coef.SplitGLM {SplitGLM}

Coefficients for SplitGLM Object

Description

coef.SplitGLM returns the coefficients for a SplitGLM object.

Usage

## S3 method for class 'SplitGLM'
coef(object, group_index = NULL, ...)

Arguments

object

An object of class SplitGLM.

group_index

The group for which to return the coefficients. Default is the ensemble.

...

Additional arguments for compatibility.

Value

The coefficients for the SplitGLM object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

SplitGLM

Examples

# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 1000
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 100
beta <- c(beta.active[1:p.active], rep(0, p-p.active))
Sigma <- matrix(0, p, p)
Sigma[1:p.active, 1:p.active] <- 0.5
diag(Sigma) <- 1

# Train data
x.train <- mvnfast::rmvn(n, mu = rep(0, p), sigma = Sigma) 
prob.train <- exp(x.train %*% beta)/
              (1+exp(x.train %*% beta))
y.train <- rbinom(n, 1, prob.train)
mean(y.train)
# Test data
x.test <- mvnfast::rmvn(N, mu = rep(0, p), sigma = Sigma)
prob.test <- exp(x.test %*% beta)/
             (1+exp(x.test %*% beta))
y.test <- rbinom(N, 1, prob.test)
mean(y.test)

# SplitGLM - CV (Multiple Groups)
split.out <- SplitGLM(x.train, y.train,
                      glm_type="Logistic",
                      G=10, include_intercept=TRUE,
                      alpha_s=3/4, alpha_d=1,
                      lambda_sparsity=1, lambda_diversity=1,
                      tolerance=1e-3, max_iter=1e3,
                      active_set=FALSE)
split.coef <- coef(split.out)
# Predictions
split.prob <- predict(split.out, newx=x.test, type="prob", group_index=NULL)
split.class <- predict(split.out, newx=x.test, type="class", group_index=NULL)
plot(prob.test, split.prob, pch=20)
abline(h=0.5,v=0.5)
mean((prob.test-split.prob)^2)
mean(abs(y.test-split.class))