Sample from predictive distribution of a variable — sample

sample_variable samples from the joint distribution of random and fixed effects to approximate the predictive distribution for a variable

Using sample_fixed=TRUE (the default) in sample_variable propagates variance in both fixed and random effects, while using sample_fixed=FALSE does not. Sampling fixed effects will sometimes cause numerical under- or overflow (i.e., output values of NA) in cases when variance parameters are estimated imprecisely. In these cases, the multivariate normal approximation being used is a poor representation of the tail probabilities, and results in some samples with implausibly high (or negative) variances, such that the associated random effects then have implausibly high magnitude.

Usage

sample_variable(
  x,
  variable_name = "mu_i",
  n_samples = 100,
  sample_fixed = TRUE,
  seed = 123456
)

Arguments

x: output from \code{tinyVAST()}
variable_name: name of variable available in report using Obj$report() or parameters using Obj$env$parList()
n_samples: number of samples from the joint predictive distribution for fixed and random effects. Default is 100, which is slow.
sample_fixed: whether to sample fixed and random effects, sample_fixed=TRUE as by default, or just sample random effects, sample_fixed=FALSE
seed: integer used to set random-number seed when sampling variables, as passed to set.seed(.)

Examples

if (FALSE) {
# Run model using selected inputs, but also with getJointPrecision=TRUE
fit = tinyVAST( ...,
    control = tinyVASTcontrol(getJointPrecision=TRUE) )

# Run sample_variable
sample = sample_variable( x = fit,
                          variable_name = "mu_i" )
}