Dear R users,

I am trying to analyze multivariate abundance data using the mvabund
package, but errors occur for several functions.

I created a mvabund object named faunadat consisting of 8 dependent
variables each containing 64 positive integer values (bact, fung, plant,
omn, pred, orib, meso, spring). There are 4 factors: harv, rep, depth and
lit.
I then created a formula:

>
mvform<-mvformula(faunadat~harv*rep*lit+harv*rep*depth+harv*lit*depth+rep*lit*depth)

When I try to create a mean-variance plot, I get the following error. I
have checked and all the factors are the same length as the dependent
variables.

> meanvar.plot(mvform)
Error in default.meanvar.plot.mvformula(x = x, n.vars = n.vars, var.subset
= var.subset,  :
  the dimensions of the variables do not match

When I fit a multivariate linear model, there is no problem. All the
following statements give the desired result.

> lm<-manylm(mvform)
> anova(lm,nBoot=500)
> plot(lm)

However, because these are count data, I am interested in fitting a
generalized linear model instead, but then I get following error:

> glm<-manyglm(mvform,family="negative.binomial")
> anova(glm,nBoot=500)
Error in XvarIn[nterms - i, varseq > i] <- 0 :
  (subscript) logical subscript too long

Also, when trying to create diagnostics plots, I get the following error.

> plot(glm)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In default.plot.manyglm(x, which = which, caption = caption, overlay =
overlay,  :
  You have passed an invalid number of variables 'n.vars' to be included in
the plot. All variables will be included instead.
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf

I know the manual says "The function is not yet available for manyglm
object", but on the other hand in one of the examples provided in that same
manual a residuals vs fitted values plot can be created for a manyglm
object:

> tasm.cop.nb <- manyglm(tasm.cop ~ block*treatment,
family="negative.binomial")
> plot(tasm.cop.nb)

The design is unbalanced, I'm not sure whether that is part of the problem.

Any help would be highly appreciated,

MLV.

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