Thank you Berend and Mark,
It seems pretty clear, the problem is with the numbers and not with R.
Intuitively, I didn't think that regressing on Y on X would give different
results than regressing Y on X - C (where C is a constant). So, I thought
that R was doing something strange with rounding. However, I think it has
more to do with the fact that the range of X is dwarfed when a sufficiently
large constant is introduced.
The one thing that made me think that R was doing something "wrong" was
that the slope function in excel worked in both cases. So, that's why I
posted the question rather than thinking that I was doing something wrong.
I appreciate your references to the wikipeida articles, those will be
helpful. I want to spend a little more time thinking about this later on.
Also, thanks for the code to illustrate your point (both Bererd and Mark).
I think I might just use this function for now to approximate the simple
slope function in excel:
slope = function(x,y) {
ret = sum((x-mean(x))*(y-mean(y)))/sum((x-mean(x))^2)
return(ret)
}
On Sat, Apr 14, 2012 at 7:40 AM, Berend Hasselman <b...@xs4all.nl> wrote:
>
> On 13-04-2012, at 22:20, Gene Leynes wrote:
>
> > I can't figure out why this is returning an NA for the slope in one case,
> > but not in the other.
> >
> > I can tell that R thinks the first case is singular, but why isn't the
> > second?
> >
> > ## Define X and Y
> > ## There are two versions of x
> > ## 1) "as is"
> > ## 2) shifted to start at 0
> > y = c(58, 57, 57, 279, 252, 851, 45, 87, 47)
> > x1 = c(1334009411.437, 1334009411.437, 1334009411.437, 1334009469.297,
> > 1334009469.297, 1334009469.297, 1334009485.697, 1334009485.697,
> > 1334009485.697)
> > x2 = x1 - min(x1)
> >
> > ## Why doesn't the LM model work for the "as is" x?
> > lm(y~x1)
> > lm(y~x2)
>
>
> With your data the matrix t(X)%*%X is extremely ill-conditioned for the
> case with x1.
> See
>
> http://en.wikipedia.org/wiki/Condition_number
> http://mathworld.wolfram.com/ConditionNumber.html
> http://en.wikipedia.org/wiki/Numerical_methods_for_linear_least_squares
>
> You can check it with
>
> makeXX <- function(x) {
> matrix(data=c(x,rep(1,length(x))),nrow=length(x), byrow=FALSE)
> }
>
> X1 <- makeXX(x1)
> (XTX1 <- t(X1) %*% X1)
> svd(XTX1)
>
> and similar for x2.
>
> Berend
>
>
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