edgenet-methods: Fit a graph-regularized linear regression model using...

In cbg-ethz/pareg: Pathway enrichment using a regularized regression approach

Fit a graph-regularized linear regression model using edge-based regularization. Adapted from https://github.com/dirmeier/netReg.

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. See the vignette for details on the objective function of the model: vignette("edgenet", package="netReg")

Usage

edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = 0,
  psigx = 0,
  psigy = 0,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian
)

## S4 method for signature 'matrix,numeric'
edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = 0,
  psigx = 0,
  psigy = 0,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian
)

## S4 method for signature 'matrix,matrix'
edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = 0,
  psigx = 0,
  psigy = 0,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = 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 X, of dimensions (p x p) where p is the number of covariables
G.Y	non-negativ affinity matrix for Y, of dimensions (q x q) where q is the number of responses
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 optimizer
maxit	maximum number of iterations for optimizer (integer)
learning.rate	step size for Adam optimizer (numerical)
family	family of response, e.g. gaussian or binomial

Value

An object of class edgenet

beta	the estimated (p x q)-dimensional coefficient matrix B.hat
alpha	the estimated (q x 1)-dimensional vector of intercepts
parameters	regularization parameters
lambda	regularization parameter lambda)
psigx	regularization parameter psigx
psigy	regularization parameter psigy
family	a description of the error distribution and link function to be used. Can be a pareg::family function or a character string naming a family function, e.g. gaussian or "gaussian".
call	the call that produced the object

References

Cheng, Wei and Zhang, Xiang and Guo, Zhishan and Shi, Yu and Wang, Wei (2014), Graph-regularized dual Lasso for robust eQTL mapping.
Bioinformatics

Examples

X <- matrix(rnorm(100 * 10), 100, 10)
b <- matrix(rnorm(100), 10)
G.X <- abs(rWishart(1, 10, diag(10))[, , 1])
G.Y <- abs(rWishart(1, 10, diag(10))[, , 1])
diag(G.X) <- diag(G.Y) <- 0

# estimate the parameters of a Gaussian model
Y <- X %*% b + matrix(rnorm(100 * 10), 100)
## dont use affinity matrices
fit <- edgenet(X = X, Y = Y, family = gaussian, maxit = 10)
## only provide one matrix
fit <- edgenet(
  X = X,
  Y = Y,
  G.X = G.X,
  psigx = 1,
  family = gaussian,
  maxit = 10
)
## use two matrices
fit <- edgenet(X = X, Y = Y, G.X = G.X, G.Y, family = gaussian, maxit = 10)
## if Y is vectorial, we cannot use an affinity matrix for Y
fit <- edgenet(X = X, Y = Y[, 1], G.X = G.X, family = gaussian, maxit = 10)

cbg-ethz/pareg documentation built on July 20, 2023, 7:30 p.m.