accept_reject: Generate sample from a conditional density estimate

In hotneim/lg: Locally Gaussian Distributions: Estimation and Methods

Description

Generate a sample from a locally Gaussian conditional density estimate using the accept-reject algorithm. If the transform_to_marginal_normality- component of the lg_object is TRUE, the replicates will be on the standard normal scale.

Usage

accept_reject(
  lg_object,
  condition,
  n_new,
  nodes,
  M = NULL,
  M_sim = 1500,
  M_corr = 1.5,
  n_corr = 1.2,
  return_just_M = FALSE,
  extend = 0.3
)

Arguments

lg_object	An object of type lg, as produced by the lg_main-function
condition	The value of the conditioning variables
n_new	The number of observations to generate
nodes	Either the number of equidistant nodes to generate, or a vector of nodes supplied by the user
M	The value for M in the accept-reject algorithm if already known
M_sim	The number of replicates to simulate in order to find a value for M
M_corr	Correction factor for M, to be on the safe side
n_corr	Correction factor for n_new, so that we mostly will generate enough observations in the first go
return_just_M	TRUE if we just want to find M, without actually generating any replications.
extend	How far to extend the grid beyond the extreme data points when interpolating, in share of the range

hotneim/lg documentation built on May 9, 2020, 7:35 a.m.