Dear all,
This question may be too basic quesition for this list, but if someone has
time to answer I will be happy. I have tried to find out, but haven't found
a consice answer.
As an example I use "Pinheiro, J. C. & Bates, D. M. 2000. Mixed-effects
models in S and S-PLUS. Springer, New York." page 225, where rats are fed
by 3 different diets over time, which body mass has been measured.
Response: Body mass, fixed effects Time*Diet, random effect ~Time|Rat. The
main question is if the interaction term is significant (i.e. growth rate).
My question is could I also look at the p-values of the main effects to say
if body mass increase significant with body mass?
>From Pinheiro, J. C. & Bates, D. M. (2000)
Fixed effects: weight ~Time * Diet
Value St.error DF t-value p-value
Intercept 251.60 13.068 157 19.254 <.0001
Time 0.36 0.088 13 4.084 0.0001
Diet2 200.78 22.657 13 8.862 <.0001
Diet3 252.17 22.662 157 11.127 <.0001
TimeDiet2 0.60 0.155 157 3.871 0.0002
TimeDiet3 0.30 0.156 157 1.893 0.0602
As stated by Pinheiro, J. C. & Bates, D. M. (2000), the growth rate of diet
2 (TimeDiet2) differs significantly from diet 1. Allthoug could I from this
also say that body mass increase significantly with time for diet 1? Like
this: f(x) = 251.60 (+/-13.068) + 0.36 x (+/- 0.088), t = 4.084, p =
0.0001? I have seen different places that it people claiming that when the
interaction is significant then it is wrong to interpret p-values for the
main effects. Is it more proper to split the data and run the test (weight
~Time) for each diet seperately, when looking at the simple effect of time
on body mass?
Best regards Ron
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