saemix.predict

object

<p>In nonlinear mixed effect models, different types of predictions may be obtained, including individual predictions and population predictions</p>

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. Documentation about 'saemix' is provided by a comprehensive user guide in the inst folder, and a reference concerning the methods  is the paper by Comets, Lavenu and Lavielle (2017, <doi:10.18637/jss.v080.i03>). See 'citation("saemix")' for details.

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm

Audrey Lavenu

Marc Lavielle

Maud Delattre

saemix.predict function

Compute model predictions after an saemix fit — saemix.predict

Compute model predictions after an saemix fit

saemix.predict: Compute model predictions after an saemix fit

Description

Usage

Arguments

Value