cvedgenet-methods: Find the optimal shrinkage parameters for edgenet

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

Find the optimal shrinkage parameters for edgenet

Description

Finds the optimal regulariztion parameters using cross-validation for edgenet. We use the BOBYQA algorithm to find the optimial regularization parameters in a cross-validation framework.

Usage

cv_edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.thresh = 0.01,
  optim.maxit = 100,
  lambda_range = seq(0, 2, length.out = 10),
  psigx_range = seq(0, 500, length.out = 10),
  psigy_range = seq(0, 500, length.out = 10),
  nfolds = 2,
  cv_method = c("grid_search", "grid_search_lsf", "optim"),
  tempdir = "."
)

## S4 method for signature 'matrix,numeric'
cv_edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.thresh = 0.01,
  optim.maxit = 100,
  lambda_range = seq(0, 2, length.out = 10),
  psigx_range = seq(0, 500, length.out = 10),
  psigy_range = seq(0, 500, length.out = 10),
  nfolds = 2,
  cv_method = c("grid_search", "grid_search_lsf", "optim"),
  tempdir = "."
)

## S4 method for signature 'matrix,matrix'
cv_edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.thresh = 0.01,
  optim.maxit = 100,
  lambda_range = seq(0, 2, length.out = 10),
  psigx_range = seq(0, 500, length.out = 10),
  psigy_range = seq(0, 500, length.out = 10),
  nfolds = 2,
  cv_method = c("grid_search", "grid_search_lsf", "optim"),
  tempdir = "."
)

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. Providing a graph G.X will optimize the regularization parameter psi.gx. If this is not desired just set G.X to NULL.
G.Y	non-negativ affinity matrix for Y, of dimensions (q x q) where q is the number of responses Y. Providing a graph G.Y will optimize the regularization parameter psi.gy. If this is not desired just set G.Y to NULL.
lambda	numerical shrinkage parameter for LASSO. Per default this parameter is set to NA_real_ which means that lambda is going to be estimated using cross-validation. If any numerical value for lambda is set, estimation of the optimal parameter will not be conducted.
psigx	numerical shrinkage parameter for graph-regularization of G.X. Per default this parameter is set to NA_real_ which means that psigx is going to be estimated in the cross-validation. If any numerical value for psigx is set, estimation of the optimal parameter will not be conducted.
psigy	numerical shrinkage parameter for graph-regularization of G.Y. Per default this parameter is set to NA_real_ which means that psigy is going to be estimated in the cross-validation. If any numerical value for psigy is set, estimation of the optimal parameter will not be conducted.
thresh	numerical threshold for the optimizer
maxit	maximum number of iterations for the optimizer (integer)
learning.rate	step size for Adam optimizer (numerical)
family	family of response, e.g. gaussian or binomial
optim.thresh	numerical threshold criterion for the optimization to stop. Usually 1e-3 is a good choice.
optim.maxit	the maximum number of iterations for the optimization (integer). Usually 1e4 is a good choice.
lambda_range	range of lambda to use in CV grid.
psigx_range	range of psigx to use in CV grid.
psigy_range	range of psigy to use in CV grid.
nfolds	the number of folds to be used - default is 10.
cv_method	which cross-validation method to use.
tempdir	where to store auxiliary files.

Value

An object of class cv_edgenet

parameters	the estimated, optimal regularization parameters
lambda	optimal estimated value for regularization parameter lambda (or, if provided as argument, the value of the parameter)
psigx	optimal estimated value for regularization parameter psigx (or, if provided as argument, the value of the parameter)
psigy	optimal estimated value for regularization parameter psigy (or, if provided as argument, the value of the parameter)
estimated.parameters	names of parameters that were estimated
family	family used for estimated
fit	an edgenet object fitted with the optimal, estimated paramters
call	the call that produced the object

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 and estimate lambda
fit <- cv_edgenet(
  X = X,
  Y = Y,
  family = gaussian,
  maxit = 1,
  lambda_range = c(0, 1)
)
## only provide one matrix and estimate lambda
fit <- cv_edgenet(
  X = X,
  Y = Y,
  G.X = G.X,
  psigx = 1,
  family = gaussian,
  maxit = 1,
  lambda_range = c(0, 1)
)
## estimate only lambda with two matrices
fit <- cv_edgenet(
  X = X,
  Y = Y,
  G.X = G.X,
  G.Y,
  psigx = 1,
  psigy = 1,
  family = gaussian,
  maxit = 1,
  lambda_range = c(0, 1)
)
## estimate only psigx
fit <- cv_edgenet(
  X = X,
  Y = Y,
  G.X = G.X,
  G.Y,
  lambda = 1,
  psigy = 1,
  family = gaussian,
  maxit = 1,
  psigx_range = c(0, 1)
)
## estimate all parameters
fit <- cv_edgenet(
  X = X,
  Y = Y,
  G.X = G.X,
  G.Y,
  family = gaussian,
  maxit = 1,
  lambda_range = c(0, 1),
  psigx_range = c(0, 1),
  psigy_range = c(0, 1)
)
## if Y is vectorial, we cannot use an affinity matrix for Y
fit <- cv_edgenet(
  X = X,
  Y = Y[, 1],
  G.X = G.X,
  family = gaussian,
  maxit = 1,
  lambda_range = c(0, 1),
  psigx_range = c(0, 1),
)

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