createSaemixObject

createSaemixObject.empty

createSaemixObject.initial

model

data

<p>a list of options (if empty, will be set to the default values by <code>saemixControl</code>)</p>

control

<p>Create saemix objects either with empty results or with parameters set by the user. 
This is an internal function used as a preliminary step to obtain predictions for new data
and is not intended to be used directly.</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

createSaemixObject function

Create saemix objects with only data filled in — createSaemixObject

Create saemix objects with only data filled in

createSaemixObject: Create saemix objects with only data filled in

Description

Usage

Arguments

Value

Details

Examples