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

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.