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I am using the rms library to perform regularized logistic regression, and wish to force the intercept to zero. I'm using the following to simulate and regress:

library(rms)
N = 100
pred <- vapply(1:12, function(i) rnorm(N, mean = 0, sd =1), numeric(N))
resp <- 20*pred[, 1] - 3*pred[, 7] - 2*pred[, 8] + matrix(rnorm(N, sd = 0.1)) + 20
pr <- 1 / (1 + exp(-resp))
y <- rbinom(N, 1, pr)
lrm(y ~ pred, penalty = 1)

The post at How to remove intercept in R suggests including '0 +' or '- 1' in the model formula. However, this does not appear to work for lrm.

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TreeStump
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1 Answers1

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You can use glmnet. It also includes a cross validation function for choosing the turning parameter.

library(glmnet)
N = 1000
pred <- vapply(1:12, function(i) rnorm(N, mean = 0, sd =1), numeric(N))
resp <- 20*pred[, 1] - 3*pred[, 7] - 2*pred[, 8] + matrix(rnorm(N, sd = 0.1)) + 20
pr <- 1 / (1 + exp(-resp))
y <- rbinom(N, 1, pr)
result <- cv.glmnet(pred, y, family="binomial", intercept=FALSE)
# best lambda based on cv
result$lambda.min
# coefficient
coef(result$glmnet.fit, s=result$lambda.min)
Randy Lai
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