[R] Regression model for predicting ranks of the dependent variable

[Frank Harrell]

require(rms)
?orm            # ordinal regression model

For a case study see Handouts in 
http://biostat.mc.vanderbilt.edu/CourseBios330

Since you have lost the original values, one part of the case study will 
not apply: the use of Mean().

Frank
-------------
I have a dataset which has several predictor variables and a dependent 
variable, "score" (which is numeric). The score for each row is 
calculated using a formula which uses some of the predictor variables. 
But, the "score" figures are not explicitly given in the dataset. The 
scores are only arranged in ascending order, and the ranks of the 
numbers are given (like 1, 2, 3, 4, etc.; rank 1 means that the 
particular row had the highest score, 2 means it had the second highest 
score and so on). So, if the data has 100 rows, the output has ranks 
from 1 to 100.
I don't think it would be proper to treat the output column as a numeric 
one, since it is an ordinal variable, and the distance (difference in 
scores) between ranks 1 and 2 may not be the same as that between ranks 
2 and 3. However, most R regression models for ordinal regression are 
made for output such as (high, medium, low), where each level of the 
output does not necessarily correspond to a unique row. In my case, each 
output (rank) corresponds to a unique row.
So please suggest me what models I could use for this problem. Will 
treating the output as numeric instead of ordinal be a reasonable 
approximation? Or will the usual models for ordinal regression work on 
this dataset as well?