Sample data from the posterior (or prior) distribution.

The purpose of the sampleData() function is to simulate new data from a model that has already been estimated. This can faciliate posterior predictive checks, as well as prior predictive checks (setting prisamp = TRUE during model estimation).

Usage

sampleData(object, nrep = NULL, conditional = FALSE, type = "response",
           simplify = FALSE, ...)

Arguments

object

An object of class blavaan.

nrep

How many datasets to generate? If not supplied, defaults to the total number of posterior samples.

conditional

Logical indicating whether to sample from the distribution that is marginal over latent variables (FALSE; default) or from the distribution that conditions on latent variables (TRUE). For TRUE, you must set save.lvs = TRUE during model estimation.

type

The type of data desired (only relevant to ordinal data). The type = "response" option generates ordinal data. The type = "link" option generates continuous variables underlying ordinal data (which would be cut by thresholds to yield ordinal data).

simplify

For single-group models, should the list structure be simplified? This makes each dataset a single list entry, instead of a list within a list (which reflects group 1 of dataset 1). Defaults to FALSE.

...

Other arguments, which for now is only parallel. Parallelization via future_lapply() is available by setting parallel = TRUE.

Details

This is a convenience function to generate data for posterior or prior predictive checking. The underlying code is also used to generate data for posterior predictive p-value computation.

See also

This function overlaps with blavPredict(). The blavPredict() function is more focused on generating pieces of data conditioned on other pieces of observed data (i.e., latent variables conditioned on observed variables; missing variables conditioned on observed variables). In contrast, the sampleData() function is more focused on generating new data given the sampled model parameters.

Examples

if (FALSE) { # \dontrun{
data(HolzingerSwineford1939, package = "lavaan")

## fit model
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- bcfa(HS.model, data = HolzingerSwineford1939)

## 1 dataset generated from the posterior
out <- sampleData(fit, nrep = 1)

## nested lists: 1 list entry per nrep.
## then, within a rep, 1 list entry per group
## so our dataset is here:
dim(out[[1]][[1]])

## 1 posterior dataset per posterior sample:
out <- sampleData(fit)

## obtain the data on x1 across reps and summarize:
x1dat <- sapply(out, function(x) x[[1]][,1])
summary( as.numeric(x1dat) )
} # }