selection.neighborhood: Neighborhood selection method

In haodongucsb/edgeSelection: Edge selection

Neighborhood selection method

Description

Apply neighborhood selection method by considering conditional density function on each variable.

Usage

selection.neighborhood(data, ...)

Arguments

data

Data frame

...

Any options can be defined. type List specifying the type of spline for each variable. alpha Parameter defining cross-validation score for smoothing parameter selection. subset Optional vector specifying a subset of observations to be used in the fitting process. na.action Function which indicates what should happen when the data contain NAs. seed Seed to be used for the random generation of "knots." prec Precision requirement for internal iterations. maxiter Maximum number of iterations allowed for internal iterations. id.basis Index of observations to be used as "knots." nbasis Number of "knots" to be used. rho Method to construct rho function for neighborhood selection method. ydomain Data frame specifying marginal support of conditional density in the neighborhood selection method. yquad Quadrature for calculating integral on Y domain in the neighborhood selection method. Mandatory if response variables other than factors or numerical vectors are involved. skip.iter Flag indicating whether to use initial values of theta and skip theta iteration in the neighborhood selection method. W Optional matrix to specify weights for two-way interactions in the neighborhood selection method for each node. neighborhoodMethod Method type in the neighborhood selection method to select tuning parameter which controls sparsity of the graph.

Examples

# Parallel backend
library(doMC)
library(foreach)
library(huge)
registerDoMC(20)
n <- 200; p <- 20
# Simulate high dimension data
set.seed(5732)
z <- huge.generator(n, d = p, graph = "random", prob = .2, verbose = FALSE, vis = FALSE, v = .65)
data <- data.frame(z$data)
edge.selection(data = data, family = "neighborhood")

haodongucsb/edgeSelection documentation built on May 8, 2022, 4:40 p.m.