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