From: David Duffy <David.Duffy_at_qimr.edu.au>

Date: Wed 07 Sep 2005 - 12:39:29 EST

y>=3 -14.336 6.287 -2.28 0.0226

dose 3.160 1.399 2.26 0.0239

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Sep 07 12:49:03 2005

Date: Wed 07 Sep 2005 - 12:39:29 EST

liu abc <liu2074@yahoo.com> wrote:

*>
*

> I am using proportional odds model for ordinal responses in

*> dose-response experiments. For some samll data, SAS can successfully
**> provide estimators of the parameters, but the built-in function polr()
**> in R fails. Would you like to tell me how to make some change so I
**> can use polr() to obtain the estimators? Or anyone can give me a hint
**> about the conditions for the existance of MLE in such a simple case?
**> By the way, for the variable "resp" which must be ordered factor, how
**> can I do it? Thanks a lot.
**>
**> Guohui
*

> The following is one example I used both in SAS and R.

*>
**> in R:
**>
**> library(MASS)
**> dose.resp = matrix( c(1,1,1,1,2,2,2,3,3,3, 2,2,3,3,4,4,5,4,5,5), ncol=2)
**> colnames(dose.resp)= c("resp", "dose")
**> polr( factor(resp, ordered=T)~dose, data=dose.resp)
**> #Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) :
**> # initial value in 'vmmin' is not finite
*

It seems to be the starting values. Using lrm() from the Design package gave

*> dose.resp <- as.data.frame(dose.resp)
**> dose.resp$resp <- factor(dose.resp$resp)
**> library(Design)
**> lrm(resp ~ dose, data=dose.resp)
*

Obs Max Deriv Model L.R. d.f. P C Dxy 10 6e-06 11.43 1 7e-04 0.909 0.818 Gamma Tau-a R2 Brier 0.931 0.6 0.768 0.014 Coef S.E. Wald Z P y>=2 -10.904 5.137 -2.12 0.0338

y>=3 -14.336 6.287 -2.28 0.0226

dose 3.160 1.399 2.26 0.0239

and giving polr starting values:

*> print(m1 <- polr(resp ~ dose, data=dose.resp, start=c(-1, -4, 3)))
*

Call:

polr(formula = resp ~ dose, data = dose.resp, start = c(-1, -4,

3))

Coefficients:

dose

3.158911

Intercepts:

1|2 2|3

10.90172 14.33296

Residual Deviance: 10.34367

**AIC: 16.34367
**
Even then, summary(m1) gives the same problem (as it refits). There is
separation in the data, of course, but I presume the ordinality gives
some extra information.

David Duffy.

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Sep 07 12:49:03 2005

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