Hey, everyone,
 
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")
dose.resp
#> dose.resp
#      resp dose
# [1,]    1    2
# [2,]    1    2
# [3,]    1    3
# [4,]    1    3
# [5,]    2    4
# [6,]    2    4
# [7,]    2    5
# [8,]    3    4
# [9,]    3    5
#[10,]    3    5
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
#In addition: Warning message:
#fitted probabilities numerically 0 or 1 occurred in: 
#glm.fit(X, y1, wt, family = binomial(), offset = offset) 
 
in SAS
NOTE: PROC LOGISTIC is fitting the cumulative logit model. The probabilities
      modeled are summed over the responses having the lower Ordered Values in
      the Response Profile table.
NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: There were 10 observations read from the data set WORK.ONE.


		
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