# plot.glmgraph: Plot coefficients from a "glmgraph" object In glmgraph: Graph-Constrained Regularization for Sparse Generalized Linear Models

## Description

Plot solution path for a fitted `"glmgraph"` object.

## Usage

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

## Arguments

 `x` Fitted `"glmgraph"` model. `...` Other graphical parameters to `plot`

## 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)

`glmgraph`
 ``` 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``` ``` 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) plot(obj) ### binomial Y <- rbinom(n,1,prob=1/(1+exp(-eta))) obj <- glmgraph(X,Y,L,family="binomial") plot(obj) ```