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multilevelmediation contains functions for computing indirect effects with multilevel models and obtaining confidence intervals for various effects using bootstrapping. The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models, the option of a moderating variable at level 1 or level 2 for either the a, b, or both paths. Currently the 1-1-1 model is supported and several options of random effects are supported; the underlying initial code has been evaluated in simulations (see Falk et al in references). Support for Bayesian estimation and the inclusion of covariates comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors (e.g., for X) must be numerically represented.
# From GitHub:
# install.packages("devtools")
devtools::install_github("falkcarl/multilevelmediation")Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11(2), 142–163. https://doi.org/10.1037/1082-989X.11.2.142
Carpenter, J. R., Goldstein, H., & Rasbash, J. (2003). A novel bootstrap procedure for assessing the relationship between class size and achievement. Applied Statistics, 52(4), 431-443.
Falk, C. F., Vogel, T., Hammami, S., & Miočević, M. (in press). Multilevel mediation analysis in R: A comparison of bootstrap and Bayesian approaches. Behavior Research Methods. doi: https://doi.org/10.3758/s13428-023-02079-4 Preprint: https://doi.org/10.31234/osf.io/ync34
Hox, J., & van de Schoot, R. (2013). Robust methods for multilevel analysis. In M. A. Scott, J. S. Simonoff & B. D. Marx (Eds.), The SAGE Handbook of Multilevel Modeling (pp. 387-402). SAGE Publications Ltd. doi: 10.4135/9781446247600.n22
Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate behavioral research, 36(2), 249-277. doi: 10.1207/S15327906MBR3602_06
van der Leeden, R., Meijer, E., & Busing, F. M. T. A. (2008). Resampling multilevel models. In J. de Leeuw & E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 401-433). Springer.
lme (the function from the nlme package that fits the models) supports is available. Pass an argument (to modmed.mlm or any of the bootstrapping functions) for na.action that will be passed down to the lme function. For example, na.action = na.omit.brms package. When it is finished an update shall be posted.tibble as input
boot.modmed.mlm.custom is not set by default (it’s NULL).brms into master. This means that some support for brms is provided. Covariates with brms are not yet supported and that code could use some more testing. Also protect against possible bug for boot.modmed.mlm.custom.modmed.mlm. Could support additional centering and/or missing data handling.boot.modmed.mlm.custom introduced as a new function to unify all case bootstrapping and residual bootstrapping methods into one function and obtain further gains in speed. This reduces reliance on the boot package and appears to be a bit faster. Testing is still in progress, though this function may soon replace boot.modmed.mlm.modmed.mlm and boot.modmed.mlm. Pass an argument for na.action that will be passed down to the lme function. For example, na.action = na.omit.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.
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