Dear Christoph,
I don't see how what you suggest can work in a mixed-effects model.
In the case you originally raised, of independent observations, you should be
able to recover the coefficients for the multinomial logit model by fitting the
logits that I suggested in my earlier email, but not
Dear John and R-helpers,
Thanks for your replies that were both very helpful.
The reason I was asking is that I´m searching for an easier way to
incorporate *random effects* in a multinomial model.
I was hoping that *combinations of binomial glmmPQL or lmer calls* might
be able to do the job
Charles Berry ucsd.edu> writes:
>
> Scherber, Christoph gwdg.de> writes:
>
> >
> > Dear all,
> >
> > I am trying to express a multinomial GLM (using nnet) as a series of GLM
> models.
[deleted]
>
> Doing the obvious comparison:
>
> ggen.preds <-
> sapply( levels(multicats),
>
Dear Christoph,
If I understand correctly what you've done, the two approaches are not
equivalent and should not in general produce the same fitted probabilities.
Letting {a, b} represent logit(a vs. b) = log(Pr(a)/Pr(b)) and {ab, cd}
represent logit(a or b vs. c or d), and numbering the respon
Scherber, Christoph gwdg.de> writes:
>
> Dear all,
>
> I am trying to express a multinomial GLM (using nnet) as a series of GLM
models.
>
> However, when I compare the multinom() predictions to those from GLM, I
see differences that I can´t
> explain. Can anyone help me out here?
>
> Here com
Dear all,
I am trying to express a multinomial GLM (using nnet) as a series of GLM models.
However, when I compare the multinom() predictions to those from GLM, I see
differences that I can´t
explain. Can anyone help me out here?
Here comes a reproducible example:
##
# set up data: (don´t care
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