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

Dear chip,

-----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?

If multinom() doesn't converge to a stable solution
after 1000 iterations,
it's probably safe to say that the problem is
ill-conditioned in some
respect. Have you looked at the covariance matrix of
the estimates?

Regards,
 John

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'))

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'))

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'))

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

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

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


      ##### binary logistic

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

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

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

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

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

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

Thanks

-----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 3:35 PM
To: John Fox
Cc: R-help at stat.math.ethz.ch
Subject: RE: [R] multinom and contrasts

Dear John,

My dataset has a response variable with 6 levels, and
12 independent variables, 10 of them are continuous variable, 
one is a categorical variable with 2 levels, the other one is 
also a categorical variable with 4 levels. total 206 
observations. I attached my dataset with the email "sample.txt".

library(MASS)
library(nnet)

sample<-read.table("sample.txt",sep='\t',header=T,row.names=1)
wts2<-sample$wts
sample<-sample[,-4]

options(contrasts=c('contr.helmert','contr.poly'))
obj1<-multinom(class~.,sample,weights=wts2,maxit=1000)
options(contrasts=c('contr.treatment','contr.poly'))
obj2<-multinom(class~.,sample,weights=wts2,maxit=1000)

predict(obj1,type='probs')[1:5,]
predict(obj2,type='probs')[1:5,]

Interestingly, if I change the values of the variable "bkgd" 
for 2 observations (from "a", to "f"), then I can get 
convergence with helmert contrast, but still not converged 
with treatment contrast:

sample$bkgd[201]<-'f'
sample$bkgd[205]<-'f'

options(contrasts=c('contr.helmert','contr.poly'))
obj1<-multinom(class~.,sample,weights=wts2,maxit=1000)

options(contrasts=c('contr.treatment','contr.poly'))
obj2<-multinom(class~.,sample,weights=wts2,maxit=1000)

predict(obj1,type='probs')[1:5,]
predict(obj2,type='probs')[1:5,]

appreciate any suggestions!

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

Dear chip,

-----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?

If multinom() doesn't converge to a stable solution after 1000 
iterations, it's probably safe to say that the problem is 
ill-conditioned in some respect. Have you looked at the covariance 
matrix of the estimates?

Regards,
 John

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