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.



> mn.year<-multinom(summercategory~n.year + (1|elkid), data=elk, Hess=T)



# weights:  20 (12 variable)

initial  value 907.722983

iter  10 value 734.516684

final  value 733.300162

converged



> summary(mn.year)



Call:

multinom(formula = summercategory ~ n.year + (1 | elkid), data = elk,

    Hess = T)



Coefficients:

        (Intercept)       n.year 1 | elkidTRUE

GTNPCEB   0.6748545  0.005700504     0.6748545

GTNPS    -1.0455490  0.115835869    -1.0455490

TW       -0.1948539 -0.002288967    -0.1948539

YNP       0.4134208 -0.037711158     0.4134208



Std. Errors:

        (Intercept)     n.year 1 | elkidTRUE

GTNPCEB   0.1338889 0.01291928     0.1338889

GTNPS     0.2444979 0.01964817     0.2444979

TW        0.1889981 0.01843526     0.1889981

YNP       0.1478354 0.01548316     0.1478354



Residual Deviance: 1466.6

AIC: 1482.6



We get comparable results for the effect of year from all three models
we’ve run. What we’re concerned about is the fact that 59 individuals only
have a single observation, and 43 only have 2. It seems like the estimation
of weights wouldn’t work because of this and we may be asking too much
trying to use a mixed-effect model with this data?



Thanks,



Jeff

	[[alternative HTML version deleted]]

