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The semTools package has a Missing Data
suite, which for years included utilities for fitting structural
equation models to multiply imputed data sets with the
lavaan package and automatically pooling the results. These
multiple-imputation features continue to grow, justifying their own
separate package (and current deprecation in semTools).
semTools:runMI() function does not exist. The
semTools::runMI() function had a fun= argument
to specify which lavaan function to call
(lavaan() or the cfa() and sem()
wrappers). Instead, analogous to lavaan, the basic function
will be lavaan.mi(), with cfa.mi() and
sem.mi() as wrappers around lavaan.mi().semTools::runMI(), users may
not impute data indirectly via miPackage=
and miArgs= arguments. This feature was originally included
to mimic Mplus, which allows for model-based imputation.
Because semTools::runMI() did not use the specified SEM as
an imputation model, the mimicry of the Mplus functionality was
only cosmetic. Even in semTools::runMI(), it was always
better to impute (with full control) using dedicated imputation software
first (e.g., the Amelia or mice package, or
external software like Blimp), to be
analyzed with lavaan.mi().
blavaan package does indirectly allow for
model-based imputation, if users explicitly request posterior samples of
missing values. It is planned to eventually capitalize on that
feature with a dedicated function in the lavaan.mi
package.lavaan(test=) argument, there is a new
test= argument for lavaan::lavTestLRT(),
enabling users to select which specific \(\Delta \chi^2\) test should be calculated
for a model comparison. Accordingly, the lavTestLRT.mi()
function’s test= argument has been renamed
pool.method=, so that users can still pass a
test= argument to lavTestLRT.mi(...).
test= argument to select "D1",
"D2", or "D3". The lavaan.mi
package has change that argument to pool.method=, as well
as implementing the newer "D4" method for pooling
likelihood-ratio statistics (see Neww Features
section).pool.method = "D4") for lavTestLRT.mi(),
replacing the old default (but pool.method = "D3" is still
available). The D4 pooling method performs similarly to D3 (see Grund et
al.’s simulation
study) but is less computationally intensive.lavaan(test=) argument, the user can
choose which statistic can be pooled using
lavTestLRT.mi(..., pool.method = "D2", pool.robust = TRUE).
This is controlled via the new scaled.test= argument. As an
alternative to the standard \(\chi^2\)
statistic, the user can also pool Browne’s
(1984) residual-based statistic using, e.g.,
lavTestLRT.mi(..., pool.method = "D2", standard.test = "browne.residual.nt")
with the default pool.robust=FALSE.lavaan functionality for pooled results:
paramterEstimates.mi(),
standardizedSolution.mi(), and
lavResiduals.mi().poolSat() function implements Cai and colleagues’
(2012, 2019) method of
pooling the saturated-model results, then fitting SEM(s) to those pooled
summary statistics. This can be much more computationally efficient than
fitting each SEM of interest to each imputation and pooling the results
of each estimated SEM. Instead, pooling only occurs for the summary
statistics, and each SEM is estimated as it would be for complete
data.VGAM package was loaded, it created a conflict with
finding the anova() method for lavaan.mi
objects in semTools. This was resolved in February 2022
when VGAM version 1.1-6 was sent to CRAN.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.