R help - Sep 2012 - Logit regression, I observed different results for glm or lrm (Design) for ordered factor variables

Dear useR's,

I was comparing results for a logistic regression model between different 
library's.

themodel formula is arranged as follows: 

response ~  (intercept) +  value +  group

OR:
glm( response ~  (intercept) +  value +  group , 
family=binomial(link='logit'))
lrm( response ~  (intercept) +  value +  group )
ROC( from = response ~  (intercept) +  value +  group , plot='ROC')

the response is a binary vaiable, 
the independent predictor 'value' is a continuous variable,
and the grouping factor is a ordered factor (with 5 levels 
(25,50,100,200,400))

When I compare the GLM model with the ROC model  and the LRM model setting 
 'group'  as factor variable,
 the resulting coefficients are similar to eachother.

When I set 'group' as an ordered factor variable (as it should be) the
GLM
model with the ROC model coefficients are still comparable,
but the LRM coefficients are completely different.

I have looked up the Design package, and there is a function 'cr.setup',
which sets up an ordinal logistic response, this is however not the case 
here.
the model hase a binary response  (0 or 1), a continuos predicter and a 
ordered grouping factor.

Does anybody know what I am doing wrong ?

Thanks for you time,
Tom






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