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Standardised solution of a latent variable model

Source: R/standardisedsolution.R
standardisedsolution.Rd

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 based on the variances of the (continuous) latent variables only. If "std.all", the standardized estimates are based on the variances of both (continuous) observed and latent variables. If "std.nox", the standardized estimates are based on the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates. Alternatively, type may be a vector of (observed) variable names (for example type = c("x1", "x2")); in that case only the parameters involving these variables are standardized (the other observed variables are left unstandardized). This is a generalization of "std.nox", where the (observed) exogenous x variables are the ones left unstandardized.

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, 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 model-implied observed covariance matrix, and the (residual) latent covariances are scaled similarly using the model-implied covariance matrix of the latent variables. Documented explicitly here (rather than inherited) because lavaan >= 0.7-1 renamed this and the next three arguments to snake_case.

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 output 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.

See also

summary(), coef(), vcov()

Examples

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

# Fit a CFA model with standardised latent variables
fit <- acfa(
  HS.model,
  data = HolzingerSwineford1939,
  test = "none",
  nsamp = 10,
  vb_correction = FALSE,
  verbose = FALSE
)
standardisedsolution(fit, nsamp = 10, se = FALSE, ci = FALSE)
#>        lhs op     rhs est.std
#> 1   visual =~      x1   0.786
#> 2   visual =~      x2   0.426
#> 3   visual =~      x3   0.538
#> 4  textual =~      x4   0.844
#> 5  textual =~      x5   0.846
#> 6  textual =~      x6   0.838
#> 7       x1 ~~      x1   0.380
#> 8       x2 ~~      x2   0.816
#> 9       x3 ~~      x3   0.708
#> 10      x4 ~~      x4   0.288
#> 11      x5 ~~      x5   0.283
#> 12      x6 ~~      x6   0.296
#> 13  visual ~~  visual   1.000
#> 14 textual ~~ textual   1.000
#> 15  visual ~~ textual   0.478