Hi Daniel,

Thanks for the info., I read the wiki link and it made sense

Chibisi

On Tue, Jul 8, 2008 at 1:42 AM, Daniel Malter <daniel@umd.edu> wrote:

> If that is so, i.e. x1=-x2, then they do not convey different meaning and
> cannot be estimated. Think about it that way you leave the house 8 hours
> after midnight. This is identical to saying that you leave the house 16
> hours before midnight. This conveys the exact (!) same information and
> neither measure is better than the other. Therefore you do not gain
> anything
> by including both. You need variation in the measures so that both can be
> meaningfully estimates. Be aware though that even if there is variation,
> but
> when this variation is marginal, then your model may suffer from
> "multicollinearity" and you may find "weird" results (e.g. unexpected,
> "crazy" coefficients; "wrong" signs on your coefficients; insignificance
> when you would expect significance). Then excluding one of the regressors
> may still be necessary because despite their variation (i.e. x1 is slightly
> different from -x2), the difference in information convey by them is
> marginal. Multicollinearity violates the model assumptions of OLS.
>
> http://en.wikipedia.org/wiki/Multicollinearity
>
> Cheers,
> Daniel
>
> -------------------------
> cuncta stricte discussurus
> -------------------------
>
> -----Ursprüngliche Nachricht-----
> Von: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org] Im
> Auftrag von Chibisi Chima-Okereke
> Gesendet: Monday, July 07, 2008 8:09 PM
> An: r-help@r-project.org
> Betreff: [R] Problems with lm()
>
> Dear all,
>
> I am trying to fit a multiple linear regression model to a table of data.
> My
> data.frame is like this ...
>
> fit.data <- data.frame(y, x1, x2, x3, x4, x5, x6), then I use the linea
> regression command ...
>
> lm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6, data = fit.data)
>
> however, for some tables the data in column x1 is equal to -x2, so I get NA
> values for both coefficients of x1 and x2. I need to have real fitted
> coefficients for all the parameters or the physical meaning of the
> parameters is lost. Is there any way of forcing R to fit all the
> parameters?
> I have seen the contrast option but I don't really understand it (I am not
> a
> statistician) so I would be greatful if anyone could explain that.
>
> Kind Regards
>
> Chibisi
>
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