Description Usage Arguments Details Value Author(s) References Examples

Cross-validation method to tuning the parameter t for ISS.

1 2 |

`X` |
An n-by-p matrix of predictors |

`y` |
Response Variable |

`K` |
Folds number for CV. Default is 5. |

`t` |
A vector of predecided tuning parameter. |

`intercept` |
If TRUE, an intercept is included in the model (and not penalized), otherwise no intercept is included. Default is TRUE. |

`normalize` |
if TRUE, each variable is scaled to have L2 norm square-root n. Default is TRUE. |

`plot.it` |
Plot it? Default is TRUE |

`se` |
Include standard error bands? Default is TRUE |

`...` |
Additonal arguments passing to lb |

K-fold cross-validation method is used to tuning the parameter $t$ for ISS. Mean square error is used as prediction error.

A list is returned. The list contains a vector of parameter t, crossvalidation error cv.error, and the estimated standard deviation for it cv.sd

Feng Ruan, Jiechao Xiong and Yuan Yao

Ohser, Ruan, Xiong, Yao and Yin, Sparse Recovery via Differential Inclusions, http://arxiv.org/abs/1406.7728

1 2 3 4 5 6 7 8 9 10 11 12 | ```
#Examples in the reference paper
library(MASS)
n = 200;p = 100;k = 30;sigma = 1
Sigma = 1/(3*p)*matrix(rep(1,p^2),p,p)
diag(Sigma) = 1
A = mvrnorm(n, rep(0, p), Sigma)
u_ref = rep(0,p)
supp_ref = 1:k
u_ref[supp_ref] = rnorm(k)
u_ref[supp_ref] = u_ref[supp_ref]+sign(u_ref[supp_ref])
b = as.vector(A%*%u_ref + sigma*rnorm(n))
cv.iss(A,b,intercept = FALSE,normalize = FALSE)
``` |

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