bootnComp

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

manip

Methods for analysis of compositional data including robust
methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace
rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>),
count zeros (<doi:10.1177/1471082X14535524>),
methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (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, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>),
contingency (<doi:10.1080/03610926.2013.824980>)
and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>)
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.

Matthias Templ

robCompositions

Compositional Data Analysis

Karel Hron

Peter Filzmoser

Kamila Facevicova

Petra Kynclova

Jan Walach

Veronika Pintar

Jiajia Chen

Dominika Miksova

Bernhard Meindl

Alessandra Menafoglio

Alessia Di Blasi

Federico Pavone

Nikola Stefelova

Gianluca Zeni

Roman Wiederkehr

bootnComp function

<dl><dt>X</dt>
<dd>predictors as a matrix</dd>
<dt>y</dt>
<dd>response</dd>
<dt>R</dt>
<dd>number of bootstrap replicates</dd>
<dt>plotting</dt>
<dd>if TRUE, a diagnostic plot is drawn for each bootstrap
replicate</dd></dl>

Arguments

Author

Bootstrap to find optimal number of components — bootnComp

<dl>

<dt>X</dt>
<dd>predictors as a matrix</dd>


<dt>y</dt>
<dd>response</dd>


<dt>R</dt>
<dd>number of bootstrap replicates</dd>


<dt>plotting</dt>
<dd>if TRUE, a diagnostic plot is drawn for each bootstrap
replicate</dd>

</dl>

Bootstrap to find optimal number of components

bootnComp: Bootstrap to find optimal number of components

Description

Usage

Value

Arguments

Author

Details

See Also

Examples