Hello,

I'm working on some multinomial models - and some reading on these methods
left me a little confused on how to generate response-scale data in
situations were I have both intercepts and slopes in the model. In a simple
intercept-only model (3 categories A,B,C in the response) I could do
(assuming R prior for the multinomial suggested by J.Hadfield in his
MCMCglmm Course Notes):

c <- sqrt(1+c2)
prediction <- exp( x/c )/(exp( x/c )+1)

where is one of my intercepts for A and B, and

prediction <- exp(( x_A-x_B )/c )/(exp( x_A-x_B )/c ) +1)

(of course I could also use plogis())

does it simply extends to slopes (e.g. I've included the cov continuous
variable)?

Y <- x/c + slope/c * cov
prediction <- exp(Y)/(exp(Y)+1)

Y_C <- (x_A-x_B)/c+(slope_A-slope_B)/c * cov
prediction <- exp(Y_C) / (exp(Y_C) +1)

?

Cheers
Szymek




*Dr Szymon Drobniak*
Anthropological Institute and Museum
Office Y42-K-66
University of Zürich - Irchel
Winterthurerstrasse 190
CH-8057 Zürich, Switzerland

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