## -------------------------------------------------------------------------- set.seed(23) X <- matrix(rnorm(1000*5), 1000) Y <- matrix(rnorm(1000*5), 1000) ## -------------------------------------------------------------------------- library(netReg) ## -------------------------------------------------------------------------- aff.mat <- matrix(rbeta(25, 1, 5), 5) aff.mat <- (t(aff.mat) + aff.mat) / 2 diag(aff.mat) <- 0 ## -------------------------------------------------------------------------- fit <- edgenet(X=X, Y=Y, G.X=aff.mat, lambda=1, psigx=1, family="gaussian") print(fit) ## -------------------------------------------------------------------------- X.new <- matrix(rnorm(10*5),10) pred <- predict(fit, X) ## -------------------------------------------------------------------------- cv <- cv.edgenet(X=X, Y=Y, G.X=aff.mat, family="gaussian", maxit=1000) print(cv) ## -------------------------------------------------------------------------- p <- 25 X <- matrix(rnorm(10*p), 10) Y <- matrix(rnorm(10*p), 10) aff.mat <- matrix(rgamma(p * p, 5, 1), p) aff.mat <- (t(aff.mat) + aff.mat) diag(aff.mat) <- 0 cv <- cv.edgenet(X=X, Y=Y, G.X=aff.mat, family="gaussian", maxit=1000) print(cv)