transformCatCov

data

The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm

Audrey Lavenu

Marc Lavielle

Belhal Karimi

Maud Delattre

Marilou Chanel

Johannes Ranke

Sofia Kaisaridi

Lucie Fayette

transformCatCov function

<dl><dt>object</dt>
<dd>saemixData object</dd>
<dt>covariate</dt>
<dd>name of the covariate</dd>
<dt>group</dt>
<dd>a vector giving the categories to which the initial values of the covariates should be mapped. If the resulting covariate is binary, it will be stored as 0/1. If it has more than 2 categories, dummy covariates will be created for the analysis.</dd>
<dt>reference</dt>
<dd>the reference group</dd>
<dt>verbose</dt>
<dd>a boolean, prints messages during the execution of the function if TRUE. Defaults to FALSE.</dd></dl>

Arguments

Transform covariates — transformCatCov

<dl>

<dt>object</dt>
<dd>saemixData object</dd>


<dt>covariate</dt>
<dd>name of the covariate</dd>


<dt>group</dt>
<dd>a vector giving the categories to which the initial values of the covariates should be mapped. If the resulting covariate is binary, it will be stored as 0/1. If it has more than 2 categories, dummy covariates will be created for the analysis.</dd>


<dt>reference</dt>
<dd>the reference group</dd>


<dt>verbose</dt>
<dd>a boolean, prints messages during the execution of the function if TRUE. Defaults to FALSE.</dd>

</dl>

Transform covariates

transformCatCov: Transform covariates

Description

Usage

Value

Arguments

Examples