simulate.tinyVAST: Simulate new data from a fitted model

In tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations

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

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

Value

A matrix with row for each row of data in the fitted model and nsim columns, containing new samples from the fitted model.

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)
}

tinyVAST documentation built on April 4, 2025, 2:43 a.m.