This package contains classes and functions for most common methods used in Chemometrics. For a complete list of functions, use library(help = 'mdatools').
The porject is hosted on GitHub, there you can find a short Wiki tutorial (https://github.com/svkucheryavski/mdatools/wiki) explaining most important features of the package.
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). |
randtest |
Randomization test for PLS-regression. |
ipls |
Interval PLS variable. |
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. |
prep.norm |
Spectra normalization. |
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.