Plots the cross-validated error curve, and confidence bounds for each
lambda in our regularization path.
additional arguments to be passed to plot
A cross validated deviance plot is produced. More regularized models are to the right (less regularized to the left)
Modified from SGL package: Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Maintainer: Kourosh Zarringhalam <[email protected]>
Simon, N., Friedman, J., Hastie T., and Tibshirani, R. (2011)
A Sparse-Group Lasso,
1 2 3 4 5 6 7 8 9 10
n = 50; p = 100; size.groups = 10 index <- ceiling(1:p / size.groups) X = matrix(rnorm(n * p), ncol = p, nrow = n) beta = (-2:2) y = X[,1:5] %*% beta + 0.1*rnorm(n) y = ifelse((exp(y) / (1 + exp(y))) > 0.5, 1, 0) data = list(x = X, y = y) weights = rep(1, size.groups) cvFit = cvSGL(data, index, weights, type = "logit", maxit = 1000, thresh = 0.001, min.frac = 0.05, nlam = 100, gamma = 0.8, nfold = 10, standardize = TRUE, verbose = FALSE, step = 1, reset = 10, alpha = 0.05, lambdas = NULL) plot(cvFit)
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