Summarizing GALAMM fits

Summary method for class "galamm".

Usage

# S3 method for class '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
#> 
#>