(Version 0.1.4.3, updated on 2022-09-08, release history) (No changes in functions since 0.1.4.0. Just fixing typo errors and changing some tests.)
Functions for estimating indirect effects, conditional indirect effects, and conditional effects in a model with moderation, mediation, and/or moderated mediation fitted by structural equation modelling (SEM) or estimated by multiple regression.
Compute an unstandardized or standardized indirect effect or conditional indirect effect in a path model.
Form the bootstrap confidence interval for this effect.
No need to define any parameters or similar code when fitting a model
in lavaan::sem()
. Just focus on fitting the model first.
After a model has been selected, users can compute the effect for nearly
any path, from nearly any variable, to nearly any other variables,
conditional on nearly any moderators, and at any levels of the
moderators. (See vignette("manymome")
for details.)
Supports structural equation models fitted by
lavaan::sem()
or by path models fitted by regression using
lm()
, although the focus of this package is on structural
equation models. The interface of the main functions are nearly the same
for both approaches.
No limit on the number of predictors, mediators, and outcome
variables, other than those by lavaan::sem()
and
lm()
.
Can estimate standardized indirect effects and standardized conditional indirect effects without the need to standardize the variables. The bootstrap confidence intervals for standardized effects correctly take into account the sampling variation of the standardizers (the standard deviations of the predictor and the outcome variable).
Supports dataset with missing data through lavaan::sem()
with full information maximum likelihood (fiml
).
Supports numeric and categorical moderators. It has a function
(factor2var()
) for the easy creation of dummy variables in
lavaan::sem()
, and can also capitalize on the native
support of categorical moderators in lm()
.
Bootstrapping, which can be time consuming, can be conducted just
once. The main functions for computing indirect effects and conditional
indirect effects can be called as many times as needed without redoing
bootstrapping because they can reuse pregenerated bootstrap estimates
(see vignette("manymome")
and
vignette("do_boot")
).
Supports indirect effects among latent variables for models fitted by
lavaan::sem()
(see vignette("med_lav")
).
Despite the aforementioned advantages, the current version of
manymome
has the following limitations:
Does not (officially) support categorical predictors.
Does not support multisample models (although lavaan
does).
Does not support multilevel models (although lavaan
does).
Only supports nonparametric bootstrapping and percentile confidence interval. Does not support other methods such as Monte Carlo confidence interval or parametric bootstrapping.
Only supports OLS estimation when lm()
is
used.
We would add more to this list (suggestions are welcomed by adding GitHub issues) so that users (and we) know when other
tools should be used instead of manymome
, or whether we can
address these limitations in manymome
in the future.
A good starting point is the Get-Started article
(vignette("manymome")
).
There are also articles
(vignettes) on special topics, such as how to use
mod_levels()
to set the levels of the moderators. More will
be added.
For more information on this package, please visit its GitHub page:
https://sfcheung.github.io/manymome/
The stable version at CRAN can be installed by
install.packages()
:
install.packages("manymome")
The latest developmental version at GitHub can be installed by
remotes::install_github()
:
remotes::install_github("sfcheung/manymome")
We developed the package stdmod
in
2021 for moderated regression. We included a function
(stdmod::stdmod_lavaan()
) for standardized moderation
effect in path models fitted by lavaan::sem()
. However, in
practice, path models nearly always included indirect effects and so
moderated mediation is common in path models. Moreover,
stdmod
is intended for moderated regression, not for
structural equation modeling. We thought perhaps we could develop a more
general tool for models fitted by structural equation modelling based on
the interface we used in stdmod::stdmod_lavaan()
. In our
own projects, we also need to estimate indirect effects in models
frequently. Large sample sizes with missing data are also common to us,
for which bootstrapping is slow even with parallel processing.
Therefore, we developed manymome
to address these
needs.
If you have any suggestions and found any bugs or limitations, please feel feel to open a GitHub issue. Thanks.
https://github.com/sfcheung/manymome/issues