coef.cv.CPGLIB {CPGLIB}R Documentation

Coefficients for cv.CPGLIB Object

Description

coef.cv.CPGLIB returns the coefficients for a cv.CPGLIB object.

Usage

## S3 method for class 'cv.CPGLIB'
coef(object, groups = NULL, ensemble_average = FALSE, ...)

Arguments

object

An object of class cv.CPGLIB.

groups

The groups in the ensemble for the coefficients. Default is all of the groups in the ensemble.

ensemble_average

Option to return the average of the coefficients over all the groups in the ensemble. Default is FALSE.

...

Additional arguments for compatibility.

Value

The coefficients for the cv.CPGLIB object. Default is FALSE.

Author(s)

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

See Also

cv.cpg

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)
mean(y.test)

# CV CPGLIB - Multiple Groups
cpg.out <- cv.cpg(x.train, y.train,
                  glm_type = "Logistic",
                  G = 5, include_intercept = TRUE,
                  alpha_s = 3/4, alpha_d = 1,
                  n_lambda_sparsity = 100, n_lambda_diversity = 100,
                  balanced_cycling = TRUE,
                  tolerance = 1e-5, max_iter = 1e5)
cpg.coef <- coef(cpg.out)

# Coefficients for each group                
cpg.coef <- coef(cpg.out, ensemble_average = FALSE)





[Package CPGLIB version 1.0.1 Index]