Nothing
blav_model_fit <- function(lavpartable = NULL,
lavmodel = NULL,
lavjags = NULL,
x = NULL,
VCOV = NULL,
TEST = NULL) {
stopifnot(is.list(lavpartable), inherits(lavmodel, c("Model",
"lavModel")))
if(!inherits(lavjags, "NULL")){
lavmcmc <- make_mcmc(lavjags)
} else {
lavmcmc <- NULL
}
# extract information from 'x'
iterations <- attr(x, "iterations")
converged <- attr(x, "converged")
fx <- attr(x, "fx")
fx.group <- as.numeric(NA)
#fx.group = attr(fx, "fx.group")
logl.group <- as.numeric(NA)
logl <- as.numeric(NA)
#print(fx.group)
control <- attr(x, "control")
attributes(fx) <- NULL
x.copy <- x # we are going to change it (remove attributes)
attributes(x.copy) <- NULL
est <- lav_model_get_parameters(lavmodel = lavmodel, type = "user")
# did we compute standard errors?
blaboot <- rearr_params(lavmcmc, lavpartable)
se <- lav_model_vcov_se(lavmodel = lavmodel, lavpartable = lavpartable,
VCOV = VCOV, BOOT = blaboot)
# did we compute test statistics
if(is.null(TEST)) {
test <- list()
} else {
test <- TEST
}
# for convenience: compute lavmodel-implied Sigma and Mu
implied <- lav_model_implied(lavmodel, delta = (lavmodel@parameterization == "delta"))
# change names back if conditional.x (see lav_model_implied.R)
if(lavmodel@conditional.x) {
names(implied) <- c("cov", "mean", "slopes", "th", "group.w")
}
# partrace?
if(!is.null(attr(x, "partrace"))) {
PARTRACE <- attr(x, "partrace")
} else {
PARTRACE <- matrix(0, 0L, 0L)
}
new("Fit",
npar = as.integer(max(lavpartable$free)),
x = x.copy,
partrace = PARTRACE,
start = lavpartable$start, # needed?
est = est,
se = se,
fx = fx,
fx.group = fx.group,
logl = logl,
logl.group = logl.group,
iterations = as.integer(iterations),
converged = converged,
control = control,
Sigma.hat = implied$cov,
Mu.hat = implied$mean,
TH = implied$th,
test = test
)
}
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