[R-sig-ME] lmer(): Higher weight in case of more measurements

[Ben Bolker]

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  It really depends what you mean by "weight clusters which have more
measurements slightly higher".  Without any more detail about your
intended statistical model I would *guess* that the model you specify
below would be appropriate, but it's really hard to tell with this
level of detail.  Can you point e.g. to a paper that uses similar
techniques and specifies the  model it is using explicitly?

On 15-05-15 08:36 AM, romunov wrote:
> Hi,
> 
> something similar has been discussed before. Have you seen this? 
> https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q3/022481.html
>
>  Cheers, Roman
> 
> 
> On Fri, May 15, 2015 at 2:28 PM, Susanne Susanne
> <susanne.stat at gmx.de> wrote:
> 
>> Dear Mr Bates and Mr Bolker, Dear R-list,
>> 
>> I have a question regarding the lme4 package and would be very
>> thankful if you could help me. My data consists of several
>> clusters with repeated measurements x. Therefore, I want to use
>> the lmer() function to regress the data using a mixed model:
>> 
>> lmer(y ~ x + (x | cluster).
>> 
>> I want to weight clusters, which have more measurements than
>> others, slightly higher, as they provide more information.
>> 
>> Is this done automatically in the lmer() function or should I do
>> it manually by myself? And how could this be realized?
>> 
>> I tried out small examples with fictitious data (and quite a few
>> with real data) but it seems impossible for me to decide whether
>> lmer() is weighting these clusters higher. If I weight some
>> clusters higher with the “weights=” argument, it seems to matter
>> how many measurements the cluster has. If I set default weights
>> it doesn't seem to matter.
>> 
>> Many thanks in advance,
>> 
>> Yours sincerely,
>> 
>> Susanne
>> 
>> _______________________________________________ 
>> R-sig-mixed-models at r-project.org mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> 
> 
> 
> 

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