parameterEstimates.mi: Pooled Parameter Estimates

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

Pooled Parameter Estimates

Description

This function pools parameter estimates from a lavaan model fitted to multiple imputed data sets.

Usage

parameterEstimates.mi(
  object,
  se = TRUE,
  zstat = se,
  pvalue = zstat,
  ci = TRUE,
  level = 0.95,
  fmi = FALSE,
  standardized = FALSE,
  cov.std = TRUE,
  rsquare = FALSE,
  asymptotic = FALSE,
  scale.W = !asymptotic,
  omit.imps = c("no.conv", "no.se"),
  remove.system.eq = TRUE,
  remove.eq = TRUE,
  remove.ineq = TRUE,
  remove.def = FALSE,
  remove.nonfree = FALSE,
  remove.unused = FALSE,
  output = "data.frame",
  header = FALSE
)

Arguments

object	An object of class lavaan.mi
se, zstat, pvalue, ci, level, standardized, cov.std, rsquare, remove.system.eq, remove.eq, remove.ineq, remove.def, remove.nonfree, remove.unused, output, header	See lavaan::parameterEstimates().
fmi	logical indicating whether to add 2 columns: the fraction of missing information (⁠$fmi⁠), which is the ratio of between-imputation variance to total (pooled) sampling variance the relative increase in variance (⁠$riv⁠), which is the ratio of between-imputation variance to within-imputation variance Thus, RIV = FMI / (1 - FMI) and FMI = RIV / (1 + RIV). Ignored when se=FALSE.
asymptotic	logical. When FALSE, pooled Wald tests will be t statistics with associated degrees of freedom (df). When TRUE, the df are assumed to be sufficiently large for a t statistic to approximate a standard normal distribution, so it is printed as a z statistic.
scale.W	logical. If TRUE (default), the vcov method will calculate the pooled covariance matrix by scaling the within-imputation component by the ARIV (see Enders, 2010, p. 235, for definition and formula). Otherwise, the pooled matrix is calculated as the weighted sum of the within-imputation and between-imputation components (see Enders, 2010, ch. 8, for details).
omit.imps	character indicating criteria for excluding imputations from pooled results. See lavaan.mi for argument details.

Value

A data.frame, analogous to lavaan::parameterEstimates(), but estimates, SEs, and tests are pooled across imputations.

Author(s)

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

References

Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/9780470316696")}

See Also

standardizedSolution.mi() to obtain inferential statistics for pooled standardized parameter estimates.

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)

## pooled estimates, with various optional features:

parameterEstimates.mi(fit, asymptotic = TRUE, rsquare = TRUE)
parameterEstimates.mi(fit, ci = FALSE, fmi = TRUE, output = "text")
parameterEstimates.mi(fit, standardized = "std.all", se = FALSE)

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