- 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. There seems to be various methods for handling missing data, so I would like to know:
- Does LASSO impose any restriction in terms of the method of imputation that I can use?
- What would be the best bet for imputation method? Ideally, I need a method that I could run on SPSS (preferably) or R.
UPDATE1: It became clear from some of the answers below 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 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.
- 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?
This is a follow-up on a previous question of mine (How to select a subset of variables from my original long list in order to perform logistic regression analysis?).
OBS: I am not a statistician.