adaptive lasso in r
require(glmnet)
## Data = considering that we have a data frame named dataF, with its first column being the class
x <- as.matrix(dataF[,-1]) # Removes class
y <- as.double(as.matrix(dataF[, 1])) # Only class
## Ridge Regression to create the Adaptive Weights Vector
set.seed(999)
cv.ridge <- cv.glmnet(x, y, family='binomial', alpha=0, parallel=TRUE, standardize=TRUE)
w3 <- 1/abs(matrix(coef(cv.ridge, s=cv.ridge$lambda.min)
[, 1][2:(ncol(x)+1)] ))^1 ## Using gamma = 1
w3[w3[,1] == Inf] <- 999999999 ## Replacing values estimated as Infinite for 999999999
## Adaptive Lasso
set.seed(999)
cv.lasso <- cv.glmnet(x, y, family='binomial', alpha=1, parallel=TRUE, standardize=TRUE, type.measure='auc', penalty.factor=w3)
plot(cv.lasso)
plot(cv.lasso$glmnet.fit, xvar="lambda", label=TRUE)
abline(v = log(cv.lasso$lambda.min))
abline(v = log(cv.lasso$lambda.1se))
coef(cv.lasso, s=cv.lasso$lambda.1se)
coef <- coef(cv.lasso, s='lambda.1se')
selected_attributes <- (coef@i[-1]+1) ## Considering the structure of the data frame dataF as shown earlier