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

[Bert Gunter]

Obviously question: Did you check the boot package ??

Also, try searching rseek.org.

I suspect that in any case, you may have to do some
customizing/programming, as you seem to have quite a few criteria.

Cheers,
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Sat, Mar 18, 2017 at 9:08 PM, David Jones <david.tn.jones at gmail.com> wrote:
> 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
> estimate)
> * 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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