On Apr 3, 2012, at 9:25 PM, Melrose2012 wrote:
I am trying to plot the logistic regression of a dataset (# of
living flies
vs days the flies are alive) and then fit a best-fit line to this
data.
Here is my code:
plot(fflies$living~fflies$day,xlab="Number of Days",ylab="Number of
Fruit
Flies",main="Number of Living Fruit Flies vs Day",pch=16)
alive <- (fflies$living)
dead <- (fflies$living[1]-alive)
glm.fit <- glm(cbind(alive,dead)~fflies$day,family="binomial")
summary(glm.fit)
lines(sort(fflies$day),fitted(glm.fit) [order (fflies$day)],col =
"red",lwd=2)
lines(glm.fit,col = "red",lwd=2)
My problem is that, while I am pretty sure that I did the 'glm'
command
correctly, when I try to plot this as a best-fit line on my data, it
does
not fit (clearly on a different scale somehow).
Can anyone enlighten me about what I am doing wrong? Should I be
scaling
one of these somehow? I've tried various types of scaling but
nothing plots
the line on top of the plot (no matter which I scale).
The parameter estimate is on the log(odds) scale. Two problems: A)
Your plot is not even on the odds scale, much less the log(odds)
scale. B) Your model assumes that the log(odds) for an event will
increase linearly with days. However your "event" == alive seems
reversed from what I would have expected it to be. I would have
expected "dead" within an interval to be the event that might increase
in probability as time increased. I also speculated that the problem
(which was not described at all) might better fit within the framework
of survival analysis rather than logistic regression?
--
David.
Thanks so much for whatever help you can give me!
Cheers,
Melissa
--
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David Winsemius, MD
West Hartford, CT
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