I am using lme4 to fit a mixed effects model to my data. I have a significant
interaction between two variables. My question is what is the correct way to
get p-values for single terms involved in that interaction.
I have been using stepwise backwards deletion and model comparisons to get
p-values,and refitting the model using a REML approach to get
estimates.However, presumably to get the p values for single terms, I also have
to remove the interaction as well, and therefore inaccurate.
I have confused myself with this now, as to whether in this case you should
compare a model with the interaction and the single term of interest removed to
the minimum adequate model (in which case the p values are over inflated for
the single terms), or whether to remove the interaction from the minimum
adequate model, and then compare this to an updated model, with the single term
removed.
This is an example of what the model would look like:
library(lme4)
minadequatemodel<-lmer(sq_rate~(day+temp+brood_size+weight+weight:brood_size+(1|ident),data=prov,REML=FALSE)
##to get p values for e.g. temp
pvalmodtemp<-update(minadequatemodel,~.+temp)
anova(modelfin,modeltemp)
###but what's the correct way to get p value for brood_size or weight?
Your help would be greatly appreciated...thanks!
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