gradLMM

Gradient of the log-likelihood of a linear mixed model with random intercept

This algorithm provides a numerical solution to the
problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.

Viviane Philipps

marqLevAlg

A Parallelized General-Purpose Optimization Based on
Marquardt-Levenberg Algorithm

gradLMM function

<dl><dt>b</dt>
<dd>numeric vector specifying the parameter's values in the
following order : first the fixed effects and then the standard
deviation of the random intercept and of the independent error</dd>
<dt>Y</dt>
<dd>numeric vector including the dependent outcome vector ordered by subject</dd>
<dt>X</dt>
<dd>numeric matrix including the covariates</dd>
<dt>ni</dt>
<dd>interger vector giving the number of repeated measures for each subject</dd></dl>

Arguments

Gradient of the log-likelihood of a linear mixed model with random intercept — gradLMM

<dl>

<dt>b</dt>
<dd>numeric vector specifying the parameter's values in the
following order : first the fixed effects and then the standard
deviation of the random intercept and of the independent error</dd>


<dt>Y</dt>
<dd>numeric vector including the dependent outcome vector ordered by subject</dd>


<dt>X</dt>
<dd>numeric matrix including the covariates</dd>


<dt>ni</dt>
<dd>interger vector giving the number of repeated measures for each subject</dd>

</dl>

gradLMM: Gradient of the log-likelihood of a linear mixed model with random intercept

Description

Usage

Value

Arguments