Hello!
I have read somewhere (somehow, I can't seem to find it again, it's been a
couple of months) that when analyzing factorial block design, the position
where you put the block factor is important, even more when there are missing
values.
I understand that when using anova.lm, the order is sequential, so that if I
want to check for a treatment effect, I should put my blocking factor before in
order to . It's just that I got confused with all the answers from previous
posts and books, and I don't know if the missing values are being handled
properly.
My code is:
P.biom = lm(biomass ~ Bloc + Trt*Clone, data=mydata)
P.aov = anova.lm(P.biom, test="F")
> anova.lm(P.ar.2, test="F")
Analysis of Variance Table
Response: M_aerien
Df Sum Sq Mean Sq F value Pr(>F)
Bloc 2 139.7 69.9 0.4054 0.6710
Trt 1 31069.5 31069.5 180.2905 6.227e-13 ***
Clone 7 1206.2 172.3 0.9999 0.4544
Trt:Clone 7 570.3 81.5 0.4728 0.8450
Residuals 25 4308.2 172.3
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Thank you very much.
Guillaume Théroux Rancourt
Ph.D. candidate --- Plant Biology
Université Laval, Québec, QC, Canada
guillaume.theroux-rancourt.1 at ulaval.ca
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