impRZalr

A modified EM alr-algorithm for replacing rounded zeros in compositional
data sets.

multivariate

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

impRZalr function

<dl><dt>x</dt>
<dd>compositional data</dd>
<dt>pos</dt>
<dd>position of the rationing variable for alr transformation</dd>
<dt>dl</dt>
<dd>detection limit for each part</dd>
<dt>eps</dt>
<dd>convergence criteria</dd>
<dt>maxit</dt>
<dd>maximum number of iterations</dd>
<dt>bruteforce</dt>
<dd>if TRUE, imputations over dl are set to dl. If FALSE,
truncated (Tobit) regression is applied.</dd>
<dt>method</dt>
<dd>either &#8220;lm&#8221; (default) or &#8220;MM&#8221;</dd>
<dt>step</dt>
<dd>if TRUE, a stepwise (AIC) procedure is applied when fitting
models</dd>
<dt>nComp</dt>
<dd>if determined, it fixes the number of pls components. If
&#8220;boot&#8221;, the number of pls components are estimated using a
bootstraped cross validation approach.</dd>
<dt>R</dt>
<dd>number of bootstrap samples for the determination of pls
components. Only important for method &#8220;pls&#8221;.</dd>
<dt>verbose</dt>
<dd>additional print output during calculations.</dd></dl>

Arguments

Author

alr EM-based imputation of rounded zeros — impRZalr

<dl>

<dt>x</dt>
<dd>compositional data</dd>


<dt>pos</dt>
<dd>position of the rationing variable for alr transformation</dd>


<dt>dl</dt>
<dd>detection limit for each part</dd>


<dt>eps</dt>
<dd>convergence criteria</dd>


<dt>maxit</dt>
<dd>maximum number of iterations</dd>


<dt>bruteforce</dt>
<dd>if TRUE, imputations over dl are set to dl. If FALSE,
truncated (Tobit) regression is applied.</dd>


<dt>method</dt>
<dd>either &#8220;lm&#8221; (default) or &#8220;MM&#8221;</dd>


<dt>step</dt>
<dd>if TRUE, a stepwise (AIC) procedure is applied when fitting
models</dd>


<dt>nComp</dt>
<dd>if determined, it fixes the number of pls components. If
&#8220;boot&#8221;, the number of pls components are estimated using a
bootstraped cross validation approach.</dd>


<dt>R</dt>
<dd>number of bootstrap samples for the determination of pls
components. Only important for method &#8220;pls&#8221;.</dd>


<dt>verbose</dt>
<dd>additional print output during calculations.</dd>

</dl>

alr EM-based imputation of rounded zeros

impRZalr: alr EM-based imputation of rounded zeros

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples