Missing coefficient on a glm object

[Klaus (Diego)]

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]]

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[Prof Brian Ripley]

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]]
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

--
Brian D. Ripley,                  ripley@...
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  [phone] (self)
1 South Parks Road,                     [phone] (PA)
Oxford OX1 3TG, UK                Fax:  [phone]

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and provide commented, minimal, self-contained, reproducible code.