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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