What mdatools can do?
The package includes classes and functions for analysis, preprocessing, and plotting of 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:
- Centering and scaling
- Savitzky-Golay filter for smoothing and derivatives
- Extended Multiplicative Scatter Correction
- Normalization of spectra to unit area, length, sum, internal standard peak, and standard normal variate (SNV).
- Baseline correction with asymmetric least squares
- Kubelka-Munk transformation
- Element wise transformations (
log,sqrt, power, etc.) - Correction of cosmic spikes
- Combination of preprocessing methods into a preprocessing chain/model
Besides that, some extensions for the basic R plotting functionality have also been implemented and allow you 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.
- Easy-to-use option to apply any user defined color scheme.
- Option to show horizontal and vertical lines on the plot with automatically adjusted axes limits.
- Option to extend plotting functionality by using some attributes for datasets.
See ?mdatools and next chapters for more details.