R help - Mar 2006 - Clustering over strata using a Cox proportional hazard model

Hi all,
 
I wish to do build discrete choice model to analyse habitat selection of
wolverines.
This can be done with a 'tricked' stratified Cox proportional hazard
model.
For each individual animal each selected position, and possible
alternative non-used available positions are combined into strata.
This means that one stratum contains a set of 1 used position and
several positions which were available to the animal but were not
selected.
Ultimately this renders unique choice sets for all observations in the
dataset.
The stratified model works fine and does its work as should.
The problem however is that, having checked the residuals, there is a
high variation between individuals.
Is it somehow possible to account for preferences that vary among
individuals? I am thinking along the lines of clustering the data over
the strata or using specific individual weights. I have looked into the
cluster() function, but this does not result in any differences in the
residuals. If using the cluster() function is the right way to take, how
can I check if it worked to remove individual preferences?
 
I hope anyone can help me with this,
 
Thankes in advance,
 
Roel May
 
 
Roel May
Norwegian Wolverine Project
Norwegian Institute for Nature Research (NINA)
Tungasletta 2, N-7485 Trondheim, Norway
Tlf. +47 73 80 14 65, Mob. +47 95 78 59 95
Email roel.may at nina.no <mailto:roel.may at nina.no> 
Internett www.nina.no <http://www.nina.no/> , www.jerv.info
<http://www.jerv.info/>

On Fri, 24 Mar 2006, May, Roel wrote:
> Hi all,
>
> I wish to do build discrete choice model to analyse habitat selection of
> wolverines.
> This can be done with a 'tricked' stratified Cox proportional
hazard
> model.
> For each individual animal each selected position, and possible
> alternative non-used available positions are combined into strata.
> This means that one stratum contains a set of 1 used position and
> several positions which were available to the animal but were not
> selected.
> Ultimately this renders unique choice sets for all observations in the
> dataset.
> The stratified model works fine and does its work as should.
You could also have used clogit(), which does exactly this.
> The problem however is that, having checked the residuals, there is a
> high variation between individuals.
> Is it somehow possible to account for preferences that vary among
> individuals? I am thinking along the lines of clustering the data over
> the strata or using specific individual weights. I have looked into the
> cluster() function, but this does not result in any differences in the
> residuals. If using the cluster() function is the right way to take, how
> can I check if it worked to remove individual preferences?
cluster() affects only the standard error computations.  It uses the 
Huber/White "sandwich" standard error estimates, which consistently 
estimate the actual standard error of your estimate. You can think of them 
as a bargain-basement version of bootstrapping.

In some situations this is all you need, because the correlation does not 
prevent a sensible interpretation of your regression coefficients.  In 
other cases you want a different model that will give different regression 
coefficients.


 	-thomas


Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle