[R] Options for bootstrapped CIs for indirect effect: Nested data structure, missing data, and fully continuous X variable

David Jones david.tn.jones at gmail.com
Sun Mar 19 05:08:36 CET 2017

I am looking for a package or other solution in R that can evaluate
indirect effects and meets all of the following criteria:

* Can create bootstrapped CIs around an indirect effect (or can
implement any other method of creating asymmetric CIs)
* Can address nested data (e.g., through multilevel/mixed effects)
* Can allow for fully continuous X variables
* Can address missing data (e.g., using multiple imputation via a
package such as mice; I have a non-normally distributed mediator so
cannot use ML for all estimation)

Any input on what would address these criteria would be greatly appreciated.

Here are the packages I have tried so far:

* lavaan.survey - can do all of the above except for bootstrap
estimation of the indirect effect (lavaan is great but cannot do
multilevel, lavaan.survey is also great but cannot do the bootstrap
* mediation - Has many strong features, but limits the X (treatment)
variable to take 2 values at a time, whereas I have dozens of X values
(from an observational study)
* piecewiseSEM - Is very flexible and allows for multilevel data
structure and multiple distributions, but does not have
bootstrap/asymmetric CIs for indirect effects

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