Once you have decided on the cutpoints, here is an old listing from 1997
that gives
a few references to help you get the theory right:
m.p.
_______________________________________________________________________________
Here are some that I know about. I'd also appreciate other references.
Altman, D.G., Lausen, B., Sauerbrei, W., and Schumacher, M.
Dangers of using "optimal" cutpoints in the evaluation of prognostic factors.
Journal of the National Cancer institute 86(11):829-835, 1994.
Hilsenbeck, S.G. and Clark, G.,M.
Practical p-value adjustment for optimally selected cutpoints.
Statistics in Medicine 15:103-112, 1996.
Kornblau, S.M., Thall, P.F., Huh, Y.O., Estey, E., and Andreeff, M.
Analysis of CD7 expression in acute myelogenous leukemia: martingale residual plots combined with 'optimal' cutpoint analysis reveals absence of prognostic significance. Leukemia 9:1735-1741, 1995.
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Judith Abrams [email protected]
Biostatistics & Research Epidemiology
Henry Ford Health Sciences Center
1 Ford Place/3E
Detroit, MI 48202-3450
Ph (313) 874-6408 Fax (313) 874-6730
_________________________________________________________________________________
Allen Buxton wrote:
You might try checking the command "egen"
egen group_varA = cut(varA) , group(x) label
this will group varA into x groups with approx. the
same frequency. Is this what you wish to do?
-Allen Buxton
--- tpolcyn <[email protected]> wrote:
Hello,
After many unfruitful hours perusing STATA manuals
and the Archives, I thought
it might be best to query the knowledgeable group on
the listserve.
One of my independent variables for my panel dataset
ranges from a scale of 1
to 7. My professor therefore suggested that I
investigate ologit and oprobit
models for my regressions. Unfortunately, my
dependent variables are
uncooperative in this respect because they all have
a range of more than 50
categories.
I have looked at a kdensity of my dependent
variables and used the tabulate
command for assistance in determining where to
impose cut points. I am aware
of "hordered" which performs a Hausman-type
specification test whether
categories in a ologit or oprobit model can be
joined or dropped. To this end
it estimates another ologit/oprobit model with a
collaped response.
My question though is how do I go about grouping my
data? How do I divide my
data into for example three ordered groups? I think
I am missing some basic
STATA manipulation....
Many thanks,
Tania Polcyn
Master's of Development Economics
Dalhousie University
Halifax, Canada
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