cpg | R Documentation |
cpg
computes the coefficients for ensembles of generalized linear models via competing proximal gradients.
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 )
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. |
An object of class cpg
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
coef.CPGLIB
, predict.CPGLIB
# 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))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.