Hi Daniel, Thank you so much for taking the time to explain this. I guess I am having trouble believing the results. condition x diff p=.001 condition p=.001 diff p=.001 When I run the ANOVA, I get more conservative results: summary(aov(value~condition*diff*rep + Error(subject/(condition*diff*rep)))) condition x diff p=2.311e-05 *** summary(aov(value~(condition*rep+diff*rep)+Error(subject/(condition*rep+diff*rep)))) condition p=0.02116 * diff p=2.2e-16 *** How do I stay conservative here? Best, Dave On Thu, May 20, 2010 at 11:38 AM, Daniel Ezra Johnson
<danielezrajohnson at gmail.com> wrote:
m0 = lme(value~condition+diff,random=~1|subject/rep) m1 = lme(value~condition*diff,random=~1|subject/rep) anova(m0,m1)
This will give you the p-value for the interaction since that's the only thing different between the two models. if you similarly compared lme(value~condition+diff...) to lme(value~condition...) that would be a test of "diff", whereas comparing lme(value~condition+diff...) to lme(value~diff...) that would be a test of "condition". there's more than one way to test these, but using anova() like this, i think is reasonable. of course, if you follow the mixed-model literature you'll see that people have shown these tests (likelihood-ratio tests) to be anti-conservative (p-values too high) when applied to mixed models.. Dan