I have started working on analyses of SNP's in the human genome, and I want to apply as many models as I can. But I don't know of any analogues to LASSO / ridge regression (or maybe even elastic net) with binary / trinary factors.

**P.S.** About my model.

Let $Y$ be dependent variable and $X_i$ – independent. Then $Y={0,1}$ and $X_i={0,1,2}$. But I always can split variable $X_i$ in this way: $X_i:=X_i'+X_i'$ which both is i.i.d. and take values ${0,1}$.

**Contents**hide

#### Best Answer

I am not sure if you are familiar with R, put the R package glmnet containts "extremely efficient procedures for fitting the entire lasso or elastic-netregularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model".

The general syntax to "Fit a generalized linear model via penalized maximum likelihood" would be:

` fit=glmnet(x,y,family="binomial") `

where x is your input matrix of independent variables, and y is your dependent variable (response variable). The binomial family would be for your binary dependent variable or family "multinomial" for a multinomial dependent variable.