Contents
What does the AOV command do with a lmer model?
The idea behind aov is that the expected mean squares for irrigation is a function of σ 2, σ f 2 and the irrigation effects. The expected mean square for field residuals is a function of σ 2 and σ f 2. When the irrigation effects are 0, both of these quantities estimate the same thing and their ratio follows an F distribution.
When to use ANOVA over fitting two models?
When the irrigation effects are 0, both of these quantities estimate the same thing and their ratio follows an F distribution. Interestingly, Bates and Pinheiro recommend using the ANOVA over fitting two models and doing a likelihood ratio test. The latter tends to be anti-conservative.
What’s the difference between ANOVA and summary function?
My understanding is that the anova function should test whether any of my groups differs from the intercept, whereas the summary function displays the significance of the deviance of individual groups from the intercept.
Which is ANOVA output shows the omnibus test?
I believe the anova () output shows the omnibus test, while the summary () function shows regression coefficients that represent specific contrasts, which are defined by the reference group (i.e., whatever level is first). Origin:Fert is showing the omnibus interaction term significance.
How to use LME and lmer in R?
Using lme from the nlme package, the code would be Using lmer from the lme4 package, the code would be something like These threads from R-help may be helpful (and to give credit, that’s where I got the nlme code from). This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.
How to use LME for repeated measures ANOVA?
I’m trying to use lme from the nlme package to replicate results from aov for repeated measures ANOVAs.
Why does LME and lmer from nlme not agree?
Also note that lme from nlme does not compute the denominator degrees of freedom correctly, so the F-statistics agree but not the p-values, and lmer from lme4 doesn’t try too because it’s very tricky in the presence of unbalanced crossed random effects, and may not even be a sensible thing to do. But that’s more than I want to get into here.