Plot the solution path for the concave penalized logistic models

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Description

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

Usage

1
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

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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)