View source: R/Coefficient_Functions.R
coef.SplitGLM | R Documentation |
coef.SplitGLM
returns the coefficients for a SplitGLM object.
## S3 method for class 'SplitGLM' coef(object, group_index = NULL, ...)
object |
An object of class SplitGLM. |
group_index |
The group for which to return the coefficients. Default is the ensemble. |
... |
Additional arguments for compatibility. |
The coefficients for the SplitGLM object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
SplitGLM
# 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))
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