cpg: Competing Proximal Gradients Library for Ensembles of...

In CPGLIB: Competing Proximal Gradients Library

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, [e-mail]

See Also

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))

CPGLIB documentation built on Nov. 22, 2022, 5:08 p.m.