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 <
kjetilbrinchmannhalvorsen@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 <jorismeys@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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>>
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>
>

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