[R-sig-ME] Multinomial mixed-effect model in nnet

[Martin Maechler]

Can you  PLEASE  stop spamming the  r-sig-mixed-models
mailing list ?

Your question ... now the  FOURTH  time, unchanged AFAICS,
seems almost completely unrelated to the topics of this
SIG (= Special Interest Group) mailing list...
and that's why nobody has answered till now.

Maybe rather use R-help,
[ but first really change what you post: 
  Give much less R output of multinom() but possibly more detailed
  description of your data design...
  --> Read the posting guide,... which also mentions how to ask questions
      that do evoke answers]

Regards,
Martin Maechler

>>>>>     on Mon, 10 Dec 2012 17:00:53 -0700 writes:

    > Hello,
    > We’re exploring temporal change in summer range selection in radio-collared
    > elk during a 3 decade period. Elk are marked on the wintering grounds and
    > tracked back to one of 5 summer ranges. There are 180 individuals; most (n
    > = 121) individuals were monitored >=2 years, resulting in a total of 546
    > summer range observations from the 180 elk. We’ve run multinomial
    > regression models with year as a predictor in packages ‘mlogit’ and ‘nnet’
    > using only the last summer range choice for each individual (n = 180):



    > m2.year<-multinom(summercategory~n.year, data=elk, Hess=T)

    > # weights:  15 (8 variable)

    > initial  value 289.698824

    > iter  10 value 237.592576

    > final  value 237.060236

    > converged

    >> summary(m2.year)

    > Call:

    > multinom(formula = summercategory ~ n.year, data = elk, Hess = T)



    > Coefficients:

    > (Intercept)       n.year

    > GTNPCEB   1.1306086  0.008673583

    > GTNPS    -2.7164924  0.128711359

    > TW       -0.4452291  0.007482690

    > YNP       0.8210785 -0.042043091



    > Std. Errors:

    > (Intercept)     n.year

    > GTNPCEB   0.3936527 0.01869625

    > GTNPS     0.9689580 0.03476211

    > TW        0.5450637 0.02550461

    > YNP       0.4229157 0.02325437



    > Residual Deviance: 474.1205

    > AIC: 490.1205



    > We’ve also run a mixed-effect model in nnet using the individual as a
    > random effect to account for the longitudinal data of repeated observations
    > through time for some individuals with all 546 observations.

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    > Thanks,

    > Jeff

    > [[alternative HTML version deleted]]

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