[R] Missing data and LME models and diagnostic plots
mark_difford at yahoo.co.uk
Thu Oct 22 07:57:19 CEST 2009
>> See e.g. Hedeker and Gibbons, Longitudinal Data Analysis, which
>> repeatedly stresses that
>> mixed models provide good estimates if the data are missing at random.
This may be true. However, one of the real strengths of LME is that it
handles unbalanced designs, which is a different thing. The fact that it
also gets good estimates when data are missing is a bonus.
Peter Flom-2 wrote:
> I wrote
>>>> I am puzzled by the performance of LME in situations where there are
>>>> missing data. As I
>>>> understand it, one of the strengths of this sort of model is how well
>>>> deals with missing
>>>> data, yet lme requires nonmissing data.
> Mark Difford replied
>>You are confusing missing data with an unbalanced design. A strengths of
>>is that it copes with the latter, which aov() does not.
> Thanks for your reply, but I don't believe I am confused with respect to
> missing data in mixed models. See e.g. Hedeker and Gibbons, Longitudinal
> Data Analysis, which repeatedly stresses that mixed models provide good
> estimates if the data are missing at random.
> Peter L. Flom, PhD
> Statistical Consultant
> Website: www DOT peterflomconsulting DOT com
> Writing; http://www.associatedcontent.com/user/582880/peter_flom.html
> Twitter: @peterflom
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
View this message in context: http://www.nabble.com/Missing-data-and-LME-models-and-diagnostic-plots-tp25996584p26004465.html
Sent from the R help mailing list archive at Nabble.com.
More information about the R-help