I ran a model using
model <- glmer(Number~Year*Treatment(1|Year/Treatment), data=data,family=poisson)
family=gaussian(link = "log"),
na.action = na.pass,verbose=FALSE)
Number1 <- log(Number)
tl;dr your diagnostic plots look OK to me, you can probably proceed to interpret your results.
might not be quite right (besides the missing
+ between the terms above ...) In general you shouldn't include the same term in both the random and the fixed effects (although there is one exception - if
Year has more than a few values and there are multiple observations per year you can include it as a continuous covariate in the fixed effects and a grouping factor in the random effects - so this might be correct).
glmmPQLif your data aren't Normal" is really shorthand for "you might want to use a GLMM if your data aren't Normal". Your
glmermodel should be fine.
log(1+x)transformations for count data are reasonable - but, again, unnecessary here.
anova()in this context does a likelihood ratio test, which is a reasonable way to compare models.