAM model
simcamres | Results of SIMCA multiclass classification
simcares | Results of SIMCA one-class classification @description 'simcares' is used to store results for SIMCA one-class classification.
simdata | Spectral data of polyaromatic hydrocarbons mixing
summary.classres | Summary statistics about classification result object
summary.ipls | Summary for iPLS results
summary.ldecomp | Summary statistics for linear decomposition
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.randtest | Summary method for randtest object
summary.regcoeffs | Summary method for regcoeffs 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