Compute a repeated measures model in lme4

lme1<-lmer(O2.intake4days~1+(1|indiv),REML=FALSE)
lme2<-lmer(O2.intake4days~1+(daytype|indiv),REML=FALSE)
lme3<-lmer(O2.intake4days~1+(time|indiv),REML=FALSE)
lme4<-lmer(O2.intake4days~1+(time+daytype|indiv),REML=FALSE) I compute

anova(lme1,lme2,lme3,lme4)

What a complete review of my study! Thanks very much. I got open Zuur
(2007) in this moment.

Mario

2015-06-08 21:38 GMT+03:00 Philippi, Tom <tom_philippi at nps.gov>:

The draft R-sig-mixed FAQ has some guidance on testing random effects
(and LRT via anova are not recommended):
http://glmm.wikidot.com/faq

Be careful.  In my applications of repeated measures to ecological data,
one model or the other for random effects is justified by the structure of
the sampling or experiment, and by the question of interest, not by
parsimony.

Also, if your O2 measurements have cyclic/periodic responses to time of
day, at the least I urge you to spend quality time with papers or books,
such as Faraway's "Extending the linear model" or Wood's "Generalized
additive models" or perhaps one of Zuur's, to fully understand the
differences between treatments in the data that are estimated or tested by
different models.

Ecologically, you may be more interested in specific parameters about the
O2 consumption: integrated 24hr consumption, estimated peak consumption,
shifts in time of peak consumption, rate of ramping up of consumption (your
rate of change of O2 might be a ramping up or ramping down following some
exertion).

Tom 2

On Mon, Jun 8, 2015 at 11:18 AM, Mario Garrido <gaiarrido at gmail.com>
wrote:

sorry, I reply without finishing my comments.
I am trying to compare the rate of change in O2 consumption in 2
consecutive days after a treatment (some individuals are treated while
others do not) days. This is my treatment variable.
daytype variable got 2 levels. the day before treatment and the day
after treatment
age variable are either juveniles or adults and time is time of teh day,
dark and night.

As I comparing the O2 consumption between day before and after. Random
effect should be  1|individual) or (1+time|individual)?


I always got the doubt.

thanks!

2015-06-08 21:12 GMT+03:00 Mario Garrido <gaiarrido at gmail.com>:

This is really very useful, also what you tell about the random effect.
I just wondering about it right now.

Thanks very much. I will look at with detail and get back here if
needed.



2015-06-08 20:53 GMT+03:00 Philippi, Tom <tom_philippi at nps.gov>:

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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