By trial and error I have discovered that it works if I don't use the formula
interface in combination with a covariance matrix as input.
If the covariance matrix has the dependent variable as its left-most
variable as the relaimpo documentation suggests, then the relaimpo package
will run by simply naming the covariance matrix as the first object in the
call and not using a formula. The downside of this is needing to create
different covariance matrices for different models.
The following will work:
# calculate covariance matrix from survey respondent data using pairwise
deletion
covmatrx =
cov(respdata[,c("V0007","V0029","V0031","V0032","V0034","V0035","V0036")],
use = "pairwise")
# try the lmg method of relative importance
> imps1 = calc.relimp(covmatrx, type="lmg", rela=TRUE)
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