[R] factorial block design with missing data

[Ben Bolker]

Guillaume Théroux Rancourt writes:

> 
> 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")
> 

  [snip]

  Sorry to make things even more complicated, but *if* you
are dealing with a large number of missing values you might
want to consider using the lme() function (in the nlme package),
along with its companion book by Pinheiro and Bates (2000);
the methods used in that package should be more robust to
lack of balance than lm/aov ...


  Ben Bolker