dataGen.case

dataGen.NP

dataGen.Par

sampDist.Par

sampDist.NP

sampDist.NPcond

These functions create bootstrapped datasets using the bootstrap methods described in <code>saemix.bootstrap</code>

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

dataGen.case function

<dl><dt>saemixObject</dt>
<dd>an object returned by the <code>saemix</code> function</dd>
<dt>nsamp</dt>
<dd>number of samples from the conditional distribution (for method="conditional")</dd>
<dt>eta.sampc</dt>
<dd>if available, samples from the conditional distribution (otherwise, they are obtained within the function)</dd>
<dt>conditional</dt>
<dd>if TRUE, sample from the conditional distribution, if FALSE, sample within the
empirical Bayes estimates (EBE) as in the traditional non-parametric residual bootstrap</dd></dl>

Arguments

Bootstrap datasets — dataGen.case

<dl>

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


<dt>nsamp</dt>
<dd>number of samples from the conditional distribution (for method="conditional")</dd>


<dt>eta.sampc</dt>
<dd>if available, samples from the conditional distribution (otherwise, they are obtained within the function)</dd>


<dt>conditional</dt>
<dd>if TRUE, sample from the conditional distribution, if FALSE, sample within the
empirical Bayes estimates (EBE) as in the traditional non-parametric residual bootstrap</dd>

</dl>

Bootstrap datasets

dataGen.case: Bootstrap datasets

Description

Usage

Arguments