cond_select: Neighborhood selection method with cross-validation to select...

In haodongucsb/edgeSelection: Edge selection

Neighborhood selection method with cross-validation to select the tuning parameter.

Description

Apply 5-fold cross-validation to select tuning parameter.

Usage

cond_select(
  data,
  formula,
  response,
  type = NULL,
  alpha,
  subset,
  na.action,
  rho,
  ydomain,
  yquad,
  prec,
  maxiter,
  skip.iter,
  M0,
  M_list,
  maxiteration,
  tolerance,
  id.basis = NULL,
  theta2,
  w2 = NULL,
  n,
  p
)

Arguments

data	Data frame
formula	Symbolic description of the model to be fit.
response	Formula listing response variables.
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.
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.
prec	Precision requirement for internal iterations.
maxiter	Maximum number of iterations allowed for internal iterations.
skip.iter	Flag indicating whether to use initial values of theta and skip theta iteration in the neighborhood selection method.
M0	Upper bound
M_list	List of values for tuning parameter selection
maxiteration	Max number of iteration
tolerance	Threshold for convergence
id.basis	Index of observations to be used as "knots."
theta2	Parameters for edge selection.
w2	Optional vector to specify weights for two-way interactions
n	Number of observation
p	Dimension of data frame

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