summary.Model

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

summary.Model function

<dl><dt>object</dt>
<dd>An `Model` object.</dd>
<dt>max_n</dt>
<dd>Integer. The maximum number of rows to print.</dd>
<dt>...</dt>
<dd>Further arguments passed from other methods</dd></dl>

Arguments

Summarizes a `Model` object — summary.Model

<dl>

<dt>object</dt>
<dd>An `Model` object.</dd>


<dt>max_n</dt>
<dd>Integer. The maximum number of rows to print.</dd>


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

</dl>

summary.Model: Summarizes a `Model` object

Description

Usage

Value

Arguments