accept_reject {lg}

Generate sample from a conditional density estimate

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