# [R] logistic regression asymptote problem

From: Kevin J Emerson <kemerson_at_darkwing.uoregon.edu>
Date: Wed 06 Jul 2005 - 06:08:29 EST

I have a question about logistic regressions.

Consider a case where you have binary data that reaches an asymptote that is not 1, maybe its 0.5. Can I still use a logistic regression to fit a curve to this data? If so, how can I do this in R. As far as I can figure out, using a logit link function assumes that the asymptote is at y = 1.

An example. Consider the following data:

where x is the independent variable, and yes and no are counts of events. plotting the data you can see that the data seem to reach an asymptote at around y=0.5. using glm to fit a logistic regression it is easily seen that it does not fit well.

tmp.glm <- glm(cbind(yes,no) ~ x, data = tmp, family = binomial(link = logit))
plot(tmp.glm\$fitted, type = "l", ylim = c(0,1)) par(new=T)
plot(tmp\$yes / (tmp\$yes + tmp\$no), ylim = c(0,1))

Any suggestions would be greatly appreciated.

Cheers,
Kevin

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Kevin J Emerson
Center for Ecology and Evolutionary Biology
1210 University of Oregon
University of Oregon
Eugene, OR 97403
kemerson@darkwing.uoregon.edu

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