R help - May 2008 - Missing coefficient on a glm object

Hello guys, i looked over the archive files and found nothing about this
kind of error.

I have a database of 33 elements described in 8 variables, i'm using the
Leave-One-Out iterative process
to take one of the elements to be the test element and make a regression
with the other 32 and then
I try to predict the clas of the element out.

 I'm using this call as a part of a Leave-One-Out experiment:

glmresult <- glm(Y ~ x1+x2+x3+x4+x5+x6+x7+x8, family=binomial,
control=glm.control(epsilon = 1e-8, maxit = 100, trace = FALSE))

but when i look at the coefficients with:

coef(glmresult)

Everything goes well (i get all the coefficients) until the 27th element
which gets a NA as a coefficient to variable x8

The vectors Y, x1,x2,x3,x4,x5,x6,x7,x8 have the same length and there isn't
any missing data.

don't know what to do right now as i already reviewed the code a lot and
i'm
starting to think the problem is in the
data.


Diego Cesar

	[[alternative HTML version deleted]]

On Tue, 13 May 2008, Diego Cesar wrote:
> Hello guys, i looked over the archive files and found nothing about this
> kind of error.
It's a feature.
> I have a database of 33 elements described in 8 variables, i'm using
the
> Leave-One-Out iterative process
> to take one of the elements to be the test element and make a regression
> with the other 32 and then
> I try to predict the clas of the element out.
>
> I'm using this call as a part of a Leave-One-Out experiment:
>
> glmresult <- glm(Y ~ x1+x2+x3+x4+x5+x6+x7+x8, family=binomial,
> control=glm.control(epsilon = 1e-8, maxit = 100, trace = FALSE))
>
> but when i look at the coefficients with:
>
> coef(glmresult)
>
> Everything goes well (i get all the coefficients) until the 27th element
> which gets a NA as a coefficient to variable x8
>
> The vectors Y, x1,x2,x3,x4,x5,x6,x7,x8 have the same length and there
isn't
> any missing data.
>
> don't know what to do right now as i already reviewed the code a lot
and i'm
> starting to think the problem is in the
> data.
Correct, collinearity aka extrinsic aliasing.  x8 is not linearly 
independent of (1, x1 ... x7) on your dataset and so is dropped from the 
model.
> Diego Cesar
>
> 	[[alternative HTML version deleted]]
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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