tinyVAST (version 1.0.1)

simulate.tinyVAST: Simulate new data from a fitted model

Description

simulate.tinyVAST is an S3 method for producing a matrix of simulations from a fitted model. It can be used with the DHARMa package among other uses. Code is modified from the version in sdmTMB

Usage

# S3 method for tinyVAST
simulate(
  object,
  nsim = 1L,
  seed = sample.int(1e+06, 1L),
  type = c("mle-eb", "mle-mvn"),
  ...
)

Value

A matrix with row for each row of data in the fitted model and nsim

columns, containing new samples from the fitted model.

Arguments

object

output from tinyVAST()

nsim

how many simulations to do

seed

random seed

type

How parameters should be treated. "mle-eb": fixed effects are at their maximum likelihood (MLE) estimates and random effects are at their empirical Bayes (EB) estimates. "mle-mvn": fixed effects are at their MLEs but random effects are taken from a single approximate sample. This latter option is a suggested approach if these simulations will be used for goodness of fit testing (e.g., with the DHARMa package).

...

not used

Examples

set.seed(101)
x = seq(0, 2*pi, length=100)
y = sin(x) + 0.1*rnorm(length(x))
fit = tinyVAST( data=data.frame(x=x,y=y), formula = y ~ s(x) )
sims = simulate(fit, nsim=100, type="mle-mvn")

if(requireNamespace("DHARMa")){
  # simulate new data conditional on fixed effects
  # and sampling random effects from their predictive distribution
  y_iz = simulate(fit, nsim=500, type="mle-mvn")

  # Visualize using DHARMa
  res = DHARMa::createDHARMa( simulatedResponse = y_iz,
                      observedResponse = y,
                      fittedPredictedResponse = fitted(fit) )
  plot(res)
}