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