# glmgraph-package: Fit a GLM with a combination of sparse and smooth... In glmgraph: Graph-Constrained Regularization for Sparse Generalized Linear Models

## Description

Fit a generalized linear model at grids of tuning parameter via penalized maximum likelihood. The regularization path is computed for a combination of sparse and smooth penalty at two grids of values for the regularization parameter lambda1(Lasso or MCP penalty) and lambda2(Laplacian penalty). Fits linear, logistic regression models.

## Details

 Package: glmgraph Type: Package Version: 1.0-0 Date: 2015-03-11 License: GPL-2

The algorithm accepts a design matrix `X`, a vector of responses `Y` and a Laplacian matrix `L`. Produces the regularization path over the grid of tuning parameter `lambda1` and `lambda2`. It consists of the following main functions
`glmgraph`
`cv.glmgraph`
`plot.glmgraph`
`coef.glmgraph`
`predict.glmgraph`

## 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)

## Examples

 ``` 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 30``` ``` set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 ## Construct Adjacency and Laplacian matrices 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 diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue Y <- eta+rnorm(n) obj <- glmgraph(X,Y,L,family="gaussian") plot(obj) betas <- coef(obj) betas <- coef(obj,lambda1=c(0.1,0.2)) yhat <- predict(obj,X,type="response") cv.obj <- cv.glmgraph(X,Y,L) plot(cv.obj) beta.min <- coef(cv.obj) yhat.min <- predict(cv.obj,X) ```

glmgraph documentation built on May 29, 2017, 9:36 a.m.