edgenet {netReg} | R Documentation

## Fit a graph-regularized linear regression model using edge-based regularization.

### Description

Fit a graph-regularized linear regression model using edge-penalization. The coefficients are computed using graph-prior knowledge in the form of one/two affinity matrices. Graph-regularization is an extension to previously introduced regularization techniques, such as the LASSO. For that reason we are also using coordinate descent for minimization of the objective function of the linear model.

### Usage

    edgenet(X, Y, G.X = NULL, G.Y = NULL, lambda = 1, psigx = 1,
      psigy = 1, thresh = 1e-05, maxit = 1e+05, family = c("gaussian"))

### Arguments

`X` | input matrix, of dimension (`n` x `p`) where `n` is the number of observations and `p` is the number of covariables. Each row is an observation vector.
`Y` | output matrix, of dimension (`n` x `q`) where `n` is the number of observations and `q` is the number of response variables. Each row is an observation vector.
`G.X` | non-negativ affinity matrix for `n`, of dimensions (`p` x `p`) where `p` is the number of covariables `X`
`G.Y` | non-negativ affinity matrix for `n`, of dimensions (`q` x `q`) where `q` is the number of responses `Y`
`lambda` | `numerical` shrinkage parameter for LASSO.
`psigx` | `numerical` shrinkage parameter for graph-regularization of `G.X`
`psigy` | `numerical` shrinkage parameter for graph-regularization of `G.Y`
`thresh` | `numerical` threshold for coordinate descent
`maxit` | maximum number of iterations for coordinate descent (`integer`)
`family` | family of response, e.g. gaussian

### Value

An object of class `edgenet`

`coefficients` | the estimated (`p` x `q`)-dimensional coefficient matrix B.hat
`intercept` | the estimated (`q` x `1`)-dimensional vector of intercepts
`call` | the call that produced the object
`family` | the family of the response

### References

Dirmeier, Simon and Fuchs, Christiane and Mueller, Nikola S and Theis, Fabian J (2018), netReg: Network-regularized linear models for biological association studies.
Bioinformatics
Friedman J., Hastie T., Hoefling H. and Tibshirani R. (2007), Pathwise coordinate optimization.
The Annals of Applied Statistics
Friedman J., Hastie T. and Tibshirani R. (2010), Regularization Paths for Generalized Linear Models via Coordinate Descent.
Journal of Statistical Software
Fu W. J. (1998), Penalized Regression: The Bridge Versus the Lasso.
Journal of Computational and Graphical Statistics
Cheng W. and Wang W. (2014), Graph-regularized dual Lasso for robust eQTL mapping.
Bioinformatics

### Examples

    X <- matrix(rnorm(100*10), 100, 10)
    b <- rnorm(10)
    G.X <- matrix(rpois(100,1), 10)
    G.X <- t(G.X) + G.X
    diag(G.X) <- 0

    # fit a Gaussian model
    Y <- X%*%b + rnorm(100)
    fit <- edgenet(X=X, Y=Y, G.X=G.X, family="gaussian")

[Package netReg version 1.10.0 Index]