sscden_selection: Fit conditional density model

In haodongucsb/edgeSelection: Edge selection

Fit conditional density model

Description

Fit conditional density model

Usage

sscden_selection(
  formula,
  response,
  type = NULL,
  data = list(),
  weights,
  subset,
  na.action = na.omit,
  alpha = 1.4,
  id.basis = NULL,
  nbasis = NULL,
  seed = NULL,
  rho = list("xy"),
  ydomain = as.list(NULL),
  yquad = NULL,
  prec = 1e-07,
  maxiter = 30,
  skip.iter = TRUE,
  p = 2,
  theta2 = NULL,
  w2 = NULL
)

Arguments

formula	Symbolic description of the model to be fit.
response	Formula listing response variables.
type	List specifying the type of spline for each variable.
data	Optional data frame containing the variables in the model.
weights	Optional vector of bin-counts for histogram data.
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.
alpha	Parameter defining cross-validation score for smoothing parameter selection.
id.basis	Index of observations to be used as "knots."
nbasis	Number of "knots" to be used.
seed	Seed to be used for the random generation of "knots."
rho	Method to construct rho function.
ydomain	Data frame specifying marginal support of conditional density.
yquad	Quadrature for calculating integral on Y domain. 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.
p	Dimension of data frame.
theta2	Parameters for two-way interactions.
w2	Weights for two-way interactions.

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