print.jointLPM

This function provides a brief summary of model estimated with the <code>jointLPM</code> function.

Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.

Viviane Philipps

JLPM

Joint Latent Process Models

Tiphaine Saulnier

Cecile Proust-Lima

print.jointLPM function

<dl><dt>x</dt>
<dd>an object inheriting from class <code>jointLPM</code> for a joint latent process model.</dd>
<dt>...</dt>
<dd>further arguments to be passed to or from other methods. They are ignored in this function.</dd></dl>

Arguments

Viviane Philipps, Tiphaine Saulnier and Cecile Proust-Lima

Author

Brief summary of a joint latent process model — print.jointLPM

<dl>

<dt>x</dt>
<dd>an object inheriting from class <code>jointLPM</code> for a joint latent process model.</dd>


<dt>...</dt>
<dd>further arguments to be passed to or from other methods. They are ignored in this function.</dd>

</dl>

print.jointLPM: Brief summary of a joint latent process model

Description

Usage

Value

Arguments

Author