I saw a standard overdispersed binomial. In particular, I saw NO
evidence of saturation at 0.5 or anything below 1. I did the following:
tmp$N - tmp$yes+tmp$no
with(tmp, plot(x, yes))
with(tmp, plot(x, yes/N))
tmp.glm - glm(cbind(yes,no) ~ x, data = tmp, family = binomial(link
=logit))
tmp.glmq - glm(cbind(yes,no) ~ x, data = tmp, family =
quasibinomial(link =logit))
summary(tmp.glm)
summary(tmp.glmq)
plot(tmp.glm)
plot(tmp.glmq)
# Test the statistical significance of the Dispersion parameter
pchisq(summary(tmp.glmq)$dispersion*12, 12, lower=FALSE)
hope this helps.
spencer graves
Kevin J Emerson wrote:
R-helpers,
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:
tmp -
structure(list(x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14), yes = c(0, 0, 0, 2, 1, 14, 24, 15, 23, 18, 22, 20, 14, 17
), no = c(94, 101, 95, 80, 81, 63, 51, 56, 30, 38, 31, 18, 21,
20)), .Names = c(x, yes, no), row.names = c(1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), class =
data.frame)
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
--
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA
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