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I am trying to do a proportional odds logistic regression model of the form:

dsnac <- polr(formula=DS1~AC1, data = pddat1, method=c("logistic"))
summary(dsnac)

The regression ran fine,however, when I implement the summary function I get an error:

svd(X) : infinite or missing values in 'x'

I checked to see if there are any missing values in the "AC1" column (assuming AC1 is "x" as mentioned in the error), but does not have any values missing. The range of AC1 is 1.3 to 170000. DS1 is a factor having the levels 0,1 and 2.

Would be a great help if someone can help me with this. Thanks

A reproducible example is:

pddat1 <- data.frame(cbind(DS1=c(rep(0,400),rep(1,60),rep(2,40)),
 AC1=runif(500,1,170000)))
pddat1$DS1 <- as.factor(pddat1$DS1)
dsnac <- polr(formula=DS1~AC1, data = pddat1, method=c("logistic")) 

summary(dsnac)

A simple transformation solved the issue. svd(X) refers to singular value decomposition of covariates matrix.

dsnac <- polr(DS1~scale(AC1) , data = pddat1, method=c("logistic")) 
summary(dsnac)

However, it is something has to do with your data. Calling clm function from ordinal package lead to the same conclusions with a warning such as "Model is nearly unidentifiable: very large eigenvalue - Rescale variables?"

library(ordinal)
dsnac <- clm(as.factor(DS1) ~ AC1, data=pddat1)
summary(dsnac)

If you downsize the maximum value in the runif command everything works fine

pddat1 <- data.frame(cbind(DS1=factor(c(rep(0,400),rep(1,60),rep(2,40))),
 AC1=runif(500,1,15)))
str(pddat1)
pddat1$DS1 <- as.factor(pddat1$DS1)
dsnac <- polr(DS1 ~ AC1, data = pddat1, method=c("logistic")) 
summary(dsnac)