Hi Thierry,
You're very right and I was very wrong. It's a warning indeed, and the
outcome is close to what I become if I calculate the logit transformation
myself (although not exactly the same, but I guess that has to do with the
correction the logit function does when p=0 or p=1).
I should have paid more attention to the course of categorical ;-)
Kind regards
Joris
On Wed, Mar 25, 2009 at 10:32 AM, ONKELINX, Thierry
thierry.onkel...@inbo.be wrote:
Hi Joris,
glm() handles proportions but will give you a warning (and not an error)
about non-integer values. So if you get an error then there should be
something wrong with the syntax, model or data. Can you provide us with
a reproducible example?
Cheers,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
Namens joris meys
Verzonden: dinsdag 24 maart 2009 20:30
Aan: R-help Mailing List
Onderwerp: [R] modelling probabilities instead of binary data with
logisticregression
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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