The glmnet
package uses a range of LASSO
tuning parameters lambda
scaled from the maximal lambda_max
under which no predi
It seems lambda_max
for a logistic regression is calculated similarly, with weights based on class proportions:
set.seed(1)
library("glmnet")
x <- matrix(rnorm(100*20),100,20)
y <- rnorm(100)
mysd <- function(y) sqrt(sum((y-mean(y))^2)/length(y))
sx <- scale(x, scale=apply(x, 2, mysd))
sx <- as.matrix(sx, ncol=20, nrow=100)
y_bin <- factor(ifelse(y<0, -1, 1))
prop.table(table(y_bin))
# y_bin
# -1 1
# 0.62 0.38
fitGLM_log <- glmnet(sx, y_bin, family = "binomial")
max(fitGLM_log$lambda)
# [1] 0.1214006
max(abs(colSums(sx*ifelse(y<0, -.38, .62))))/100
# [1] 0.1214006