Plot Method for a

Plots the cross-validation curve, and the upper and lower standard deviation curves, as a function of the lambda or kappa values.

Usage

# S3 method for class 'cv.glmtlp'
plot(x, vertical.line = TRUE, ...)

Arguments

x: Fitted cv.glmtlp object
vertical.line: Logical. Whether or not include a vertical line indicating the position of the index which gives the smallest CV error.
...: Additional arguments.

Details

The generated plot is a ggplot object, and therefore, the users are able to customize the plots following the ggplot2 syntax.

References

Shen, X., Pan, W., & Zhu, Y. (2012). Likelihood-based selection and sharp parameter estimation. Journal of the American Statistical Association, 107(497), 223-232.
Shen, X., Pan, W., Zhu, Y., & Zhou, H. (2013). On constrained and regularized high-dimensional regression. Annals of the Institute of Statistical Mathematics, 65(5), 807-832.
Li, C., Shen, X., & Pan, W. (2021). Inference for a Large Directed Graphical Model with Interventions. arXiv preprint arXiv:2110.03805.
Yang, Y., & Zou, H. (2014). A coordinate majorization descent algorithm for l1 penalized learning. Journal of Statistical Computation and Simulation, 84(1), 84-95.
Two R package Github: ncvreg and glmnet.

Author

Chunlin Li, Yu Yang, Chong Wu
Maintainer: Yu Yang yang6367@umn.edu

Examples

X <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
cv.fit <- cv.glmtlp(X, y, family = "gaussian", penalty = "tlp")
plot(cv.fit)

plot(cv.fit, vertical.line = FALSE)

cv.fit2 <- cv.glmtlp(X, y, family = "gaussian", penalty = "l0")
plot(cv.fit2)

plot(cv.fit2, vertical.line = FALSE)


data("gau_data")
cv.fit <- cv.glmtlp(gau_data$X, gau_data$y, family = "gaussian", penalty = "tlp")
plot(cv.fit)


data("bin_data")
cv.fit <- cv.glmtlp(bin_data$X, bin_data$y, family = "binomial", penalty = "l1")
plot(cv.fit)