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