## What mdatools can do?

The package includes classes and functions for analysis, preprocessing and plotting data and results. So far the following methods for analysis are implemented:

• Principal Component Analysis (PCA)
• Soft Independent Modelling of Class Analogy (SIMCA), including data driven approach (DD-SIMCA)
• Partial Least Squares regression (PLS) with calculation of VIP scores and Selectivity ratio
• Partial Least Squares Discriminant Analysis (PLS-DA)
• Randomization test for PLS regression models
• Interval PLS for variable selection
• Multivariate curve resolution using the purity approach
• Multivariate curve resolution using the constrained alternating least squares
• Procrustes cross-validation for PCA

Preprocessing methods include:

• Mean centering, standardization and autoscaling
• Savitzky-Golay filter for smoothing and derivatives
• Standard Normal Variate for removing scatter and global intensity effect from spectral data
• Mutliplicative Scatter Correction for the same issue
• Normalization of spectra to unit area, unit length, unit sum, unit area under given range.
• Baseline correction with asymmetric least squares
• Kubelka-Munk transformation
• Element wise transformations (log, sqrt, power, etc.)

Besides that, some extensions for the basic R plotting functionality have been also implemented and allow to do the following:

• Color grouping of objects with automatic color legend bar.
• Plot for several groups of objects with automatically calculated axes limits and plot legend.
• Three built-in color schemes — one is based on Colorbrewer and the other two are jet and grayscale.
• Very easy-to-use possibility to apply any user defined color scheme.
• Possibility to show horizontal and vertical lines on the plot with automatically adjusted axes limits.
• Possibility to extend plotting functionality by using some attributes for datasets.

See ?mdatools and next chapters for more details.