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

1 2 |

`x` |
fitted |

`...` |
Other parameters to |

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.

Li Chen <li.chen@emory.edu> , Jun Chen <chen.jun2@mayo.edu>

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

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)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.