Dear Rodolfo,
Sending the data helps, though if you had done what I suggested, you would have
seen what's going on:
-------------------- snip ------------------
> dim(data)
[1] 8 8
> summary(lm(response_variable ~ predictor_1 + predictor_2 + predictor_3 +
> predictor_4
+ + predictor_5 + predictor_6 + predictor_7, data = data))
Call:
lm(formula = response_variable ~ predictor_1 + predictor_2 +
predictor_3 + predictor_4 + predictor_5 + predictor_6 + predictor_7,
data = data)
Residuals:
ALL 8 residuals are 0: no residual degrees of freedom!
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.1905 NA NA NA
predictor_1yellow 2.4477 NA NA NA
predictor_2fora 6.5056 NA NA NA
predictor_2interior 6.0769 NA NA NA
predictor_3 0.6750 NA NA NA
predictor_4 3.0742 NA NA NA
predictor_5 0.6715 NA NA NA
predictor_6 -0.9850 NA NA NA
predictor_7 NA NA NA NA
Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: NaN
F-statistic: NaN on 7 and 0 DF, p-value: NA
-------------------- snip ------------------
So the data set that you're using has 8 cases and 8 variables, one of which is
a factor with 3 levels. Consequently, the model you're fitting my LS has 9
coefficients. Necessarily the rank of the model matrix is deficient. When you
eliminate a coefficient, you get a perfect fit: 8 coefficients fit to 8 cases
with 0 df for error.
This is of course nonsense: You don't have enough data to fit a model of this
complexity. In fact, you might not have enough data to reasonably fit a model
with just 1 predictor.
I'm cc'ing this response to the r-help email list, where you started this
thread.
Best,
John
On Sat, 28 Mar 2015 12:04:05 -0300
Rodolfo Pelinson <rodolfopelin...@gmail.com> wrote:
> Thanks a lot for your answer and your time! But Im still having the same
> problem.
>
> That's the script I am using:
> ____________________________________________________________________________________________________________________
> library(car)
>
> data <-read.table("data_vif.txt", header = T, sep = "\t", row.names = 1)
> data
>
> vif(lm(response_variable ~ predictor_1 + predictor_2 + predictor_3 +
> predictor_4 + predictor_5 + predictor_6 + predictor_7, data = data))
>
> vif(lm(response_variable ~ predictor_1 + predictor_2 + predictor_3 +
> predictor_4 + predictor_5 + predictor_6, data = data))
> ____________________________________________________________________________________________________________________
>
> the first vif function above returns me the following error:
>
> "Error in vif.default(lm(response_variable ~ predictor_1 + predictor_2 + :
> there are aliased coefficients in the model"
>
> Then if I remove any one of the predictors (in the script I removed
> predictor_7 as an example), it returns this:
>
> GVIF Df GVIF^(1/(2*Df))
> predictor_1 NaN 1 NaN
> predictor_2 NaN 2 NaN
> predictor_3 NaN 1 NaN
> predictor_4 NaN 1 NaN
> predictor_5 NaN 1 NaN
> predictor_6 NaN 1 NaN
> Warning message:
> In cov2cor(v) : diag(.) had 0 or NA entries; non-finite result is doubtful
>
>
> Can you help me with this? I even attached to this e-mail my data set. It's
> a small table.
>
> Sorry for the question.
>
>
>
> 2015-03-27 21:51 GMT-03:00 John Fox <j...@mcmaster.ca>:
>
> > Dear Rodolfo,
> >
> > It's apparently the case that at least one of the columns of the model
> > matrix for your model is perfectly collinear with others.
> >
> > There's not nearly enough information here to figure out exactly what the
> > problem is, and the information that you provided certainly falls short of
> > allowing me or anyone else to reproduce your problem and diagnose it
> > properly. It's not even clear from your message exactly what the structure
> > of the model is, although localizacao is apparently a factor with 3
> > levels.
> >
> >
> > If you look at the summary() output for your model or just print it, you
> > should at least see which coefficients are aliased, and that might help you
> > understand what went wrong.
> >
> > I hope this helps,
> > John
> >
> > -------------------------------------------------------
> > John Fox, Professor
> > McMaster University
> > Hamilton, Ontario, Canada
> > http://socserv.mcmaster.ca/jfox/
> >
> >
> > > -----Original Message-----
> > > From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Rodolfo
> > > Pelinson
> > > Sent: March-27-15 3:07 PM
> > > To: r-help@r-project.org
> > > Subject: [R] vif in package car: there are aliased coefficients in the
> > model
> > >
> > > Hello. I'm trying to use the function vif from package car in a lm.
> > However it
> > > returns the following error:
> > > "Error in vif.default(lm(MDescores.sitescores ~ hidroperiodo +
> > localizacao
> > > + : there are aliased coefficients in the model"
> > >
> > > When I exclude any predictor from the model, it returns this warning
> > > message:
> > > "Warning message: In cov2cor(v) : diag(.) had 0 or NA entries; non-finite
> > > result is doubtful"
> > >
> > > When I exclude any other predictor from the model vif finally works. I
> > can't
> > > figure it out whats the problem. This are the results that R returns
> > > me:
> > >
> > > > vif(lm(MDescores.sitescores ~ hidroperiodo + localizacao + area +
> > > profundidade + NTVM + NTVI + PCs...c.1.., data = MDVIF)) Error in
> > > vif.default(lm(MDescores.sitescores ~ hidroperiodo + localizacao +
> > > : there are aliased coefficients in the model
> > >
> > > > vif(lm(MDescores.sitescores ~ localizacao + area + profundidade + NTVM
> > > > +
> > > NTVI + PCs...c.1.., data = MDVIF))
> > > GVIF Df GVIF^(1/(2*Df))
> > > localizacao NaN 2 NaN
> > > area NaN 1 NaN
> > > profundidade NaN 1 NaN
> > > NTVM NaN 1 NaN
> > > NTVI NaN 1 NaN
> > > PCs...c.1.. NaN 1 NaN
> > > Warning message:
> > > In cov2cor(v) : diag(.) had 0 or NA entries; non-finite result is
> > doubtful
> > >
> > > Thanks.
> > > --
> > > Rodolfo Mei Pelinson.
> > >
> > > [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > > 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.
> >
> >
> > ---
> > This email has been checked for viruses by Avast antivirus software.
> > http://www.avast.com
> >
> >
>
>
> --
> Rodolfo Mei Pelinson.
------------------------------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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