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Simulate from the posterior distribution of a GAM

Source: R/simulate-methods.R
simulate.Rd

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 class 'gam'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  data = newdata,
  weights = NULL,
  ...,
  newdata = NULL
)

# S3 method for class 'gamm'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  data = newdata,
  weights = NULL,
  ...,
  newdata = NULL
)

# S3 method for class '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.

Value

(Currently) A matrix with nsim columns.

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().

Author

Gavin L. Simpson

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