QIC for conditional logistic regression + GEE

Dear Timothy,

Thanks for the hint! I didn't know about the package "yags"... I was 
able to install it and use it in simple cases (glm), and it does 
provide a QIC!

However, it seems to be limited to the case of GLM (any family), but 
not Cox models... At least, I was unable to use it with a Cox model 
approach.

Last but not least, the routine that computes the QIC (pan.aic) is in 
C, not in R, and my skills in C are below zero.

Any idea?
Mathieu.


PS: I cc-ed back the list, since it could be useful there...




Le 09/11/2010 05:04, treid a ?crit :

Hi Mathieu,
Someone has probably already told you, but just in case, there is an R
package named yags that does QIC. Last I looked, you had to go to the
R-forge site to get it rather than cran.
tim.

----- Original Message -----
From: Mathieu Basille <basille at ase-research.org>
Date: Monday, November 8, 2010 16:32
Subject: [R-sig-eco] QIC for conditional logistic regression + GEE
To: r-sig-ecology at r-project.org

Dear list,

I'm currently trying to fit a conditional logistic regression on
correlated data (these are actually steps from animals, with 1
case and a bunch of random controls). The state of the art is to
use a GEE approach to estimate the variance of the coefficients,
using a coxph model by strata (composed of one step +
corresponding random steps), and a clustered estimation of the
variances (i.e. robust variances). This is quite easy to achieve
in R, with nevertheless some convergence problems...

The next step, however, is to select the best model within a set
of competing ones. Again, the state of the art is to use the
Quasi-likelihood under Independence Criterion (QIC) [1] also
sometimes known as modified AIC [2].

After an extensive search (with the help of rseek.org), I was
unable to find any guidance for this criterion using R. I know
the criterion is already available in SAS or Stata, but I was
wondering if anyone tried to code QIC in R? That would be a very
valuable tool in the R toolbox!

I was considering posting this question directly to the main R-
help, but decided to try it here first... Let me know if it is
the right move!

Sincerely,
Mathieu Basille.


[1] Pan, W. (2001a). Akaike's information criterion in
generalized estimating equations. Biometrics, 57, 120-125.

http://www.biostat.jhsph.edu/~fdominic/teaching/bio655/references/extra/QIC.biometrics.pdf 

[2] McCullagh, P., and Nelder, J. A. (1989). Generalized Linear
Models (2nd ed.). London:Chapman & Hall.

See an example in: A. H. M. M. Latif, M. Z. Hossain and M. A.
Islam: Model selection using modified Akaike's Information
Criterion: an application to maternal morbidity data. Austrian
Journal of Statistics, 37, 2008, 175-184.
http://www.stat.tugraz.at/AJS/ausg082/082Latif.pdf


--

~$ whoami
Mathieu Basille, Post-Doc

~$ locate
Laboratoire d'?cologie Comportementale et de Conservation de la Faune
+ Centre d'?tude de la For?t
D?partement de Biologie
Universit? Laval, Qu?bec

~$ info
http://ase-research.org/basille

~$ fortune
``If you can't win by reason, go for volume.''
Calvin, by Bill Watterson.

Dear Matthieu,

Henric Nilsson
(http://hisdu.sph.uq.edu.au/lsu/SSAI%20course/course_tools.htm) and
Casper Kraan
(http://www.waddenacademie.knaw.nl/fileadmin/inhoud/pdf/06-wadweten/Proefschriften/Thesis_Casper_Kraan.pdf)
provide the R code for the QIC. Maybe this might be useful to you.
Interestingly however is that the code of Nilsson gives another QIC
output as you get from the "yags package.

Kind regards,

Maarten

On 11/9/2010 4:50 PM, Mathieu Basille wrote:

Dear Timothy,

Thanks for the hint! I didn't know about the package "yags"... I was
able to install it and use it in simple cases (glm), and it does
provide a QIC!

However, it seems to be limited to the case of GLM (any family), but
not Cox models... At least, I was unable to use it with a Cox model
approach.

Last but not least, the routine that computes the QIC (pan.aic) is in
C, not in R, and my skills in C are below zero.

Any idea?
Mathieu.


PS: I cc-ed back the list, since it could be useful there...




Le 09/11/2010 05:04, treid a ?crit :

Hi Mathieu,
Someone has probably already told you, but just in case, there is an R
package named yags that does QIC. Last I looked, you had to go to the
R-forge site to get it rather than cran.
tim.

----- Original Message -----
From: Mathieu Basille <basille at ase-research.org>
Date: Monday, November 8, 2010 16:32
Subject: [R-sig-eco] QIC for conditional logistic regression + GEE
To: r-sig-ecology at r-project.org

Dear list,

I'm currently trying to fit a conditional logistic regression on
correlated data (these are actually steps from animals, with 1
case and a bunch of random controls). The state of the art is to
use a GEE approach to estimate the variance of the coefficients,
using a coxph model by strata (composed of one step +
corresponding random steps), and a clustered estimation of the
variances (i.e. robust variances). This is quite easy to achieve
in R, with nevertheless some convergence problems...

The next step, however, is to select the best model within a set
of competing ones. Again, the state of the art is to use the
Quasi-likelihood under Independence Criterion (QIC) [1] also
sometimes known as modified AIC [2].

After an extensive search (with the help of rseek.org), I was
unable to find any guidance for this criterion using R. I know
the criterion is already available in SAS or Stata, but I was
wondering if anyone tried to code QIC in R? That would be a very
valuable tool in the R toolbox!

I was considering posting this question directly to the main R-
help, but decided to try it here first... Let me know if it is
the right move!

Sincerely,
Mathieu Basille.


[1] Pan, W. (2001a). Akaike's information criterion in
generalized estimating equations. Biometrics, 57, 120-125.

http://www.biostat.jhsph.edu/~fdominic/teaching/bio655/references/extra/QIC.biometrics.pdf

[2] McCullagh, P., and Nelder, J. A. (1989). Generalized Linear
Models (2nd ed.). London:Chapman & Hall.

See an example in: A. H. M. M. Latif, M. Z. Hossain and M. A.
Islam: Model selection using modified Akaike's Information
Criterion: an application to maternal morbidity data. Austrian
Journal of Statistics, 37, 2008, 175-184.
http://www.stat.tugraz.at/AJS/ausg082/082Latif.pdf


--

~$ whoami
Mathieu Basille, Post-Doc

~$ locate
Laboratoire d'?cologie Comportementale et de Conservation de la Faune
+ Centre d'?tude de la For?t
D?partement de Biologie
Universit? Laval, Qu?bec

~$ info
http://ase-research.org/basille

~$ fortune
``If you can't win by reason, go for volume.''
Calvin, by Bill Watterson.

Dear Maarten,

Thank you for the links! They are very useful!

However, in both cases, they address the problem of QIC for GLMs, i.e.
for parametric models. This said, I was unable to adjust the code for
Cox models, which are semi-parametric, and use partial likelihood
instead (which should behave similarly). In the end, I have no idea how
to extract/compute the quasi partial likelihood for such a model, which
is required for the QIC...

I'm still struggling a lot about QIC on coxph, any additional hint would
be greatly appreciated!
Mathieu.


Le 10/11/2010 02:32, Maarten de Groot a ?crit :

Dear Matthieu,

Henric Nilsson
(http://hisdu.sph.uq.edu.au/lsu/SSAI%20course/course_tools.htm) and
Casper Kraan
(http://www.waddenacademie.knaw.nl/fileadmin/inhoud/pdf/06-wadweten/Proefschriften/Thesis_Casper_Kraan.pdf)

provide the R code for the QIC. Maybe this might be useful to you.
Interestingly however is that the code of Nilsson gives another QIC
output as you get from the "yags package.

Kind regards,

Maarten

On 11/9/2010 4:50 PM, Mathieu Basille wrote:

Dear Timothy,

Thanks for the hint! I didn't know about the package "yags"... I was
able to install it and use it in simple cases (glm), and it does
provide a QIC!

However, it seems to be limited to the case of GLM (any family), but
not Cox models... At least, I was unable to use it with a Cox model
approach.

Last but not least, the routine that computes the QIC (pan.aic) is in
C, not in R, and my skills in C are below zero.

Any idea?
Mathieu.


PS: I cc-ed back the list, since it could be useful there...




Le 09/11/2010 05:04, treid a ?crit :

Hi Mathieu,
Someone has probably already told you, but just in case, there is an R
package named yags that does QIC. Last I looked, you had to go to the
R-forge site to get it rather than cran.
tim.

----- Original Message -----
From: Mathieu Basille <basille at ase-research.org>
Date: Monday, November 8, 2010 16:32
Subject: [R-sig-eco] QIC for conditional logistic regression + GEE
To: r-sig-ecology at r-project.org

Dear list,

I'm currently trying to fit a conditional logistic regression on
correlated data (these are actually steps from animals, with 1
case and a bunch of random controls). The state of the art is to
use a GEE approach to estimate the variance of the coefficients,
using a coxph model by strata (composed of one step +
corresponding random steps), and a clustered estimation of the
variances (i.e. robust variances). This is quite easy to achieve
in R, with nevertheless some convergence problems...

The next step, however, is to select the best model within a set
of competing ones. Again, the state of the art is to use the
Quasi-likelihood under Independence Criterion (QIC) [1] also
sometimes known as modified AIC [2].

After an extensive search (with the help of rseek.org), I was
unable to find any guidance for this criterion using R. I know
the criterion is already available in SAS or Stata, but I was
wondering if anyone tried to code QIC in R? That would be a very
valuable tool in the R toolbox!

I was considering posting this question directly to the main R-
help, but decided to try it here first... Let me know if it is
the right move!

Sincerely,
Mathieu Basille.


[1] Pan, W. (2001a). Akaike's information criterion in
generalized estimating equations. Biometrics, 57, 120-125.

http://www.biostat.jhsph.edu/~fdominic/teaching/bio655/references/extra/QIC.biometrics.pdf

[2] McCullagh, P., and Nelder, J. A. (1989). Generalized Linear
Models (2nd ed.). London:Chapman & Hall.

See an example in: A. H. M. M. Latif, M. Z. Hossain and M. A.
Islam: Model selection using modified Akaike's Information
Criterion: an application to maternal morbidity data. Austrian
Journal of Statistics, 37, 2008, 175-184.
http://www.stat.tugraz.at/AJS/ausg082/082Latif.pdf


--

~$ whoami
Mathieu Basille, Post-Doc

~$ locate
Laboratoire d'?cologie Comportementale et de Conservation de la Faune
+ Centre d'?tude de la For?t
D?partement de Biologie
Universit? Laval, Qu?bec

~$ info
http://ase-research.org/basille

~$ fortune
``If you can't win by reason, go for volume.''
Calvin, by Bill Watterson.