edgenet  R Documentation 
Fit a graphregularized linear regression model using
edgepenalization. The coefficients are computed using graphprior
knowledge in the form of one/two affinity matrices. Graphregularization 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")
edgenet(
X,
Y,
G.X = NULL,
G.Y = NULL,
lambda = 0,
psigx = 0,
psigy = 0,
thresh = 1e05,
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 = 1e05,
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 = 1e05,
maxit = 1e+05,
learning.rate = 0.01,
family = gaussian
)
X 
input matrix, of dimension ( 
Y 
output matrix, of dimension ( 
G.X 
nonnegativ affinity matrix for 
G.Y 
nonnegativ affinity matrix for 
lambda 

psigx 

psigy 

thresh 

maxit 
maximum number of iterations for optimizer
( 
learning.rate 
step size for Adam optimizer ( 
family 
family of response, e.g. gaussian or binomial 
An object of class edgenet
beta 
the estimated ( 
alpha 
the estimated ( 
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 
call 
the call that produced the object 
Cheng, Wei and Zhang, Xiang and Guo, Zhishan and Shi, Yu and Wang, Wei
(2014),
Graphregularized dual Lasso for robust eQTL mapping.
Bioinformatics
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)
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