cv.ProxGrad | R Documentation |
cv.ProxGrad
computes and cross-validates the coefficients for generalized linear models using proximal gradients.
cv.ProxGrad( x, y, glm_type = c("Linear", "Logistic")[1], include_intercept = TRUE, alpha_s = 3/4, n_lambda_sparsity = 100, tolerance = 1e-08, max_iter = 1e+05, n_folds = 10, n_threads = 1 )
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". |
include_intercept |
Argument to determine whether there is an intercept. Default is TRUE. |
alpha_s |
Elastic net mixing parmeter. Default is 3/4. |
n_lambda_sparsity |
Sparsity tuning parameter value. Default is 100. |
tolerance |
Convergence criteria for the coefficients. Default is 1e-8. |
max_iter |
Maximum number of iterations in the algorithm. Default is 1e5. |
n_folds |
Number of cross-validation folds. Default is 10. |
n_threads |
Number of threads. Default is a single thread. |
An object of class cv.ProxGrad
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
coef.cv.ProxGrad
, predict.cv.ProxGrad
# 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) # 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) # ProxGrad - Single Groups proxgrad.out <- cv.ProxGrad(x.train, y.train, glm_type = "Logistic", include_intercept = TRUE, alpha_s = 3/4, n_lambda_sparsity = 100, tolerance = 1e-5, max_iter = 1e5) # Predictions proxgrad.prob <- predict(proxgrad.out, newx = x.test, type = "prob") proxgrad.class <- predict(proxgrad.out, newx = x.test, type = "class") plot(prob.test, proxgrad.prob, pch = 20) abline(h = 0.5,v = 0.5) mean((prob.test-proxgrad.prob)^2) mean(abs(y.test-proxgrad.class))
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