logLik.mcml

Extracts the final log-likelihood value from an mcml object returned from call of `MCML` or `LA` in the Model class. The fitting algorithm estimates
the fixed effects, random effects, and covariance parameters all separately. The log-likelihood is separable in the fixed and covariance parameters, so one can return 
the log-likelihood for either component, or the overall log-likelihood.

Specification, analysis, simulation, and fitting of generalised linear mixed models.
Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models,
non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily,
robust and bias-corrected standard error estimation, power calculation, data simulation, and more.
See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.

Sam Watson

glmmrBase

Generalised Linear Mixed Models in R

logLik.mcml function

<dl><dt>object</dt>
<dd>An `mcml` model fit.</dd>
<dt>fixed</dt>
<dd>Logical whether to include the log-likelihood value from the fixed effects.</dd>
<dt>covariance</dt>
<dd>Logical whether to include the log-likelihood value from the covariance parameters.</dd>
<dt>...</dt>
<dd>Further arguments passed from other methods</dd></dl>

Arguments

Extracts the log-likelihood from an mcml object — logLik.mcml

<dl>

<dt>object</dt>
<dd>An `mcml` model fit.</dd>


<dt>fixed</dt>
<dd>Logical whether to include the log-likelihood value from the fixed effects.</dd>


<dt>covariance</dt>
<dd>Logical whether to include the log-likelihood value from the covariance parameters.</dd>


<dt>...</dt>
<dd>Further arguments passed from other methods</dd>

</dl>

logLik.mcml: Extracts the log-likelihood from an mcml object

Description

Usage

Value

Arguments