standardizedSolution.mi: Standardized Pooled Parameter Estimates

In lavaan.mi: Fit Structural Equation Models to Multiply Imputed Data

Standardized Pooled Parameter Estimates

Description

This function calculates pooled parameter estimates from a lavaan model fitted to multiple imputed data sets, then transforms the pooled estimates and their SEs using the delta method.

Usage

standardizedSolution.mi(
  object,
  return.vcov = FALSE,
  omit.imps = c("no.conv", "no.se"),
  ...
)

Arguments

object	An object of class lavaan.mi
return.vcov	logical indicating whether to return only the pooled asymptotic covariance matrix, vcov(object), but transformed for standardized parameters. This is a way to obtain a pooled analog of lavInspect(object, "vcov.std.all") with a lavaan::lavaan object, and it is how the SEs are derived for standardized solutions.
omit.imps	character indicating criteria for excluding imputations from pooled results. See lavaan.mi for argument details.
...	Arguments passed to lavaan::standardizedSolution().

Value

A data.frame containing standardized model parameters, analogous to lavaan::standardizedSolution(). Delta-method SEs and CIs rely on asymptotic theory, so only Wald z tests are available, analogous to setting parameterEstimates.mi(fit, asymptotic = TRUE).

Author(s)

Terrence D. Jorgensen (University of Amsterdam; [e-mail])

See Also

parameterEstimates.mi() for pooling unstandardized parameter estimates, which can also add standardized point estimates to indicate effect size.

Examples

data(HS20imps) # import a list of 20 imputed data sets

## specify CFA model from lavaan's ?cfa help page
HS.model <- '
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
'
## fit model to 20 imputed data sets
fit <- cfa.mi(HS.model, data = HS20imps)

standardizedSolution.mi(fit) # default: type = "std.all"

## only standardize latent variables:
standardizedSolution.mi(fit, type = "std.lv",
                        output = "text") # display like summary()

lavaan.mi documentation built on April 3, 2025, 9:36 p.m.