subset

subset.SaemixData

subset-methods

Return an SaemixData object containing the subset of data which meets conditions.

methods

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

subset function

<dl><dt>x</dt>
<dd>saemixData object</dd>
<dt>subset</dt>
<dd>logical expression indicating elements or rows to keep: missing values are taken as false</dd>
<dt>...</dt>
<dd>additional parameters (ignored)</dd></dl>

Arguments

Data subsetting — subset

<dl>

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


<dt>subset</dt>
<dd>logical expression indicating elements or rows to keep: missing values are taken as false</dd>


<dt>...</dt>
<dd>additional parameters (ignored)</dd>

</dl>

Data subsetting

subset: Data subsetting

Description

Usage

Value

Arguments

Examples