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.jo...@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 > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
