Class For Representing a (Fitted) Latent Variable Model
Source:R/INLAvaan-class.R, R/method-others.R, R/method-show.R, and 1 more
INLAvaan-class.RdThis is a class that extends the lavaan::lavaan class. Several S4 methods are available.
Usage
# S4 method for class 'INLAvaan'
coef(object, ...)
# S4 method for class 'INLAvaan'
nobs(object, ...)
# S4 method for class 'INLAvaan'
show(object)
# S4 method for class '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.
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
#> INLAvaan 0.2.5.9004 ended normally after 66 iterations
#>
#> Estimator BAYES
#> Optimization method NLMINB
#> Number of model parameters 21
#>
#> Number of observations 301
#>
#> Model Test (User Model):
#>
#> Marginal log-likelihood -3848.435
# Detailed summary
summary(fit)
#> INLAvaan 0.2.5.9004 ended normally after 66 iterations
#>
#> Estimator BAYES
#> Optimization method NLMINB
#> Number of model parameters 21
#>
#> Number of observations 301
#>
#> Model Test (User Model):
#>
#> Marginal log-likelihood -3848.435
#>
#> Parameter Estimates:
#>
#> Marginalisation method SKEWNORM
#> VB correction TRUE
#>
#> Latent Variables:
#> Estimate SD 2.5% 97.5% NMAD Prior
#> visual =~
#> x1 0.907 0.082 0.748 1.068 0.009 normal(0,10)
#> x2 0.502 0.081 0.344 0.662 0.000 normal(0,10)
#> x3 0.663 0.078 0.512 0.817 0.002 normal(0,10)
#> textual =~
#> x4 1.001 0.058 0.891 1.117 0.003 normal(0,10)
#> x5 1.114 0.064 0.993 1.243 0.003 normal(0,10)
#> x6 0.926 0.055 0.823 1.037 0.003 normal(0,10)
#> speed =~
#> x7 0.616 0.074 0.466 0.758 0.003 normal(0,10)
#> x8 0.732 0.073 0.586 0.872 0.014 normal(0,10)
#> x9 0.681 0.075 0.537 0.833 0.016 normal(0,10)
#>
#> Covariances:
#> Estimate SD 2.5% 97.5% NMAD Prior
#> visual ~~
#> textual 0.449 0.064 0.318 0.567 0.001 beta(1,1)
#> speed 0.465 0.084 0.298 0.625 0.011 beta(1,1)
#> textual ~~
#> speed 0.280 0.070 0.139 0.414 0.003 beta(1,1)
#>
#> Variances:
#> Estimate SD 2.5% 97.5% NMAD Prior
#> .x1 0.565 0.117 0.341 0.797 0.011 gamma(1,.5)[sd]
#> .x2 1.150 0.107 0.956 1.374 0.001 gamma(1,.5)[sd]
#> .x3 0.856 0.097 0.672 1.054 0.003 gamma(1,.5)[sd]
#> .x4 0.379 0.049 0.287 0.480 0.003 gamma(1,.5)[sd]
#> .x5 0.455 0.059 0.344 0.577 0.003 gamma(1,.5)[sd]
#> .x6 0.364 0.045 0.281 0.456 0.002 gamma(1,.5)[sd]
#> .x7 0.823 0.090 0.662 1.015 0.004 gamma(1,.5)[sd]
#> .x8 0.506 0.087 0.346 0.689 0.023 gamma(1,.5)[sd]
#> .x9 0.569 0.090 0.392 0.742 0.007 gamma(1,.5)[sd]
#> visual 1.000
#> textual 1.000
#> speed 1.000
#>
# Extract coefficients
coef(fit)
#> visual=~x1 visual=~x2 visual=~x3 textual=~x4 textual=~x5
#> 0.907 0.502 0.663 1.001 1.114
#> textual=~x6 speed=~x7 speed=~x8 speed=~x9 x1~~x1
#> 0.926 0.616 0.732 0.681 0.565
#> x2~~x2 x3~~x3 x4~~x4 x5~~x5 x6~~x6
#> 1.150 0.856 0.379 0.455 0.364
#> x7~~x7 x8~~x8 x9~~x9 visual~~textual visual~~speed
#> 0.823 0.506 0.569 0.449 0.465
#> textual~~speed
#> 0.280
# }