[R] factorial block design with missing data

[Guillaume Théroux Rancourt]

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