Extract the model-implied (fitted) sample statistics from a fitted
INLAvaan model. As in lavaan and blavaan, the moments are
the model-implied covariance matrix (and mean vector, when a mean structure
is present) evaluated at the parameter estimates – here the posterior means.
Usage
# S4 method for class 'INLAvaan'
fitted(object, type = "moments", labels = TRUE, ...)
# S4 method for class 'INLAvaan'
fitted.values(object, type = "moments", labels = TRUE, ...)Arguments
- object
An object of class INLAvaan.
- type
Character.
"moments"(default) returns the model-implied variance-covariance matrix and, when relevant, the mean vector (plus thresholds for ordinal data)."casewise"(aliases"obs","ov") returns the model-predicted values for each observation.- labels
Logical. Attach variable names to the output. Default
TRUE.- ...
Currently unused.
Value
For type = "moments", a list (or list of lists, for
multiple groups) with elements such as cov, mean, and
th. For type = "casewise", a numeric matrix of predicted
observed-variable values.
Details
This delegates to lavaan's own fitted() machinery, so the return
structure matches lavaan exactly. Because INLAvaan stores the posterior means
as the point estimates of the fitted object, the implied moments are the
posterior-mean model-implied moments (mirroring blavaan).
Examples
# \donttest{
HS.model <- "
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
"
utils::data("HolzingerSwineford1939", package = "lavaan")
fit <- acfa(HS.model, HolzingerSwineford1939, std.lv = TRUE, nsamp = 100,
test = "none", verbose = FALSE)
# Model-implied covariance matrix (posterior means)
fitted(fit)
#> $cov
#> x1 x2 x3 x4 x5 x6 x7 x8 x9
#> x1 1.387
#> x2 0.455 1.402
#> x3 0.601 0.333 1.295
#> x4 0.407 0.225 0.298 1.380
#> x5 0.453 0.251 0.331 1.115 1.696
#> x6 0.377 0.209 0.276 0.927 1.032 1.222
#> x7 0.260 0.144 0.190 0.172 0.192 0.160 1.203
#> x8 0.309 0.171 0.226 0.205 0.228 0.190 0.451 1.041
#> x9 0.287 0.159 0.210 0.191 0.212 0.177 0.419 0.498 1.033
#>
# Casewise model-predicted observed values
head(fitted(fit, type = "ov"))
#> x1 x2 x3 x4 x5 x6 x7 x8
#> [1,] 4.118731 5.635919 1.653088 2.926310 4.190668 2.0609440 4.254147 5.608193
#> [2,] 4.989095 6.117548 2.289401 2.047171 3.211824 1.2469280 4.815793 6.275771
#> [3,] 4.182414 5.671159 1.699646 1.186824 2.253904 0.4503122 3.359411 4.544701
#> [4,] 5.359268 6.322389 2.560030 3.078493 4.360111 2.2018545 3.918648 5.209416
#> [5,] 4.514878 5.855133 1.942706 2.939067 4.204872 2.0727562 4.378565 5.756077
#> [6,] 4.962713 6.102950 2.270114 1.729739 2.858392 0.9530101 4.892349 6.366766
#> x9
#> [1,] 5.449602
#> [2,] 6.070781
#> [3,] 4.460027
#> [4,] 5.078541
#> [5,] 5.587207
#> [6,] 6.155451
# }