[R] vif in package car: there are aliased coefficients in the model

[Rodolfo Pelinson]

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.

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[John Fox]

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/

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[John Fox]

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 <rodolfo...@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.

> --
> Rodolfo Mei Pelinson.

------------------------------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

[Rodolfo Pelinson]

Thanks a lot for the answer and I'm sorry for the silly question!

Also thanks for the conceptual advice! It was also a doubt of me and my
advisors.


Best!