vcov.tramTMB

Variance-covariance matrix of the parameters

Likelihood-based estimation of mixed-effects transformation models
using the Template Model Builder ('TMB', Kristensen et al., 2016)
<doi:10.18637/jss.v070.i05>. The technical details of transformation models
are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood
contributions of exact, randomly censored (left, right, interval) and
truncated observations are supported. The random effects are assumed to be
normally distributed on the scale of the transformation function, the
marginal likelihood is evaluated using the Laplace approximation, and the
gradients are calculated with automatic differentiation (Tamasi & Hothorn,
2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be
defined using 'mgcv'.

Balint Tamasi

tramME

Transformation Models with Mixed Effects

Torsten Hothorn

vcov.tramTMB function

<dl><dt>object</dt>
<dd>A <code>tramTMB</code> object.</dd>
<dt>par</dt>
<dd>An optional vector of parameter values.</dd>
<dt>method</dt>
<dd>Method for calculating the covariance matrix.</dd>
<dt>control</dt>
<dd>Optional named list of controls to be passed to the specific methods.</dd>
<dt>...</dt>
<dd>Optional arguments (ignored)</dd></dl>

Arguments

Variance-covariance matrix of the parameters — vcov.tramTMB

<dl>

<dt>object</dt>
<dd>A <code>tramTMB</code> object.</dd>


<dt>par</dt>
<dd>An optional vector of parameter values.</dd>


<dt>method</dt>
<dd>Method for calculating the covariance matrix.</dd>


<dt>control</dt>
<dd>Optional named list of controls to be passed to the specific methods.</dd>


<dt>...</dt>
<dd>Optional arguments (ignored)</dd>

</dl>

vcov.tramTMB: Variance-covariance matrix of the parameters

Description

Usage

Arguments