View source: R/CPGLIB_Prediction_Functions.R
predict.cv.CPGLIB | R Documentation |
predict.cv.CPGLIB
returns the predictions for a ProxGrad object.
## S3 method for class 'cv.CPGLIB' predict( object, newx, groups = NULL, ensemble_type = c("Model-Avg", "Coef-Avg", "Weighted-Prob", "Majority-Vote")[1], class_type = c("prob", "class")[1], ... )
object |
An object of class cv.CPGLIB. |
newx |
New data for predictions. |
groups |
The groups in the ensemble for the predictions. Default is all of the groups in the ensemble. |
ensemble_type |
The type of ensembling function for the models. Options are "Model-Avg", "Coef-Avg" or "Weighted-Prob" for classifications predictions. Default is "Model-Avg". |
class_type |
The type of predictions for classification. Options are "prob" and "class". Default is "prob". |
... |
Additional arguments for compatibility. |
The predictions for the cv.CPGLIB object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
cv.cpg
# 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, tolerance = 1e-5, max_iter = 1e5) # Predictions cpg.prob <- predict(cpg.out, newx = x.test, type = "prob", groups = 1:cpg.out$G, ensemble_type = "Model-Avg") cpg.class <- predict(cpg.out, newx = x.test, type = "class", groups = 1:cpg.out$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))
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