selection.semi: Semi-parametric method for edge selection.

In haodongucsb/edgeSelection: Edge selection

Semi-parametric method for edge selection.

Description

Semi-parametric method for edge selection.

Usage

selection.semi(data, ...)

Arguments

data

Data Frame.

...

Any options can be defined. px Dimension of variables in the semi-parametric method to be estimated using non-parametric method. maxLambda Number used to generate the range of the tuning parameter for selection of Lambda matrix. maxTheta Number used to generate the range of the tuning parameter for selection of Theta matrix. iterLambda Number of iterations to find optimal Lambda matrix. iterTheta Number of iterations to find optimal Theta matrix. cutoff Cutoff value for squared projection. N Number of simulations in non-parametric part to calculate the standard error. semiMethod Method type to select optimal matrix in semi-parametric method.

Examples

library(huge)
n <- 200; p <- 20; px <- 5
# 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)
# Assume we estimate the first five variables using non-parametric method.
edge.selection(data = data, family ="semi", px = px)

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