[Rd] anova.mlm (single-model case) does not handle factors? (PR#8679)

[Peter Dalgaard]

Yves.Rosseel at ugent.be writes:

> Full_Name: Yves Rosseel
> Version: 2.2.1
> OS: i686-pc-linux-gnu
> Submission from: (NULL) (157.193.116.152)
> 
> 
> Dear developers,
> 
> For the single-model case, the anova.mlm() function does not seem to handle
> multi-parameter predictors (eg factors) correctly. A toy example illustrates the
> problem:
> 
> Y <- cbind(rnorm(100),rnorm(100),rnorm(100))
> A <- factor(rep(c(1,2,3,4), each=25))
> fit <- lm(Y ~ A)
> anova.mlm(fit)
> 
> gives
> 
> Error in T %*% ss[[i]] : non-conformable arguments
> 
> I think the problem lies in the computation of the 'ss' terms (line 237 in the
> file mlm.R in the source code). Changing this (and the following line) by
> something similar to what is done in summary.manova seems to resolve the
> problem:
> 
>  comp <- as.matrix(fit$effects)[seq(along=asgn), ,drop=FALSE]
>  uasgn <- unique(asgn)
>  nterms <- length(uasgn)
>  ss <- list(nterms)
>  df <- numeric(nterms)
>  for(i in seq(nterms)) {
>     ai <- (asgn == uasgn[i])
>     ss[[i]] <- crossprod(comp[ai, ,drop=FALSE])
>     df[i] <- sum(ai)
>  }

Thanks for pointing this out. Amazing that it hasn't cropped up before...

The culprit seems to be the line

ss <- lapply(split(comp, asgn), function(x) crossprod(t(x)))

in which "comp" (an effects by responses matrix) is effectively turned
into a vector by split(). And, adding insult to injury, had you
actually gotten a matrix result, you wouldn't want to transpose it
before calculating the cross products.

A simpler, but slightly dirty fix is to set 

ss <- lapply(split.data.frame(comp, asgn), crossprod)

(the dirtiness is that split.data.frame actually does the right thing
for matrices; possibly, an identically defined split.matrix() would be
cleaner, although I'm never quite happy with matrix functions that
aren't symmetric in rows/columns.)

-- 
   O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
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