I am fitting an elastic net with cross-validation and I am looking at how big the coefficients are for each predictor:
lambda <- cv.glmnet(x = features_training, y = outcomes_training, alpha = 0)
elnet <- lambda$glmnet.fit
coefs <- coef(elnet, s = lambda$lambda.min, digits = 3)
The coefs variable contains a dgCMatrix:
1
(Intercept) -1.386936e-16
ret 4.652863e-02
ind30 -2.419878e-03
spyvol 1.570406e-02
Is there a quick way to turn this into a dataframe with 2 columns (one for the predictor name and the other for the coefficient value)? as.data.frame, as.matrix or chaining both did not work. I would notably like to sort the rows according to the second column.
Another way, and no hacks through attributes() function, but extracting the rownames and matrix values. The attributes(class(coefs)) informs that dgCMatrix is a sparse matrix created using Matrix package.
data.frame( predict_names = rownames(coefs),
coef_vals = matrix(coefs))
# predict_names coef_vals
# 1 (Intercept) 21.117339411
# 2 (Intercept) 0.000000000
# 3 cyl -0.371338786
# 4 disp -0.005254534
# 5 hp -0.011613216
# 6 drat 1.054768651
# 7 wt -1.234201216
# 8 qsec 0.162451314
# 9 vs 0.771959823
# 10 am 1.623812912
# 11 gear 0.544171362
# 12 carb -0.547415029