library(help = 'mdatools').Every method is represented by two classes: a model class for keeping all parameters and information about the model, and a class for keeping and visualising results of applying the model to particular data values.
Every model class, e.g. pls, has all needed functionality implemented as class methods, including model calibration, validation (test set and cross-validation), visualisation of the calibration and validation results with various plots and summary statistics.
So far the following modelling methods are implemented:
pca, pcares Principal Component Analysis (PCA).
pls, plsres Partial Least Squares regression (PLS).
simca, simcares Soft Independent Modelling of Class Analogues (SIMCA)
simcam, simcamres SIMCA for multiple classes case (SIMCA)
plsda, plsdares Partial Least Squares Dscriminant Analysis (PLS-DA).
}
Methods for data preprocessing:
prep.autoscale data mean centering and/or standardization.
prep.savgol Savitzky-Golay transformation.
prep.snv Standard normal variate.
prep.msc Multiplicative scatter correction.
}
All plotting methods are based on two functions, mdaplot and mdaplotg. The functions extend the basic functionality of R plots and allow to make automatic legend and color grouping of data points or lines with colorbar legend, automatically adjust axes limits when several data groups are plotted and so on.