View source: R/Coefficient_Plot_Functions.R
plot.cv.SplitGLM | R Documentation |
plot.cv.SplitGLM
returns the coefficients for a cv.SplitGLM object.
## S3 method for class 'cv.SplitGLM' plot( x, group_index = NULL, plot_type = c("Coef", "CV-Error")[1], active_only = TRUE, path_type = c("Log-Lambda", "L1-Norm")[1], labels = TRUE, ... )
x |
An object of class cv.SplitGLM. |
group_index |
The group for which to return the coefficients. Default is the ensemble coefficients. |
plot_type |
Plot of coefficients, "Coef" (default), or cross-validated error or deviance, "CV-Error". |
active_only |
Only include the variables selected in final model (default is TRUE). |
path_type |
Plot of coefficients paths as a function of either "Log-Lambda" (default) or "L1-Norm". |
labels |
Include the labels of the variables (default is FALSE). |
... |
Additional arguments for compatibility. |
The coefficients for the cv.SplitGLM object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
cv.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 <- cv.SplitGLM(x.train, y.train, glm_type="Logistic", G=10, include_intercept=TRUE, alpha_s=3/4, alpha_d=1, n_lambda_sparsity=50, n_lambda_diversity=50, tolerance=1e-3, max_iter=1e3, n_folds=5, active_set=FALSE, n_threads=1) # Plot of coefficients paths (function of Log-Lambda) plot(split.out, plot_type="Coef", path_type="Log-Lambda", group_index=1, labels=FALSE) # Plot of coefficients paths (function of L1-Norm) plot(split.out, plot_type="Coef", path_type="L1-Norm", group_index=1, labels=FALSE) # Plot of CV error plot(split.out, plot_type="CV-Error")
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