both lrm() and polr() now run successfully, and they both deal with appyear by dropping some values of the factor.The str() command is also awesome and this is what my data now looks like (with some of the non-relevant variables omitted).My DV, 'Outcome' is indeed ordinal and after making it a factor(), I also made it ordered().

Factors are awesome and I can't believe I didn't notice them earlier.

I'm pretty new to R, but loving the freedom so far. The error doesn't go away even with penalty=100. Setting it to 1 or 5 changes the error such that it only names one of the variables as offending. I found a suggestion here to set the penalty parameter of lrm to a small positive value. I understand that including all options of a dummy variable over-defines the model, but I get the error whether I include all 11 years or just 10. Offending variable(s):ДЄdmityear2009 admityear2000 admityear1999Д®rror in lrm(Outcome ~ relGPA + mcAvgGPA + Interview_Z + WorkHistory_years + : I get the error singular information matrix in lrm.fit (rank= 22 ). (sorry for all of the other random variables, but I don't want to introduce confusion by changing my code) M4 <- lrm(Outcome ~ relGPA + mcAvgGPA + Interview_Z + WorkHistory_years + GMAT + UGI_Gourman + admityear1999 + admityear2000 + admityear2001 + admityear2002 + admityear2003 + admityear2004 + admityear2005 + admityear2006 + admityear2007 + admityear2008 + admityear2009, data=fsd)

The lrm model is specified as follows library(Design) I created the dummy variables with car:recode in this manner (one statement like this for each of the 11 years) fsd$admityear2000 <- recode(fsd$ApplicationYear ,'2000=1 else=0') Next I want to add dummy variables for each of the years represented in my dataset. Rounding errorsmay lead to small but non-zero singular values in a rank deficient matrix, hence the rank of a matrix determined by the number of non-zero singular values is sometimes called effective rank. My model works great with my first set of predictors. TherankofAequals the number of non-zero singular values which is the same as the number of non-zero diagonal elements in. I am running an ordinal logistic regression in R and running into trouble when I include dummy variables.

Singular matrix