My situation:
- small sample size: 116
- binary outcome variable
- long list of explanatory variables: 44
- explanatory variables did not come from the top of my head; their choice was based on the literature.
- most cases in the sample and most variables have missing values.
Approach to feature selection chosen: LASSO
R's glmnet package won't let me run the glmnet routine, apparently due to the existence of missing values in my data set. It became clear from some of the answers to my previous question that I have do deal with more basic issues before considering imputation methods. I would like to add here new questions regarding that. On the the answer suggesting the coding as constant value and the creation of a new variable in order to deal with 'not applicable' values in continuous variables and the usage of group lasso:
Would you say that if I use group LASSO, I would be able to use the approach suggested to continuous predictors also to categorical predictors? If so, I assume it would be equivalent to creating a new category - I am wary that this may introduce bias. If that is not advisable, what would be in the case of categorical variables?
Does anyone know if R's glmnet package supports group LASSO? If not, would anyone suggest another one that does that in combination with logistic regression? Several options mentioning group LASSO can be found in CRAN repository, any suggestions of the most appropriate for my case? Maybe SGL?
OBS: I am not a statistician.
