Hi
I have a huge array with series of data. For each cell in the array I
fit a linear model, either using lm() or gls()
with lm() there is no problem, but with gls() I get an error:
Error in glsEstimate(glsSt, control = glsEstControl) :
computed gls fit is singular, rank 2
as soon as there are data like this:
> y1 <- c(0,0,0,0)
> x1 <- c(0,1,1.3,0)
> gls(y1~x1)
Error in glsEstimate(glsSt, control = glsEstControl) :
computed gls fit is singular, rank 2
of course, this is not a problem for lm()
> lm(y1~x1)
Call:
lm(formula = y1 ~ x1)
Coefficients:
(Intercept) x1
0 0
I know, that such data does not make "sense" but it is possible, that
something like this occurs in my data-set. Since I call gls() for every
cell of my array in a loop, I don't want such errors to occur, since
this breaks my loop.
what is the problem here? What are potential solutions?
Many thanks
Christoph
--
Christoph Lehmann <[EMAIL PROTECTED]>
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