Extracting coefficient variable names from glmnet into a data.frame

Question

I would like to extract the glmnet generated model coefficients and create a SQL query from them. The function coef(cv.glmnet.fit) yields a \'dgCMa

Answer 1

Assuming you know how to obtain your lambda, I found two different ways to show the predictors needed in the selected model for that particular lambda. One of them includes the intercept. The lambda can be obtained using cross-validation by the mean of cv.glmnet from "glmnet" library. You might want to only look at the last lines for each method:

 myFittedLasso = glmnet(x=myXmatrix, y=myYresponse, family="binomial")
 myCrossValidated = cv.glmnet(x=myXmatrix, y=myYresponse, family="binomial")
 myLambda = myCrossValidated$lambda.1se  # can be simply lambda

 # Method 1 without the intercept
 myBetas = myFittedLasso$beta[, which(myFittedLasso$lambda == myLambda)]
 myBetas[myBetas != 0]
 ## myPredictor1    myPredictor2    myPredictor3
 ##   0.24289802      0.07561533      0.18299284


 # Method 2 with the intercept
 myCoefficients = coef(myFittedLasso, s=myLambda)
 dimnames(myCoefficients)[[1]][which(myCoefficients != 0)]
 ## [1] "(Intercept)"    "myPredictor1"    "M_myPredictor2"    "myPredictor3"

 myCoefficients[which(myCoefficients != 0)]
 ## [1] -4.07805560  0.24289802  0.07561533  0.18299284

Note that the example above implies a binomial distribution but the steps can be applied to any other kind.

Answer 2

Building on Mehrad's solution above, here is a simple function to print a table containing only the non-zero coefficients:

print_glmnet_coefs <- function(cvfit, s="lambda.min") {
    ind <- which(coef(cvfit, s=s) != 0)
    df <- data.frame(
        feature=rownames(coef(cvfit, s=s))[ind],
        coeficient=coef(cvfit, s=s)[ind]
    )
    kable(df)
}

The function above uses the kable() function from knitr to produce a Markdown-ready table.