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