cpg {CPGLIB} R Documentation

## Competing Proximal Gradients Library for Ensembles of Generalized Linear Models

### Description

cpg computes the coefficients for ensembles of generalized linear models via competing proximal gradients.

### Usage

cpg(
x,
y,
glm_type = c("Linear", "Logistic")[1],
G = 5,
include_intercept = TRUE,
alpha_s = 3/4,
alpha_d = 1,
lambda_sparsity,
lambda_diversity,
tolerance = 1e-08,
max_iter = 1e+05
)


### Arguments

 x Design matrix. y Response vector. glm_type Description of the error distribution and link function to be used for the model. Must be one of "Linear" or "Logistic". Default is "Linear". G Number of groups in the ensemble. include_intercept Argument to determine whether there is an intercept. Default is TRUE. alpha_s Sparsity mixing parmeter. Default is 3/4. alpha_d Diversity mixing parameter. Default is 1. lambda_sparsity Sparsity tuning parameter value. lambda_diversity Diversity tuning parameter value. tolerance Convergence criteria for the coefficients. Default is 1e-8. max_iter Maximum number of iterations in the algorithm. Default is 1e5.

### Value

An object of class cpg

### Author(s)

Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca

coef.CPGLIB, predict.CPGLIB

### Examples


# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
# 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)

# CPGLIB - Multiple Groups
cpg.out <- cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
lambda_sparsity = 0.01, lambda_diversity = 1,
tolerance = 1e-5, max_iter = 1e5)

# Predictions
cpg.prob <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg") cpg.class <- predict(cpg.out, newx = x.test, type = "prob", groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
plot(prob.test, cpg.prob, pch = 20)
abline(h = 0.5,v = 0.5)
mean((prob.test-cpg.prob)^2)
mean(abs(y.test-cpg.class))



[Package CPGLIB version 1.1.1 Index]