# [R-sig-ME] MCMCglmm multinomial logit

Mon Aug 27 19:23:41 CEST 2012

```Hi Jackson,

If x1 is the fixed effect prediction for the log contrast B-A then

plogis(x1/sqrt(1+v1*c2))

gives the expected probability of being B rather than A after
integrating over the random effects (in your case the residuals under
the assumption that they have unit variance v1=1). You can write it
another way:

Have nu1 = x1+e1

plogis(x1/sqrt(1+v1*c2)) \approx  int plogis(nu1)pnorm(e1,0,sqrt(v1))de

and we can see the approximation is pretty good:

x1<-2
c2<-0.345843
v1<-1

plogis(x1/sqrt(1+v1*c2))
integrate(function(e1){plogis(x1+e1)*dnorm(e1,0,sqrt(v1))}, -Inf,Inf)

Obtaining the expected probability of being B rather than A, or C
rather than A, is therefore relatively straightforward.

Obtaining the expected probability of B rather than C is a bit more tricky.

take nu2 = log contrast C-A

nu1-nu2 = log contrast B-C

which is

x1+e1-x2+e2

and is distributed as N(x1-x2, V[1,1]+V[2,2]-2*V[1,2]) where in you case V=R.

We can therefore use the same approximation as before replacing x1
with x1-x2 and v1 with V[1,1]+V[2,2]-2*V[1,2].

If we chose V = IJ  then V[1,1] = V[2,2] = V[1,1]+V[2,2]-2*V[1,2] and
so we multiply c2 by the same constant for all contrasts. This was the
main motivation. In fact, it might make more sense to scale IJ by
1/(j-1) rather than 1/j so all contrasts have unit variance, but
anything could be used really as long as you are careful.

Looking at it now, the justification that these results/approximations
extend to the case A versus (B and C) as I suggest in the CourseNotes
seems more tenuous and I will check it out.

Cheers,

Jarrod

Quoting Jackson King <jackson.king722 at gmail.com> on Sun, 26 Aug 2012
09:28:45 -0500:

> Hello,
>
> I am attempting to fit a multinomial logit model (3 categories) with random
> effects (across states). I have attempted to follow the advice in the
> course notes, but am a little uncertain the reason for the priors on the
> residuals, and how this is used to calculate predicted probabilities. I
> would like my covariates to predict each outcome -- cov1 predicting option3
> versus option1 and cov1 predicting option2 versus option1, rather than a
> main effect.
>
> Here is a simple version of my model
>
> j<-length(levels(data\$char))
>
> I<-diag<-(j-1) #2x2 identity matrix
>
> J=matrix(rep(1, (j-1)^2), c(j-1, j-1)) #2x2 Unit Matrix
>
> IJ <- (1/3) * (diag(2) + matrix(1, 2, 2)) #Residual covriance matrix
>
> prior = list(R = list(V =IJ, fix = 1), G = list(G1 = list(V = diag(2), nu =
> 0.002))) #Why can't I use V=diag(2) for prior R?
>
>
> model<- MCMCglmm(char~-1+trait*(cov1), random=~idh(trait):state, rcov = ~us(
> trait):units, data=data, family = "categorical", prior = prior, verbose =
> TRUE)
>
>
> I find that the model converges, and the coefficients look similar to
> maximum likelihood, but then I would like to predict probabilities for
> being in each category. Typically, I think this is done by
> plogis(x/sqrt(1+c2)), so why is it necessary to multiply by the delta
> matrix (course notes p. 97)? Alternatively, if I simply use a 2x2 diagonal
> matrix for the prior for R, shouldn't I be able to use the same
> transformation -- plogis(x/sqrt(1+c2)). In short, I am a little confused
> about the IJ matrix and where it comes from. Is there a quick answer, or
> another paper that explains this? And (2) is it reasonable to predict
> probabilities from my model, based on fixed values of cov1, using this
> simple transformation above... plogis(x/sqrt(1+c2))
>
> Many thanks.
>
> Jackson
>
> 	[[alternative HTML version deleted]]
>
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>
>

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