[R-sig-ME] Compute a repeated measures model in lme4

[Ryan Hope]

You could use anova() to actually compare the models with different
random effects terms to see if a more complicated model is justified.

On Mon, Jun 8, 2015 at 1:53 PM, Philippi, Tom <tom_philippi at nps.gov> wrote:
> Mario--
> Yes your formula is redundant.  It may or may not describe the model you
> are interested in.
> Look at the documentation for formula specification:
> * as in
> treatment*daytype*time*age
> includes both the individual main effects and the interactions up to the
> 4-way interaction, so your other terms are already included.
> : specifies an interaction.
>
> If you only want main effects plus those 3 2-way interactions, you can use
> something like:
> lme.mean7<-lmer(averageba~ treatment+daytype+time+age+
>                              age:activity+ time:activity+treatment:daytype+
>                             (1|indiv), REML = FALSE)
> Again, ?formula will help you with the syntax to specify the model you are
> interested in.
>
> Also, think hard about your random effect.  While there are some repeated
> measures models where (1|individual) is appropriate, in many cases
> (1+time|individual) or equivalently (time|individual) is more appropriate
> and informative.
>
> I hope that this helps get you pointed in the right direction.
>
> Tom 2
>
>
> On Mon, Jun 8, 2015 at 1:07 AM, Mario Garrido <gaiarrido at gmail.com> wrote:
>
>> Dear list,
>> I am interesting in introduce in the same model these following groups of
>> variables
>> treatment*daytype*time*age
>> age*activity
>> time*activity
>> treatment*daytype+activity
>>
>> Is this the correct way to do it? or is redundant and I get spurious
>> results?
>> lme.mean7<-lmer(averageba~ treatment*daytype*time*age+age*activity+
>> time*activity+treatment*daytype+activity+(1|indiv), REML = FALSE)
>>
>> Thanks!
>>
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>>
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-- 
Ryan Hope, M.S.
CogWorks Lab
Cognitive Science Department
Rensselaer Polytechnic Institute