Simulations from the posterior distribution of a fitted GAM model involve computing predicted values for the observation data for which simulated data are required, then generating random draws from the probability distribution used when fitting the model.

## Usage

```
# S3 method for gam
simulate(
object,
nsim = 1,
seed = NULL,
data = newdata,
weights = NULL,
...,
newdata = NULL
)
# S3 method for gamm
simulate(
object,
nsim = 1,
seed = NULL,
data = newdata,
weights = NULL,
...,
newdata = NULL
)
# S3 method for scam
simulate(
object,
nsim = 1,
seed = NULL,
data = newdata,
weights = NULL,
...,
newdata = NULL
)
```

## Arguments

- object
a fitted GAM, typically the result of a call to mgcv::gam` or

`mgcv::gamm()`

.- nsim
numeric; the number of posterior simulations to return.

- seed
numeric; a random seed for the simulations.

- data
data frame; new observations at which the posterior draws from the model should be evaluated. If not supplied, the data used to fit the model will be used for

`newdata`

, if available in`object`

.- weights
numeric; a vector of prior weights. If

`newdata`

is null then defaults to`object[["prior.weights"]]`

, otherwise a vector of ones.- ...
arguments passed to methods.

`simulate.gam()`

and`simulate.scam()`

pass`...`

on to`predict.gam()`

. As such you can pass additional arguments such as`terms`

,`exclude`

, to select which model terms are included in the predictions. This may be useful, for example, for excluding the effects of random effect terms.- newdata
Deprecated. Use

`data`

instead.

## Details

For `simulate.gam()`

to function, the `family`

component of the fitted
model must contain, or be updateable to contain, the required random
number generator. See `mgcv::fix.family.rd()`

.

## Examples

```
load_mgcv()
dat <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")
sims <- simulate(m1, nsim = 5, seed = 42)
head(sims)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 11.445470 11.374304 10.098681 7.264881 8.796630
#> [2,] 6.510912 5.909584 9.057362 7.698084 11.444781
#> [3,] 3.837995 3.230610 3.550240 3.759380 4.774581
#> [4,] 12.361830 11.209226 10.714215 11.861957 10.746417
#> [5,] 14.851461 12.911440 11.356984 15.783913 15.106270
#> [6,] 5.921276 4.158963 5.520856 7.973614 9.654888
```