parameterEstimates.mi {lavaan.mi} | R Documentation |
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 |
se , zstat , pvalue , ci , level , standardized , cov.std , rsquare , remove.system.eq , remove.eq , remove.ineq , remove.def , remove.nonfree , remove.unused , output , header |
|
fmi |
Thus, RIV = FMI / (1 |
asymptotic |
|
scale.W |
|
omit.imps |
|
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; TJorgensen314@gmail.com)
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. 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)