Predictions are given at the population level, i.e., with random
effects set to zero. For fitted models including random effects, see
`fitted.galamm`

. For mixed response models, only predictions on
the scale of the linear predictors is supported.

## Arguments

- object
An object of class

`galamm`

returned from`galamm`

.- newdata
Data from for which to evaluate predictions, in a

`data.frame`

. Defaults to "NULL", which means that the predictions are evaluate at the data used to fit the model.- type
Character argument specifying the type of prediction object to be returned. Case sensitive.

- ...
Optional arguments passed on to other methods. Currently used for models with smooth terms, for which these arguments are forwarded to

`mgcv::predict.gam`

.

## See also

`fitted.galamm()`

for model fits, `residuals.galamm()`

for
residuals, and `predict()`

for the generic function.

Other details of model fit:
`VarCorr()`

,
`coef.galamm()`

,
`confint.galamm()`

,
`deviance.galamm()`

,
`factor_loadings.galamm()`

,
`family.galamm()`

,
`fitted.galamm()`

,
`fixef()`

,
`formula.galamm()`

,
`llikAIC()`

,
`logLik.galamm()`

,
`nobs.galamm()`

,
`print.VarCorr.galamm()`

,
`ranef.galamm()`

,
`residuals.galamm()`

,
`sigma.galamm()`

,
`vcov.galamm()`

## Examples

```
# Poisson GLMM
count_mod <- galamm(
formula = y ~ lbas * treat + lage + v4 + (1 | subj),
data = epilep, family = poisson
)
# Plot response versus link:
plot(
predict(count_mod, type = "link"),
predict(count_mod, type = "response")
)
# Predict on a new dataset
nd <- data.frame(lbas = c(.3, .2), treat = c(0, 1), lage = 0.2, v4 = -.2)
predict(count_mod, newdata = nd)
#> [,1]
#> 1 2.188055
#> 2 1.832320
predict(count_mod, newdata = nd, type = "response")
#> [,1]
#> 1 8.917850
#> 2 6.248365
```