logreg {rIsing}

L1 Regularized Logistic Regression

Description

L1 Regularized logistic regression using OWL-QN L-BFGS-B optimization.

Usage

logreg(X, y, nlambda = 50, lambda.min.ratio = 0.001, lambda = NULL,
  scale = TRUE, type = 2)

Arguments

X

The design matrix.

y

Vector of binary observations of length equal to nrow(X).

nlambda

(positive integer) The number of parameters in the regularization path (default 50).

lambda.min.ratio

(non-negative double) The ratio of max(lambda) / min(lambda) (default 1e-3).

lambda

A user-supplied vector of regularization parameters. Under the default option (NULL), the function computes a regularization path using the input data.

scale

(boolean) Whether to scale X before running the regression. The output parameters will always be rescaled. Use FALSE if X is already scaled.

type

(integer 1 or 2) Type 1 aggregates the input data based on repeated rows in X. Type 2 (default) uses the data as is, and is generally faster. Use Type 1 if the data contains several repeated rows.

Value

A list containing the matrix of fitted weights (wmat), the vector of regularization parameters, sorted in decreasing order (lambda), and the vector of log-likelihoods corresponding to lambda (logliks).

Examples

# simulate some linear regression data
n <- 1e3
p <- 100
X <- matrix(rnorm(n*p),n,p)
wt <- sample(seq(0,9),p+1,replace = TRUE) / 10
z <- cbind(1,X) %*% wt + rnorm(n)
probs <- 1 / (1 + exp(-z))
y <- sapply(probs, function(p) rbinom(1,1,p))

m1 <- logreg(X, y)
m2 <- logreg(X, y, nlambda = 100, lambda.min.ratio = 1e-4, type = 1)

## Not run: 
# Performance comparison
library(glmnet)
library(microbenchmark)
nlambda = 50; lambda.min.ratio = 1e-3
microbenchmark(
  logreg_type1 = logreg(X, y, nlambda = nlambda,
                         lambda.min.ratio = lambda.min.ratio, type = 1),
  logreg_type2 = logreg(X, y, nlambda = nlambda,
                         lambda.min.ratio = lambda.min.ratio, type = 2),
  glmnet       = glmnet(X, y, family = "binomial",
                         nlambda = nlambda, lambda.min.ratio = lambda.min.ratio),
  times = 20L
)

## End(Not run)