plot.glmtlp {glmtlp}

Plot Method for a "glmtlp" Object

Description

Generates a solution path plot for a fitted "glmtlp" object.

Usage

## S3 method for class 'glmtlp'
plot(
  x,
  xvar = c("lambda", "kappa", "deviance", "l1_norm", "log_lambda"),
  xlab = iname,
  ylab = "Coefficients",
  title = "Solution Path",
  label = FALSE,
  label.size = 3,
  ...
)

Arguments

x

Fitted glmtlp object.

xvar

The x-axis variable to plot against, including "lambda", "kappa", "deviance", "l1_norm", and "log_lambda".

xlab

The x-axis label of the plot, default is "Lambda", "Kappa", "Fraction of Explained Deviance", "L1 Norm", and "Log Lambda".

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 FALSE.

label.size

The text size of the labels, default is 3.

...

Additional arguments.

Details

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

Value

A ggplot object.

Author(s)

Chunlin Li, Yu Yang, Chong Wu
Maintainer: Yu Yang [e-mail]

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.

See Also

print, predict, coef and plot methods, and the cv.glmtlp function.

Examples

X <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
fit <- glmtlp(X, y, family = "gaussian", penalty = "l1")
plot(fit, xvar = "lambda")
plot(fit, xvar = "log_lambda")
plot(fit, xvar = "l1_norm")
plot(fit, xvar = "log_lambda", label = TRUE)
fit2 <- glmtlp(X, y, family = "gaussian", penalty = "l0")
plot(fit2, xvar = "kappa", label = TRUE)