chemometrics

Plots classical and robust Mahalanobis distances

NIR data

GC retention indices

Phenyl data set

Generating random projection directions

additive logratio transformation

ash data

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).

centered logratio transformation

compute and plot cluster validity

Delete intercept from model matrix

Draws ellipses according to Mahalanobis distances

glass vessels data

glass types of the glass data

Hyptis data set

isometric logratio transformation

kNN evaluation by CV

CV for Lasso regression

Plot Lasso coefficients

Repeated Cross Validation for lm

Repeated double-cross-validation for PLS and PCR

PCA calculation with the NIPALS algorithm

Neural network evaluation by CV

Determine the number of PCA components with repeated cross validation

Diagnostics plot for PCA

PCA diagnostics for variables

Plot results of Ridge regression

Plot SEP from repeated DCV

Plot trimmed SEP from repeated DCV of PRM

Component plot for repeated DCV

Component plot for repeated DCV of PRM

Plot predictions from repeated DCV

Plot predictions from repeated DCV of PRM

Plot results from robust PLS

Plot residuals from repeated DCV

Plot residuals from repeated DCV of PRM

Plot SOM results

PLS1 by NIPALS

PLS2 by NIPALS

Eigenvector algorithm for PLS

Robust PLS

Cross-validation for robust PLS

Repeated double-cross-validation for robust PLS

Repeated CV for Ridge regression

Trimmed standard deviation

Stepwise regression

Support Vector Machine evaluation by CV

Classification tree evaluation by CV