bootnComp

if TRUE, a diagnostic plot is drawn for each bootstrap
replicate

plotting


Combined bootstrap and cross validation procedure to find optimal number of
PLS components


manip

Methods for analysis of compositional data including robust
methods, imputation, methods to replace rounded zeros, (robust) outlier
detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data, (robust)
discriminant analysis for compositional data (Fisher rule), robust regression
with compositional predictors and (robust) Anderson-Darling normality tests for
compositional data as well as popular log-ratio transformations (addLR, cenLR,
isomLR, and their inverse transformations). In addition, visualisation and
diagnostic tools are implemented as well as high and low-level plot functions
for the ternary diagram.

bootnComp: Bootstrap to find optimal number of components

Description

Usage

Arguments

Value

Details

See Also

Examples