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 <[EMAIL PROTECTED]> 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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org 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. > > [[alternative HTML version deleted]]
______________________________________________ R-help@r-project.org 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.
