Summary method for class "galamm".

## Usage

```
# S3 method for galamm
summary(object, ...)
```

## Arguments

- object
An object of class

`galamm`

returned from`galamm`

.- ...
Further arguments passed on to other methods. Currently not used.

## Value

A list of summary statistics of the fitted model of class
`summary.galamm`

, containing the following elements:

`AICtab`

a table of model fit measures, returned by`llikAIC`

.`call`

the matched call used when fitting the model.`fixef`

a matrix with fixed effect estimated, returned by`fixef`

.`gam`

List containing information about smooth terms in the model. If no smooth terms are contained in the model, then it is a list of length zero.`model`

a list with various elements related to the model setup and fit. See`?galamm`

for details.`parameters`

A list object with model parameters and related information. See`?galamm`

for details.`Lambda`

An object containing the estimated factor loadings. Returned from`factor_loadings.galamm`

. If there are no estimated factor loadings, then this object is`NULL`

.`random_effects`

a list containing the random effects. See`?galamm`

for details.`VarCorr`

An object of class`VarCorr.galamm`

, returned from`VarCorr.galamm`

.`weights`

An object containing information about estimated variance functions, when there are heteroscedastic residuals. Otherwise the object is`NULL`

.

## See also

`print.summary.galamm()`

for the print method and `summary()`

for
the generic.

Other summary functions:
`anova.galamm()`

,
`plot.galamm()`

,
`plot_smooth.galamm()`

,
`print.summary.galamm()`

## Author

Some of the code for producing summary information has been derived
from the summary methods of `mgcv`

(author: Simon Wood) and
`lme4`

(Bates et al. 2015)
(authors: Douglas M. Bates, Martin Maechler, Ben Bolker, and Steve Walker).

## Examples

```
# Linear mixed model with heteroscedastic residuals
mod <- galamm(
formula = y ~ x + (1 | id),
weights = ~ (1 | item),
data = hsced
)
summary(mod)
#> GALAMM fit by maximum marginal likelihood.
#> Formula: y ~ x + (1 | id)
#> Data: hsced
#> Weights: ~(1 | item)
#>
#> AIC BIC logLik deviance df.resid
#> 4126.3 4151.7 -2058.1 4116.3 1195
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -5.6545 -0.7105 0.0286 0.6827 4.3261
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> id (Intercept) 0.9880 0.9940
#> Residual 0.9597 0.9796
#> Number of obs: 1200, groups: id, 200
#>
#> Variance function:
#> 1 2
#> 1.000 1.995
#>
#> Fixed effects:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.1289 0.0992 1.299 1.938e-01
#> x 0.7062 0.1213 5.822 5.819e-09
#>
#>
```