transformCatCov

object

covariate

<p>a vector giving the categories to which the initial values of the covariates should be mapped. If the resulting covariate is binary, it will be stored as 0/1. If it has more than 2 categories, dummy covariates will be created for the analysis.</p>

group

reference

<p>a boolean, prints messages during the execution of the function if TRUE. Defaults to FALSE.</p>

verbose

data

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/>).

transformCatCov: Transform covariates

Description

Usage

Arguments

Value

Examples