initial.long

Initialisation of Longitudinal Submodel

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

Léonie Courcoul

FlexVarJM

Estimate Joint Models with Subject-Specific Variance

Antoine Barbieri

Hélène Jacqmin-Gadda

initial.long function

<dl><dt>formFixed</dt>
<dd>A formula which indicates the fixed effects for the longitudinal submodel</dd>
<dt>formRandom</dt>
<dd>A formula which indicates the random effects for the longitudinal submodel</dd>
<dt>idVar</dt>
<dd>A character, indicates the name of the group variable</dd>
<dt>data.long1</dt>
<dd>A dataframe with the longitudinal data</dd>
<dt>ncX</dt>
<dd>An integer, the number of columns of matrix X, ie, the number of fixed effects</dd>
<dt>nproc</dt>
<dd>An integer, the number of cores for parallel computation</dd></dl>

Arguments

Initialisation of Longitudinal Submodel — initial.long

<dl>

<dt>formFixed</dt>
<dd>A formula which indicates the fixed effects for the longitudinal submodel</dd>


<dt>formRandom</dt>
<dd>A formula which indicates the random effects for the longitudinal submodel</dd>


<dt>idVar</dt>
<dd>A character, indicates the name of the group variable</dd>


<dt>data.long1</dt>
<dd>A dataframe with the longitudinal data</dd>


<dt>ncX</dt>
<dd>An integer, the number of columns of matrix X, ie, the number of fixed effects</dd>


<dt>nproc</dt>
<dd>An integer, the number of cores for parallel computation</dd>

</dl>

initial.long: Initialisation of Longitudinal Submodel

Description

Usage

Value

Arguments