glmnet error for logistic regression/binomial

Question

I get this error when trying to fit glmnet() with family="binomial", for Logistic Regression fit:

> data <- read.csv("DAFMM_HE16_matrix.csv", header=F)
> x <- as.data.frame(data[,1:3])
> x <- model.matrix(~.,data=x)
> y <- data[,4]

> train=sample(1:dim(x)[1],287,replace=FALSE)

> xTrain=x[train,]
> xTest=x[-train,]
> yTrain=y[train]
> yTest=y[-train]

> fit = glmnet(xTrain,yTrain,family="binomial")

Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  : 
one multinomial or binomial class has 1 or 0 observations; not allowed

Any help would be greatly appreciated - I've searched the internet and haven't been able to find anything that helps

EDIT:

Here's what data looks like:

> data
          V1       V2    V3      V4
1   34927.00   156.60 20321  -12.60
2   34800.00   156.60 19811  -18.68
3   29255.00   156.60 19068    7.50
4   25787.00   156.60 19608    6.16
5   27809.00   156.60 24863   -0.87
...
356 26495.00 12973.43 11802    6.35
357 26595.00 12973.43 11802   14.28
358 26574.00 12973.43 11802    3.98
359 25343.00 14116.18 11802   -2.05

Answer 1

I think it is because of the levels of your factor variable. Suppose there are 10 levels and your 1 level has only one record, try to remove this level. You can use drop levels from gdata package.

Answer 2

This is generally because of data structure and their response variable, sometimes the response has more than binary output. or the data response variable has binary out come, but they have much more one class from the other and we may called them most probably class imbalance problem. Therefore the problem then occur during training and testing the data. So, you must convert the response variable into binary if there are more than two outcomes, 2nd you may apply multinomial as respect to binomial. Hope this can help you.