stepwise.procedure

Joint selection of covariates and random effects in a nonlinear mixed effects model by a stepwise-type
algorithm based on two different versions of BIC for covariate selection and random effects selection
respectively. Selection is made among the covariates as such specified in the SaemixData object.
Only uncorrelated random effects structures are considered.

selection

stepwise

covariate

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

stepwise.procedure function

<dl><dt>saemixObject</dt>
<dd>An object returned by the <code>saemix</code> function</dd>
<dt>covariate.init</dt>
<dd>A matrix specifying the initial covariate structure to be considered in the algorithm.</dd>
<dt>trace</dt>
<dd>If TRUE, a table summarizing the steps of the algorithm is printed. Default "TRUE"</dd></dl>

Arguments

Author

Stepwise procedure for joint selection of covariates and random effects — stepwise.procedure

<dl>

<dt>saemixObject</dt>
<dd>An object returned by the <code>saemix</code> function</dd>


<dt>covariate.init</dt>
<dd>A matrix specifying the initial covariate structure to be considered in the algorithm.</dd>


<dt>trace</dt>
<dd>If TRUE, a table summarizing the steps of the algorithm is printed. Default "TRUE"</dd>

</dl>

Stepwise procedure for joint selection of covariates and random effects

stepwise.procedure: Stepwise procedure for joint selection of covariates and random effects

Description

Usage

Value

Arguments

Author

References