cv.plot: Plot the cross validation performance

In grppenalty: Concave 1-norm and 2-norm group penalty in linear and logistic regression

Description

Plot the cross validation performance, for linear model CV-PMSE will be plotted, for logistic model CV-AUC will be plotted.

Usage

cv.plot(cv.out)

Arguments

cv.out

the object from the cv.grppenalty function

Details

The cv.plot shows the cross validation performance relative to the kappa and lambda. This is to visualize the overall cross validation process.

Author(s)

Dingfeng Jiang

References

Jiang, D., Huang, J., Zhang, Y. (2011). The cross-validated AUC for MCP-Logistic regression with high-dimensional data. Statistical Methods in Medical Research, online first.

Yuan, M., Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of Royal Statistical Society Series B, 68 (1): 49 - 67.

Meier, L., van de Geer, S., B\”uhlmann, P., (2008). The group lasso for logistic regression. Journal of Royal Statistical Society Series B, 70 (1): 53 - 71

See Also

grppenalty

Examples

set.seed(10000)
n=100
ybi=rbinom(n,1,0.4)
yga=rnorm(n)
p=20
x=matrix(rnorm(n*p),n,p)
index=rep(1:10, each = 2)
## cv.out=cv.grppenalty(yga, x, index, "gaussian", "l1", "mcp",  1/2.7)
## cv.plot(cv.out)
## multiple kappas
cv.out=cv.grppenalty(yga, x, index, "gaussian", "l1", "mcp",  c(0,0.1,1/2.7))
cv.plot(cv.out)

Example output

grppenalty documentation built on May 30, 2017, 4:33 a.m.