# 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

 `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.

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.

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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.