Post-tests to consider in mixed models

Question

I have a mixed model of the form y~condition + Replicate + Error(Replicate) but at the moment, and am running into difficulties in performing a post-hoc test.

I know there is no best option and it entirely depends on experimental design and the characteristics of my data, so I am hoping to find out a few options that I should investigate and attempt to implement.

In the past I have used Tukey Kramer and Dunnet using R and glht i.e. glht(model, linfct=mcp(Group="Dunnett")) but at this time I have to stay with aov() which unfortunately the glht function can't accept as in input model, and aov() can't apply more than a single layer of variables to it's multiple comparisons i.e. can't use this multiple comparison to account for my random effect.

1. If anyone knows of a multiple comparisons package that can do a post-hoc test compatible with the aov() model I would love to give it a try.

2. If anyone can recommend some post-tests that can be used to make my p-value a little less conservative I would also be interested to investigate it. The usual p-value adjustment tests don't seem to be quite suitable because of the semi-continuous nature of the point sampling (These aren't quite discrete groups which makes the length variable impossible to determine. ANOVA P-value adjustments for semi-continuous data)

Answer 1

The lsmeans package does support aovlist objects, but you should do something like options(contrasts="contr.sum","contr.poly") before fitting the model, to ensure the dummy variables for factors sum to zero. This is necessary because the intercept is put in a separate stratum and we want the rest of the effects to be orthogonal to it. You can then do something like

library(lsmeans)
( mod.lsm = lsmeans(model, "condition") )
contrast(mod.lsm, "trt.vs.ctrl")

If your data are unbalanced, though, I'd suggest you install the lme4 package, and fit your model using lmer with a model like y ~ condition + (1 | Replicate), which is the analogous model. The above statements still work exactly as shown, without the need to change the contrasts options. In the contrast call, there are several other options such as "pairwise" in case you don't want Dunnett style contrasts.