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
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.
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.
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)