multinom and contrasts

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch 
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of array chip
Sent: Thursday, April 14, 2005 1:00 PM
To: John Fox
Cc: R-help at stat.math.ethz.ch
Subject: RE: [R] multinom and contrasts

Dear John,

Thanks for the answer! In my own dataset, The
multinom() did not converge even after I had tried to 
increase the maximum number of iteration (from default 100 to 
1000). In this situation, there is some bigger diffrenece in 
fitted probabilities under different contrasts (e.g. 
0.9687817 vs. 0.9920816). My question is whether the analysis 
(fitted probabilities) is still valid if it does not 
converge? and what else can I try about it?

Thank you!




--- John Fox <jfox at mcmaster.ca> wrote:

Dear chip,

The difference is small and is due to computational error.

Your example:

max(abs(zz[1:10,] - yy[1:10,]))

[1] 2.207080e-05

Tightening the convergence tolerance in multinom() eliminates the
difference:

options(contrasts=c('contr.treatment','contr.poly'))

xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),],

reltol=1.0e-12)
# weights:  20 (12 variable)
initial  value 91.495428
iter  10 value 91.124526
final  value 91.124523
converged

yy<-predict(xx,type='probs')
options(contrasts=c('contr.helmert','contr.poly'))

xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),],

reltol=1.0e-12)
# weights:  20 (12 variable)
initial  value 91.495428
iter  10 value 91.125287
iter  20 value 91.124523
iter  20 value 91.124523
iter  20 value 91.124523
final  value 91.124523
converged

zz<-predict(xx,type='probs')
max(abs(zz[1:10,] - yy[1:10,]))

[1] 1.530021e-08

I hope this helps,
 John

--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch 
[mailto:r-help-bounces at stat.math.ethz.ch] On

Behalf Of array chip

Sent: Wednesday, April 13, 2005 6:26 PM
To: R-help at stat.math.ethz.ch
Subject: [R] multinom and contrasts

Hi,

I found that using different contrasts (e.g.
contr.helmert vs. contr.treatment) will generate

different

fitted probabilities from multinomial logistic

regression

using multinom(); while the fitted probabilities

from binary

logistic regression seem to be the same. Why is

that? and for

multinomial logisitc regression, what contrast

should be

used? I guess it's helmert?

here is an example script:

library(MASS)
library(nnet)

      #### multinomial logistic

options(contrasts=c('contr.treatment','contr.poly'))

xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),])

yy<-predict(xx,type='probs')
yy[1:10,]

options(contrasts=c('contr.helmert','contr.poly'))

xx<-multinom(Type~Infl+Cont,data=housing[-c(1,10,11,22,25,30),])

zz<-predict(xx,type='probs')
zz[1:10,]


      ##### binary logistic

options(contrasts=c('contr.treatment','contr.poly'))

obj.glm<-glm(Cont~Infl+Type,family='binomial',data=housing[-c(

1,10,11,22,25,30),])

yy<-predict(xx,type='response')

options(contrasts=c('contr.helmert','contr.poly'))

obj.glm<-glm(Cont~Infl+Type,family='binomial',data=housing[-c(

1,10,11,22,25,30),])

zz<-predict(xx,type='response')

Thanks