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# lavSimulate: fit the *same* model, on simulated datasets
# YR - 4 July 2016
lavSimulate <- function(pop.model = NULL, # population model
model = NULL, # user model
dataFunction = simulateData,
dataFunction.args = list(
model = pop.model,
sample.nobs = 1000L
),
ndat = 1000L,
cmd = "sem",
cmd.pop = "sem",
...,
store.slots = c("partable"),
FUN = NULL,
show.progress = FALSE,
store.failed = FALSE,
parallel = c("no", "multicore", "snow"),
ncpus = max(1L, parallel::detectCores() - 1L),
cl = NULL) {
# dotdotdot
dotdotdot <- list(...)
# dotdotdot for fit.pop
dotdotdot.pop <- dotdotdot
dotdotdot.pop$verbose <- FALSE
dotdotdot.pop$debug <- FALSE
dotdotdot.pop$data <- NULL
dotdotdot.pop$sample.cov <- NULL
# 'fit' population model without data, to get 'true' parameters
fit.pop <- do.call(cmd.pop,
args = c(list(model = pop.model), dotdotdot.pop)
)
# check model object
if (is.null(model)) {
model <- fit.pop@ParTable
}
# per default, use 'true' values as starting values
if (is.null(dotdotdot$start)) {
dotdotdot$start <- fit.pop
}
# no warnings during/after the simulations
# add 'warn = FALSE' to args
# generate simulations
fit <- do.call("lavaanList", args = c(list(
model = model,
dataFunction = dataFunction,
dataFunction.args = dataFunction.args,
ndat = ndat, cmd = cmd,
store.slots = store.slots, FUN = FUN,
show.progress = show.progress,
store.failed = store.failed,
parallel = parallel, ncpus = ncpus, cl = cl
), dotdotdot))
# flag this is a simulation
fit@meta$lavSimulate <- TRUE
# NOTE!!!
# if the model != pop.model, we may need to 'reorder' the
# 'true' parameters, so they correspond to the 'model' parameters
p2.id <- lav_partable_map_id_p1_in_p2(
p1 = fit@ParTable,
p2 = fit.pop@ParTable,
stopifnotfound = FALSE
)
est1 <- fit@ParTable$est
na.idx <- which(is.na(p2.id))
if (length(na.idx) > 0L) {
lav_msg_warn(gettext(
"some estimated parameters were not mentioned in the population model;
partable user model idx = ", lav_msg_view(na.idx, "none")))
# replace NA by '1' (override later!)
p2.id[na.idx] <- 1L
}
est.pop <- fit.pop@ParTable$est[p2.id]
# by default, the 'unknown' population values are set to 0.0
if (length(na.idx) > 0L) {
est.pop[na.idx] <- 0
}
# store 'true' parameters in meta$est.true
fit@meta$est.true <- est.pop
fit
}
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