print.cv.glmgraph: print a glmgraph object In glmgraph: Graph-Constrained Regularization for Sparse Generalized Linear Models

Description

Print a summary of the cv.glmgraph solution path information during cross validation

Usage

 ```1 2``` ```## S3 method for class 'cv.glmgraph' print(x, ...) ```

Arguments

 `x` fitted `cv.glmgraph` object `...` Other parameters to `print`

Details

The call prints the `cvmat` object from a fitted `cv.glmgraph` object. The call also prints the chosen regularization parameters lambda1 and lambda2 along with best `cv.type`(minimum "mse" or "mae" if `family` is "gaussian"; maximum "auc" or minimum "deviance" if `family` is "binomial") after cross validation.

Author(s)

Li Chen <[email protected]> , Jun Chen <[email protected]>

References

Li Chen. Han Liu. Hongzhe Li. Jun Chen. (2015) glmgraph: Graph-constrained Regularization for Sparse Generalized Linear Models.(Working paper)

`cv.glmgraph`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ``` set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 A <- matrix(rep(0,p*p),p,p) A[1:p1,1:p1] <- 1 A[(p1+1):p,(p1+1):p] <- 1 diag(A) <- 0 btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue ### construct laplacian matrix from adjacency matrix diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL ### gaussian Y <- eta+rnorm(n) cv.obj <- cv.glmgraph(X,Y,L) print(cv.obj) ```