I am trying to model wildlife survival data (multiple encounter records
for 117 individual cougars over 9 years). I have divided the data into
several "groupings" based on varying hunting pressure and geographic
location. I wish to use Cox modeling and post estimation AIC (estat ic)
to test which grouping best fits the mortality patterns observed. For
instance, all areas were hunted heavily for the first 3 years (group 1)
at which point one area was protected (group 2) while hunting continued
in the remainder of the study area (group 3). A second model would test
if dividing the study animals into 6 groups (i.e. 2 geographic areas, 3
time periods) provides a better fit. Each group is coded as a string
variable.
The outputs I receive from STATA suggest that several groups are
collinear resulting in no values of Standard Error and erroneous hazard
ratios. For instance, in the above model group 3 and group 1 are
collinear. With group 1 coded as the dummy variable, STATA provides a
hazard ratio of 0.99 and no standard error, z, or p-value. I'm not sure
if I can trust the AIC values I receive from post estimation.
Can anyone suggest how I should approach this collinearity issue?
Hugh S Robinson Ph.D.
Postdoctoral Researcher
Wildlife Biology Program
College of Forestry and Conservation
University of Montana
Missoula, MT 59802
(406) 243-4128
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