Description Usage Arguments Author(s) References See Also Examples
Plots the cross-validation curve for the "cv.glmgraph" object, along with standard error bars.
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Other graphical parameters to |
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)
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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)
plot(cv.obj)
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Loading required package: Rcpp
Starting CV fold #1
Starting CV fold #2
Starting CV fold #3
Starting CV fold #4
Starting CV fold #5
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