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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TSML.R
\name{twostage}
\alias{twostage}
\alias{cfa.2stage}
\alias{sem.2stage}
\alias{growth.2stage}
\alias{lavaan.2stage}
\title{Fit a lavaan model using 2-Stage Maximum Likelihood (TSML) estimation for
missing data.}
\usage{
twostage(..., aux, fun, baseline.model = NULL)
lavaan.2stage(..., aux = NULL, baseline.model = NULL)
cfa.2stage(..., aux = NULL, baseline.model = NULL)
sem.2stage(..., aux = NULL, baseline.model = NULL)
growth.2stage(..., aux = NULL, baseline.model = NULL)
}
\arguments{
\item{\dots}{Arguments passed to the \code{\link[lavaan:lavaan]{lavaan::lavaan()}} function
specified in the \code{fun} argument. See also
\code{\link[lavaan:lavOptions]{lavaan::lavOptions()}}. At a minimum, the user must supply the
first two named arguments to \code{\link[lavaan:lavaan]{lavaan::lavaan()}} (i.e.,
\code{model} and \code{data}).}
\item{aux}{An optional character vector naming auxiliary variable(s) in
\code{data}}
\item{fun}{The character string naming the lavaan function used to fit the
Step-2 hypothesized model (\code{"cfa"}, \code{"sem"}, \code{"growth"}, or
\code{"lavaan"}).}
\item{baseline.model}{An optional character string, specifying the lavaan
\code{\link[lavaan:model.syntax]{lavaan::model.syntax()}} for a user-specified baseline model.
Interested users can use the fitted baseline model to calculate incremental
fit indices (e.g., CFI and TLI) using the corrected chi-squared values (see
the \code{anova} method in \linkS4class{twostage}). If \code{NULL},
the default "independence model" (i.e., freely estimated means and
variances, but all covariances constrained to zero) will be specified
internally.}
}
\value{
The \linkS4class{twostage} object contains 3 fitted lavaan
models (saturated, target/hypothesized, and baseline) as well as the names
of auxiliary variables. None of the individual models provide the correct
model results (except the point estimates in the target model are unbiased).
Use the methods in \linkS4class{twostage} to extract corrected
\emph{SE}s and test statistics.
}
\description{
This function automates 2-Stage Maximum Likelihood (TSML) estimation,
optionally with auxiliary variables. Step 1 involves fitting a saturated
model to the partially observed data set (to variables in the hypothesized
model as well as auxiliary variables related to missingness). Step 2
involves fitting the hypothesized model to the model-implied means and
covariance matrix (also called the "EM" means and covariance matrix) as if
they were complete data. Step 3 involves correcting the Step-2 standard
errors (\emph{SE}s) and chi-squared statistic to account for additional
uncertainty due to missing data (using information from Step 1; see
References section for sources with formulas).
}
\details{
All variables (including auxiliary variables) are treated as endogenous
varaibles in the Step-1 saturated model (\code{fixed.x = FALSE}), so data
are assumed continuous, although not necessarily multivariate normal
(dummy-coded auxiliary variables may be included in Step 1, but categorical
endogenous variables in the Step-2 hypothesized model are not allowed). To
avoid assuming multivariate normality, request \code{se = "robust.huber.white"}. CAUTION: In addition to setting \code{fixed.x = FALSE} and \code{conditional.x = FALSE} in \code{\link[lavaan:lavaan]{lavaan::lavaan()}},
this function will automatically set \code{meanstructure = TRUE},
\code{estimator = "ML"}, \code{missing = "fiml"}, and \code{test = "standard"}. \code{\link[lavaan:lavaan]{lavaan::lavaan()}}'s \code{se} option can only be
set to \code{"standard"} to assume multivariate normality or to
\code{"robust.huber.white"} to relax that assumption.
}
\examples{
## impose missing data for example
HSMiss <- HolzingerSwineford1939[ , c(paste("x", 1:9, sep = ""),
"ageyr","agemo","school")]
set.seed(12345)
HSMiss$x5 <- ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age <- HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 <- ifelse(age <= quantile(age, .3), NA, HSMiss$x9)
## specify CFA model from lavaan's ?cfa help page
HS.model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
'
## use ageyr and agemo as auxiliary variables
out <- cfa.2stage(model = HS.model, data = HSMiss, aux = c("ageyr","agemo"))
## two versions of a corrected chi-squared test results are shown
out
## see Savalei & Bentler (2009) and Savalei & Falk (2014) for details
## the summary additionally provides the parameter estimates with corrected
## standard errors, test statistics, and confidence intervals, along with
## any other options that can be passed to parameterEstimates()
summary(out, standardized = TRUE)
## use parameter labels to fit a more constrained model
modc <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + a*x8 + a*x9
'
outc <- cfa.2stage(model = modc, data = HSMiss, aux = c("ageyr","agemo"))
## use the anova() method to test this constraint
anova(out, outc)
## like for a single model, two corrected statistics are provided
}
\references{
Savalei, V., & Bentler, P. M. (2009). A two-stage approach to missing data:
Theory and application to auxiliary variables.
\emph{Structural Equation Modeling, 16}(3), 477--497.
\doi{10.1080/10705510903008238}
Savalei, V., & Falk, C. F. (2014). Robust two-stage approach outperforms
robust full information maximum likelihood with incomplete nonnormal data.
\emph{Structural Equation Modeling, 21}(2), 280--302.
\doi{10.1080/10705511.2014.882692}
}
\seealso{
\linkS4class{twostage}
}
\author{
Terrence D. Jorgensen (University of Amsterdam; \email{TJorgensen314@gmail.com})
}
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