plot.glmtlp.Rd
Generates a solution path plot for a fitted "glmtlp"
object.
# S3 method for glmtlp plot( x, xvar = c("lambda", "kappa", "deviance", "l1_norm", "log_lambda"), xlab = iname, ylab = "Coefficients", title = "Solution Path", label = FALSE, label.size = 3, ... )
x | Fitted |
---|---|
xvar | The x-axis variable to plot against, including |
xlab | The x-axis label of the plot, default is |
ylab | The y-axis label of the plot, default is "Coefficients". |
title | The main title of the plot, default is "Solution Path". |
label | Logical, whether or not attach the labels for the non-zero
coefficients, default is |
label.size | The text size of the labels, default is 3. |
... | Additional arguments. |
A ggplot
object.
The generated plot is a ggplot
object, and therefore, the users are able
to customize the plots following the ggplot2
syntax.
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.
print
, predict
, coef
and plot
methods,
and the cv.glmtlp
function.
Chunlin Li, Yu Yang, Chong Wu
Maintainer: Yu Yang yang6367@umn.edu
X <- matrix(rnorm(100 * 20), 100, 20) y <- rnorm(100) fit <- glmtlp(X, y, family = "gaussian", penalty = "l1") plot(fit, xvar = "lambda")