On Feb 17, 2010, at 5:32 PM, FishR wrote:
We are looking the extinction of a species of freshwater fish. The
logistic
regression was derived by scoring the anecdotal descriptions of the
species'
former population size (1 for a positive description of the
population e.g.
abundant, and 0 for a negative description e.g. scarce) and plotting
this
against time. Therefore it’s the population size relative to t=0. The
anecdotal evidence in not regular and therefore why I used a derived
measure
of the population.
We then have the predictor variables temperature, oxygen and river
modification for some of the 1800-2000 time period. Unfortunately
the data
is collected in bursts e.g. for the oxygen 1923-1938 and the
1954-1972, so
the missing data will not be random.
If you had large amounts of river-level data that spanned intervals
and ended with localized extinction events, I wonder if it might be
possible to do interval type "survival analysis"? One of the
formulation for coxph is to model a Surv object created with left and
right censoring. It "looks" like Surv(time1, time2, event). If you
have credible population counts over intervals, perhaps some sort of
expansion of those observations could put the data in an acceptable
form. (Unfortunately those very large gaps in the record make me a bit
dubious about the validity of results from that effort that would
generalize across a 200 year span.) Another formulation might be a
properly constructed Poisson regression. At least that framework is
easily configured to handle grouped data as might be found in surveys.
There are also real statisticians (I'm just an old county doctor)
reading this, and with yet a better description of the data resources
they might be able to comment on further alternatives. Another forum
for such a question might be the geospatial group.
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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