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
multinom and contrasts
2 messages · array chip, John Fox
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
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