path.plot: Plot the solution path for the concave penalized logistic...

In cvplogistic: Penalized Logistic Regression Model using Majorization Minimization by Coordinate Descent (MMCD) Algorithm

Description

Plot the path trajectories for the solutions computed by the implemented methods.

Usage

path.plot(out)

Arguments

out	the object return from function cvplogistic or hybrid.logistic.

Details

The function plots the trajectories of solutions, with x-axis being the grids of lambda, and y-axis being the coefficients profile.

Author(s)

Dingfeng Jiang

References

Dingfeng Jiang, Jian Huang. Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models.

Zou, H., Li, R. (2008). One-step Sparse Estimates in Nonconcave Penalized Likelihood Models. Ann Stat, 364: 1509-1533.

Breheny, P., Huang, J. (2011). Coordinate Descent Algorithms for Nonconvex Penalized Regression, with Application to Biological Feature Selection. Ann Appl Stat, 5(1), 232-253.

Jiang, D., Huang, J., Zhang, Y. (2011). The Cross-validated AUC for MCP-Logistic Regression with High-dimensional Data. Stat Methods Med Res, online first, Nov 28, 2011.

See Also

cvplogistic, hybrid.logistic, cv.hybrid, cv.cvplogistic

Examples

set.seed(10000)
n=100
y=rbinom(n,1,0.4)
p=10
x=matrix(rnorm(n*p),n,p)

## MCP
out=cvplogistic(y, x)
path.plot(out)
## hybrid penalty
## out=hybrid.logistic(y, x, "mcp")
## path.plot(out)

cvplogistic documentation built on May 29, 2017, 11:34 p.m.