cv.edgenet {netReg} | R Documentation |
Finds the optimal shrinkage parameters using cross-validation for edgenet. We use the BOBYQA algorithm to find the optimial regularization parameters and coordinate descent in order to minimize the objective function of the linear model.
cv.edgenet(X, Y, G.X = NULL, G.Y = NULL, lambda = NULL, psigx = NULL, psigy = NULL, thresh = 1e-05, maxit = 1e+05, family = c("gaussian"), optim.epsilon = 0.001, optim.maxit = 10000, nfolds = 10)
X |
input matrix, of dimension ( |
Y |
output matrix, of dimension ( |
G.X |
non-negativ affinity matrix for |
G.Y |
non-negativ affinity matrix for |
lambda |
|
psigx |
|
psigy |
|
thresh |
|
maxit |
maximum number of iterations for the coordinate descent
( |
family |
family of response, e.g. gaussian |
optim.epsilon |
|
optim.maxit |
the maximum number of iterations for the optimization
( |
nfolds |
the number of folds to be used - default is 10
(minimum 3, maximum |
An object of class cv.edgenet
call |
the call that produced the object |
lambda |
the estimated ( |
psigx |
the estimated ( |
psigy |
the estimated ( |
Dirmeier, Simon and Fuchs, Christiane and Mueller, Nikola S and Theis,
Fabian J (2018),
netReg: Network-regularized linear models for biological association
studies.
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
Powell M.J.D. (2009),
The BOBYQA algorithm for bound constrained optimization without
derivatives.
http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf
X <- matrix(rnorm(100*10), 100, 10) b <- rnorm(10) G.X <- matrix(rpois(10*10,1),10) G.X <- t(G.X) + G.X diag(G.X) <- 0 # fit a Gaussian model Y <- X%*%b + rnorm(100) cv.edge <- cv.edgenet(X=X, Y=Y, G.X=G.X, family="gaussian")