psp_control {psp} | R Documentation |
Control the behaviour of the psp_global implementation
Description
psp_control
allows users to define characteristics of the
parameter space partitioning MCMC algorithm as implemented in
psp_global
.
Usage
psp_control(radius = 0.1, init, lower, upper,
pop = 400, cl = NULL,
param_names = NULL,
parallel = FALSE,
cluster_names = NULL,
export_objects = NULL,
export_libs = NULL,
iterations = 1000)
Arguments
radius |
The radius of the hypershere with n-dimensions to sample from. Must be a double or a numeric vector, where elements correspond to parameters in 'init, lower, upper'. Default is 0.1. |
init |
A vector of parameters to use as the first jumping distribution. |
lower , upper |
Vectors specifiying the lower and upper boundaries of the parameter space for each parameter. The i-th element of lower and upper bounds applies to the i-th parameter. |
pop |
The minimum population psp_global aims to find for each ordinal
pattern discovered. This can stop the parameter search early in case
the population of all ordinal pattern are equal to or larger than
|
parallel |
If TRUE, uses the parallel package to run evaluations of
jumping distributions for each chain parallel. Default value is
|
cl |
If parallel is TRUE, the number of cores to use for
|
param_names |
A character vector that includes the names of each
parameter. If |
cluster_names |
Maintained for backwards-compatibility. See
|
export_objects |
A character vector that includes all of the objects
to be loaded into each cluster. It is handled by
|
export_libs |
A character vector that includes all the packages to
be loaded into each cluster. It is handled by
|
iterations |
The number of global iterations for psp_global. Default is 1000. |
Value
Returns a control list suitable for psp_global
with the above
elements.
Examples
# two parameter model
psp_control(lower = rep(0, 2), upper = rep(1, 2), init = rep(0.5, 2),
radius = rep(0.25, 2), cluster_names = NULL,
parallel = FALSE, iterations = 500)