I am running an experiment where I get repeated measures over time from ~10 subjects. Because the effects of time aren't constant, I am treating timepoints categorically. So my model looks like:
Model<-lmer(data, Response ~ Categorical.Time + (1|Animal.Name))
Now when I do
I get this (made up values)
Estimate Std. Error df t value Pr(>|t|) (Intercept) 10 2 10 20 1.00E-06 Day1 4 2 10 1 0.3 Day2 6 3 10 2 0.06 Day3 3 2 10 0.18 0.2 Day4 5 2 10 3 0.01*
Now, I figured I would also have to run an anova to get an overall effect of time on my response variable.
anova(Model), then take the p-value from that
A couple questions:
assuming the anova of the overall effect of time is NOT significant, should I disregard any individually significant timepoints? My thought is yes, to ignore anything if the overall time effect is ns. On the other hand, if the anova is significant I can report that the overall effect of time is significant, and Day4 specifically has a positive association with my response variable.
- Second question: is anything else horribly wrong, from what I've written here?
Many thanks for any advice!
(teaching myself so please forgive basic errors)