# cv.iss: CV for ISS In Libra: Linearized Bregman Algorithms for Generalized Linear Models

## Description

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

## Usage

 ```1 2``` ```cv.iss(X, y, K = 5, t, intercept = TRUE, normalize = TRUE, plot.it = TRUE, se = TRUE, ...) ```

## Arguments

 `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

## Details

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

## Value

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

## Author(s)

Feng Ruan, Jiechao Xiong and Yuan Yao

## References

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

## Examples

 ``` 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) ```

Libra documentation built on May 2, 2019, 3:55 p.m.