summary-methods

summary,ANY-method

Methods for function <code>summary</code>

methods

The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - 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, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (http://group.monolix.org/).

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
algorithm

summary-methods function

<dl class="dl-horizontal"><dt><code>signature(x = "ANY")</code></dt><dd>default summary function ?</dd><dt><code>signature(x = "SaemixData")</code></dt><dd>summary of the data</dd><dt><code>signature(x = "SaemixModel")</code></dt><dd>summary of the model</dd><dt><code>signature(x = "SaemixObject")</code></dt><dd>summary of an SaemixObject</dd></dl>

summary-methods: Methods for Function summary

Description

Arguments

Methods