residuals.Model

Return the residuals from a `Model` object. This function is a wrapper for `Model$residuals()`.
Generates one of several types of residual for the model. If conditional = TRUE then 
the residuals include the random effects, otherwise only the fixed effects are included. For type,
there are raw, pearson, and standardized residuals. For conditional residuals a matrix is returned 
with each column corresponding to a sample of the random effects.

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

residuals.Model function

<dl><dt>object</dt>
<dd>A `Model` object.</dd>
<dt>type</dt>
<dd>Either "standardized", "raw" or "pearson"</dd>
<dt>conditional</dt>
<dd>Logical indicating whether to condition on the random effects (TRUE) or not (FALSE)</dd>
<dt>...</dt>
<dd>Further arguments passed from other methods</dd></dl>

Arguments

Extract residuals from a `Model` object — residuals.Model

<dl>

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


<dt>type</dt>
<dd>Either "standardized", "raw" or "pearson"</dd>


<dt>conditional</dt>
<dd>Logical indicating whether to condition on the random effects (TRUE) or not (FALSE)</dd>


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

</dl>

residuals.Model: Extract residuals from a `Model` object

Description

Usage

Value

Arguments