gratia (version 0.9.0)

simulate.gam: Simulate from the posterior distribution of a GAM

Description

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
)

Value

(Currently) A matrix with nsim columns.

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.

Author

Gavin L. Simpson

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)