Thank you all for the very fast answers.
My proportions come from a factor analysis on a number of binary variables,
in order to avoid having to fit 12 logistic regressions on the same dataset.
By scaling the obtained scores to 0 and 1, I get weighted averages of the
response combinations I'm interested in.
I tried the betareg function, but that one can't deal with probabilities 0
and 1 unfortunately. I'll have to manually do the logit transformation, I'm
afraid. Thanks for the help.
Kind regards
Joris
On Tue, Mar 24, 2009 at 8:48 PM, Kjetil Halvorsen
kjetilbrinchmannhalvor...@gmail.com wrote:
You did'nt say how your proportions have arisen! If each corresonds to one
observation, you could simply simulate
indicator variables with those proportions as prob's, fit glm, repeat many
times, and
average results!
More seriously, you could transform the proportions to logits
logit - log(p/(1-p))
and fit a linear regression.
Kjetil
On Tue, Mar 24, 2009 at 3:30 PM, joris meys jorism...@gmail.com wrote:
Dear all,
I have a dataset where I reduced the dimensionality, and now I have a
response variable with probabilities/proportions between 0 and 1. I wanted
to do a logistic regression on those, but the function glm refuses to do
that with non-integer values in the response. I also tried lrm, but that
one
interpretes the probabilities as different levels and gives for every
level
a different intercept. Not exactly what I want...
Is there a way to specify that the response variable should be interpreted
as a probability?
Kind regards
Joris
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