I am analyzing from a very simple experiment.
I have measured plants of two different colours (yellow and purple) in 9
different populations.
So, I have two different factors : a fixed effect (Colour with two
levels) and a random one (Population with 9 levels).
I first analyzed the data with the aov function
LargS is the variable
aov(formula = LargS ~ Col + Error(Col/Pop))
Terms:
Col
Sum of Squares 3.440351
Deg. of Freedom 1
Estimated effects are balanced
Stratum 2: Col:Pop
Terms:
Residuals
Sum of Squares 3017.112
Deg. of Freedom 16
Residual standard error: 13.73206
Stratum 3: Within
Terms:
Residuals
Sum of Squares 3347.385
Deg. of Freedom 302
To test for the interaction Col*Pop, I used the following F-ratio =
(3017/16)/(3347/302) = 188. Highly significant !
Now, let's go to the analysis performed by lmer - First I do the linear
model without the Col*Pop interaction :
m3=lmer(LargS ~ Col + (1 | Pop)
And next with the interaction : m2=lmer(LargS ~ Col + (Col | Pop))
Comparing both models : anova(m2,m3) :
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
m3 3 1710.67 1721.97 -852.33
m2 5 1714.59 1733.43 -852.30 0.0746 2 0.9634
=> Conclusion : the interaction Col*Pop is not significant !
I guess I am missing something.
Who can help ?
Eric
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