glm.apg: Fit a generalized linear model with various regularizations.

In jpvert/apg: Optimization with accelerated proximal gradient

Description

Fit a generalized linear model via penalized maximum likelihood. Fits linear and logistic regression models, with elastic net or isotonic regularization.

Usage

glm.apg(x, y, family = c("gaussian", "binomial", "survival"),
  penalty = c("elasticnet", "isotonic", "boundednondecreasing"), lambda = 1,
  intercept = TRUE, opts = list())

Arguments

x	The input matrix, each row is a sample, each column a feature.
y	The response variable. Quantitative for family="gaussian", binary with values +1 and -1 for family="binomial", survival data with two columes (follow-up time and event) for family="survival"
family	The response type. For family="gaussian", ...
penalty	The penalty type.
lambda	The scaling of the penalty (default 1)
intercept	Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE)
opts	List of parameters, which must include:

Value

toto

Examples

n <- 100
p <- 5
x <- matrix(rnorm(n*p),n,p)
y <- rbinom(n,1,0.5)*2-1
lambda <- 0.2*max(abs(crossprod(y,x)))/n

# Lasso regression with intercept:
m <- glm.apg(x, y, lambda=lambda)
# same as
library(glmnet)
m2 <- glmnet(x, y, standardize=FALSE, lambda=lambda)

# Ridge regression with intercept:
m <- glm.apg(x, y, lambda=lambda, opts=list(alpha=0))
# Does the same as
m2 <- glmnet(x, y, standardize=FALSE, lambda=lambda, alpha=0)

# Elastic net regression with intercept:
m <- glm.apg(x, y, lambda=lambda, opts=list(alpha=0.5))
# Does the same as
m2 <- glmnet(x, y, standardize=FALSE, lambda=lambda, alpha=0.5)

# Elastic net regression without intercept:
m <- glm.apg(x, y, lambda=lambda, intercept=FALSE, opts=list(alpha=0.5))
# Does the same as
m2 <- glmnet(x, y, standardize=FALSE, lambda=lambda, alpha=0.5, intercept=FALSE)

# Lasso penalized logistic regression with intercept:
m <- glm.apg(x, y, family="binomial", lambda=lambda)
# Does the same as
m2 <- glmnet(x, y, family="binomial", lambda=lambda, standardize=FALSE)

# Elastic net penalized logistic regression with intercept:
m <- glm.apg(x, y, family="binomial", lambda=lambda, opts=list(alpha=0.5))
# Does the same as
m2 <- glmnet(x, y, family="binomial", lambda=lambda, standardize=FALSE, alpha=0.5)

# Isotonic regression with offset
m <- glm.apg(x, y, penalty="isotonic", lambda=lambda)

# Isotonic logistic regression with offset
m <- glm.apg(x, y, family="binomial", penalty="isotonic", lambda=lambda)

# Isotonic logistic regression with offset, with non-decreasing model of bounded norm
m <- glm.apg(x, y, family="binomial", penalty="boundednondecreasing", lambda=lambda, opts=list(maxnorm=2))

jpvert/apg documentation built on May 19, 2019, 11:51 p.m.