INLAvaan (version 0.2.4)

INLAvaan-class: Class For Representing a (Fitted) Latent Variable Model

Description

This is a class that extends the lavaan::lavaan class. Several S4 methods are available.

Usage

# S4 method for INLAvaan
coef(object, ...)
# S4 method for INLAvaan
nobs(object, ...)
# S4 method for INLAvaan
show(object)
# S4 method for INLAvaan
summary(
  object,
  header = TRUE,
  fit.measures = TRUE,
  estimates = TRUE,
  standardized = FALSE,
  rsquare = FALSE,
  postmedian = FALSE,
  postmode = FALSE,
  nmad = TRUE,
  kld = FALSE,
  vb_shift = FALSE,
  priors = TRUE,
  nd = 3L,
  ...
)

Arguments

object

An object of class INLAvaan.

...

Additional arguments passed to methods.

header

Logical; if TRUE, print model fit information header.

fit.measures

Logical; if TRUE, print fit measures (DIC and PPP).

estimates

Logical; if TRUE, print parameter estimates table.

standardized

Logical; if TRUE, include standardized estimates.

rsquare

Logical; if TRUE, include R-square values.

postmedian

Logical; if TRUE, include posterior median in estimates.

postmode

Logical; if TRUE, include posterior mode in estimates.

nmad

Logical; if TRUE (default), include the NMAD column for skew-normal marginal fit quality.

kld

Logical; if FALSE (default), omit the per-parameter KLD column. Set to TRUE to show it.

vb_shift

Logical; if FALSE (default), omit the VB shift column (shift in units of posterior SD). Set to TRUE to show it.

priors

Logical; if TRUE, include prior information in estimates.

nd

Integer; number of decimal places to print for numeric values.

Slots

external

A list containing an inlavaan_internal object.

See Also

lavaan::lavaan, inlavaan(), acfa(), asem(), agrowth()

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)

# Print basic info
fit

# Detailed summary
summary(fit)

# Extract coefficients
coef(fit)
# }