Description Usage Arguments Author(s) References See Also Examples
Similar to other predict methods, this function returns predictions from
a fitted "glmgraph"
object.
1 2 3 |
object |
Fitted |
X |
Matrix of values at which predictions are to be made. |
lambda1 |
Values of the regularization parameter |
lambda2 |
Values of the regularization parameter |
type |
Type of prediction: |
... |
Other 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)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | set.seed(1234)
library(glmgraph)
n <- 100
p1 <- 10
p2 <- 90
p <- p1+p2
X <- matrix(rnorm(n*p), n,p)
magnitude <- 1
## construct laplacian matrix from adjacency matrix
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
diagL <- apply(A,1,sum)
L <- -A
diag(L) <- diagL
### gaussian
Y <- eta+rnorm(n)
obj <- glmgraph(X,Y,L)
res <- predict(obj, X, type="link", lambda1=0.05,lambda2=0.01)
res <- predict(obj, X, type="response", lambda1=0.05,lambda2=0.01)
res <- predict(obj,X,type="nzeros",lambda1=0.05,lambda2=0.01)
### binomial
Y <- rbinom(n,1,prob=1/(1+exp(-eta)))
obj <- glmgraph(X,Y,L,family="binomial")
res <- predict(obj,X,type="class",lambda1=c(0.05,0.06),lambda2=c(0.02,0.16,0.32))
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