[R-sig-ME] mixed effect modeling with imputed data set

[Ken Beath]

mice is very easy to use and customise, I haven't used Stata's routines but
they seem a bit more complex to use but maybe less powerful. The advantage
with using mice is it is just a few statements to set up the analysis. The
author of mice has a book describing the capabilities and methods.


On 17 June 2015 at 11:21, Ben Bolker <bbolker at gmail.com> wrote:

>   For convenience, you might want to consider using imputation with
> the 'mice' package in order to
> stay within R for your analysis (I don't know Stata's capabilities in
> this area; I'd expect them to be pretty good,
> but it is my impression that 'mice' is also quite
> well-written/full-featured)
>
> On Tue, Jun 16, 2015 at 12:26 PM, Daniel Fulop <dfulop.ucd at gmail.com>
> wrote:
> > Use one of the apply functions to iterate over your imputed datasets.
> >
> > If your imputed datasets are in columns 5 through n+4 of "mydata" (i.e.
> > assuming that x1, x2, x3, and regioid are in columns 1:4), the you could
> do
> > something like:
> >
> > model.list <- lapply(1:n, function(i)
> >
> > glmer(mydata[,i+4] ~ x1+x2 +x3+(1|regiogid),family= binomial("logit"),
> > data=mydata) )
> >
> > The output will then be a list of model objects (i.e. model fits).  You
> can
> > then iterated through this results list in order to calculate mean
> parameter
> > values from all your imputed data fits.
> >
> > Or, likewise:
> >
> > model.list <- lapply(5:ncol(mydata), function(i) ...
> >
> > Hope this helps,
> > Dan.
> >
> >
> > ali via R-sig-mixed-models wrote:
> >>
> >> Hi all
> >> I imputed my data using multiple imputation procedure in STATA. I would
> >> like
> >> to conduct mixed effect modeling on the imputed data set in R. I do not
> >> know
> >> how to write the code over the imputed data set.
> >> My code is:
> >> fit<- glmer(y ~ x1+x2 +x3+(1|regiogid),family= binomial("logit"), data
> >> =mydata)
> >>
> >> Best Regards
> >>
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
> > --
> > Daniel Fulop, Ph.D.
> > Postdoctoral Scholar
> > Dept. Plant Biology, UC Davis
> > Maloof Lab, Rm. 2220
> > Life Sciences Addition, One Shields Ave.
> > Davis, CA 95616
> >
> > 510-253-7462
> > dfulop at ucdavis.edu
> >
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 

*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia

Phone: +61 (0)2 9850 8516

Level 2, AHH
http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/

CRICOS Provider No 00002J
This message is intended for the addressee named and may...{{dropped:9}}