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Rodolfo Pelinson

2015-Mar-27 19:06 UTC

[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]]

John Fox

2015-Mar-28 00:51 UTC

head link

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

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-bounces at r-project.org] On Behalf Of Rodolfo
> Pelinson
> Sent: March-27-15 3:07 PM
> To: r-help at 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 at 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

John Fox

2015-Mar-28 18:17 UTC

head link

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

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 <rodolfopelinson at 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 <jfox at 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-bounces at r-project.org] On Behalf
Of Rodolfo
> > > Pelinson
> > > Sent: March-27-15 3:07 PM
> > > To: r-help at 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 at 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/

Rodolfo Pelinson

2015-Mar-30 12:53 UTC

head link

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

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!

2015-03-28 15:17 GMT-03:00 John Fox <jfox at mcmaster.ca>:
> 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 <rodolfopelinson at 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 <jfox at 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-bounces at r-project.org] On
Behalf Of
> Rodolfo
> > > > Pelinson
> > > > Sent: March-27-15 3:07 PM
> > > > To: r-help at 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 at 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/
>
>
>
>

-- 
Rodolfo Mei Pelinson.

	[[alternative HTML version deleted]]

R help - Mar 2015 - vif in package car: there are aliased coefficients in the model

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

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

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

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