GC retention indices
Repeated double-cross-validation for robust PLS
Hyptis data set
Plot predictions from repeated DCV of PRM
Plot Lasso coefficients
Plot SEP from repeated DCV
Plots classical and robust Mahalanobis distances
Stepwise regression
Plot residuals from repeated DCV
Repeated Cross Validation for lm
Support Vector Machine evaluation by CV
Plot residuals from repeated DCV of PRM
Phenyl data set
glass types of the glass data
PCA calculation with the NIPALS algorithm
Trimmed standard deviation
Plot results of Ridge regression
Component plot for repeated DCV
NIR data
PCA diagnostics for variables
Plot results from robust PLS
Plot SOM results
Repeated CV for Ridge regression
additive logratio transformation
ash data
Cross-validation for robust PLS
Plot trimmed SEP from repeated DCV of PRM
glass vessels data
Component plot for repeated DCV of PRM
isometric logratio transformation
Repeated double-cross-validation for PLS and PCR
Plot predictions from repeated DCV
kNN evaluation by CV
Data from cereals
Eigenvector algorithm for PLS
Determine the number of PCA components with repeated cross validation
centered logratio transformation
Diagnostics plot for PCA
Neural network evaluation by CV
Classification tree evaluation by CV
Generating random projection directions
compute and plot cluster validity
This package is the R companion to the book "Introduction to Multivariate
Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
CV for Lasso regression
Draws ellipses according to Mahalanobis distances
PLS2 by NIPALS
PLS1 by NIPALS
Robust PLS