Logistic Regression
Alternatively you might use log(p/1-p) as your dependent variable and use OLS with robust standard errors. Much of your inference would be analogous to a logistic regression John C Frain 3 Aranleigh Park Rathfarnham Dublin 14 Ireland www.tcd.ie/Economics/staff/frainj/home.html mailto:frainj at tcd.ie mailto:frainj at gmail.com
On 23 January 2016 at 20:46, David Winsemius <dwinsemius at comcast.net> wrote:
On Jan 23, 2016, at 12:41 PM, pari hesabi <statistics84 at hotmail.com>
wrote:
Hello everybody, I am trying to fit a logistic regression model by using glm() function
in R. My response variable is a sample proportion NOT binary numbers(0,1). So multiply the sample proportions (and 1-proportions) by the number of samples, round to integers, you will have an appropriate response variable and complements, and you can fit a binomial model.
Regarding glm() function, I receive this error: non integer # successes
in a binomial glm!
I would appreciate if anybody conducts me.
Regards,
Pari
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-- David Winsemius Alameda, CA, USA
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