Standardised solution of a latent variable model

Usage

standardisedsolution(
  object,
  type = "std.all",
  se = TRUE,
  ci = TRUE,
  level = 0.95,
  postmedian = FALSE,
  postmode = FALSE,
  cov.std = TRUE,
  remove.eq = TRUE,
  remove.ineq = TRUE,
  remove.def = FALSE,
  nsamp = 250,
  ...
)

standardisedSolution(
  object,
  type = "std.all",
  se = TRUE,
  ci = TRUE,
  level = 0.95,
  postmedian = FALSE,
  postmode = FALSE,
  cov.std = TRUE,
  remove.eq = TRUE,
  remove.ineq = TRUE,
  remove.def = FALSE,
  nsamp = 250,
  ...
)

standardizedsolution(
  object,
  type = "std.all",
  se = TRUE,
  ci = TRUE,
  level = 0.95,
  postmedian = FALSE,
  postmode = FALSE,
  cov.std = TRUE,
  remove.eq = TRUE,
  remove.ineq = TRUE,
  remove.def = FALSE,
  nsamp = 250,
  ...
)

standardizedSolution(
  object,
  type = "std.all",
  se = TRUE,
  ci = TRUE,
  level = 0.95,
  postmedian = FALSE,
  postmode = FALSE,
  cov.std = TRUE,
  remove.eq = TRUE,
  remove.ineq = TRUE,
  remove.def = FALSE,
  nsamp = 250,
  ...
)

Arguments

object: An object of class INLAvaan.
type: If "std.lv", the standardized estimates are on the variances of the (continuous) latent variables only. If "std.all", the standardized estimates are based on both the variances of both (continuous) observed and latent variables. If "std.nox", the standardized estimates are based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.
se: Logical. If TRUE, standard errors for the standardized parameters will be computed, together with a z-statistic and a p-value.
ci: If TRUE, simple symmetric confidence intervals are added to the output
level: The confidence level required.
postmedian: Logical; if TRUE, include posterior median in estimates.
postmode: Logical; if TRUE, include posterior mode in estimates.
cov.std: Logical. If TRUE, the (residual) observed covariances are scaled by the square root of the `Theta' diagonal elements, and the (residual) latent covariances are scaled by the square root of the `Psi' diagonal elements. If FALSE, the (residual) observed covariances are scaled by the square root of the diagonal elements of the observed model-implied covariance matrix (Sigma), and the (residual) latent covariances are scaled by the square root of diagonal elements of the model-implied covariance matrix of the latent variables.
remove.eq: Logical. If TRUE, filter the output by removing all rows containing equality constraints, if any.
remove.ineq: Logical. If TRUE, filter the output by removing all rows containing inequality constraints, if any.
remove.def: Logical. If TRUE, filter the ouitput by removing all rows containing parameter definitions, if any.
nsamp: The number of samples to draw from the approximate posterior distribution for the calculation of standardised estimates.
...: Additional arguments sent to lavaan::standardizedSolution().

Value

A data.frame containing standardised model parameters.

Examples

HS.model <- "
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"
utils::data("HolzingerSwineford1939", package = "lavaan")

# Fit a CFA model with standardised latent variables
fit <- acfa(HS.model, data = HolzingerSwineford1939, test = "none")
#> ℹ Finding posterior mode.
#> ✔ Finding posterior mode. [70ms]
#> 
#> ℹ Computing the Hessian.
#> ✔ Computing the Hessian. [145ms]
#> 
#> ℹ Performing VB correction.
#> ✔ VB correction; mean |δ| = 0.025σ. [123ms]
#> 
#> ⠙ Fitting skew normal to 0/21 marginals.
#> ✔ Fitting skew normal to 21/21 marginals. [718ms]
#> 
#> ℹ Sampling covariances and defined parameters.
#> ✔ Sampling covariances and defined parameters. [92ms]
#> 
standardisedsolution(fit, nsamp = 100)
#>        lhs op     rhs est.std    se ci.lower ci.upper
#> 1   visual =~      x1   0.734 0.094    0.537    0.897
#> 2   visual =~      x2   0.435 0.099    0.252    0.647
#> 3   visual =~      x3   0.589 0.093    0.404    0.789
#> 4  textual =~      x4   0.850 0.022    0.810    0.890
#> 5  textual =~      x5   0.852 0.028    0.794    0.890
#> 6  textual =~      x6   0.838 0.026    0.784    0.879
#> 7    speed =~      x7   0.547 0.078    0.422    0.734
#> 8    speed =~      x8   0.695 0.116    0.495    0.923
#> 9    speed =~      x9   0.653 0.116    0.442    0.895
#> 10      x1 ~~      x1   0.453 0.134    0.195    0.711
#> 11      x2 ~~      x2   0.801 0.089    0.581    0.937
#> 12      x3 ~~      x3   0.644 0.110    0.378    0.837
#> 13      x4 ~~      x4   0.276 0.037    0.208    0.344
#> 14      x5 ~~      x5   0.273 0.047    0.207    0.369
#> 15      x6 ~~      x6   0.297 0.044    0.227    0.385
#> 16      x7 ~~      x7   0.694 0.092    0.462    0.822
#> 17      x8 ~~      x8   0.504 0.164    0.149    0.755
#> 18      x9 ~~      x9   0.560 0.157    0.198    0.804
#> 19  visual ~~  visual   1.000 0.000    1.000    1.000
#> 20 textual ~~ textual   1.000 0.000    1.000    1.000
#> 21   speed ~~   speed   1.000 0.000    1.000    1.000
#> 22  visual ~~ textual   0.457 0.070    0.321    0.584
#> 23  visual ~~   speed   0.482 0.085    0.316    0.629
#> 24 textual ~~   speed   0.284 0.077    0.138    0.420