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

[Thierry Onkelinx]

Dear David,

Please have a look at our multimput package
(https://github.com/inbo/multimput). It handles multiple imputation
based on generalised linear mixed models. Currently based on either
glmer (lme4) and inla (INLA) . After imputation you can apply any
model or function you like. So you could use the boot package as Bert
suggested.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey


2017-03-19 5:08 GMT+01:00 David Jones <david.tn.jones op gmail.com>:
> 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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