meta_sampling {MaxWiK}R Documentation

Function to get Approximate Bayesian Computation based on Maxima Weighted Isolation Kernel mapping

Description

The function meta_sampling() iteratively generates tracer based on the simple procedure:

The function MaxWiK.predictor() uses the meta-sampling for a prediction

The function get.MaxWiK() is used to get Approximate Bayesian Computation based on Maxima Weighted Isolation Kernel mapping. On given data frame of parameters, statistics of the simulations and an observation, using the internal parameters psi and t, the function get.MaxWiK() returns the estimation of a parameter corresponding to Maxima weighted Isolation Kernel ABC method.

Usage

meta_sampling(
  psi = 4,
  t = 35,
  param,
  stat.sim,
  stat.obs,
  talkative = FALSE,
  check_pos_def = FALSE,
  n_bullets = 16,
  n_best = 10,
  halfwidth = 0.5,
  epsilon = 0.001,
  rate = 0.1,
  max_iteration = 15,
  save_web = TRUE,
  use.iKernelABC = NULL
)

MaxWiK.predictor(
  psi = 4,
  t = 35,
  param,
  stat.sim,
  new.param,
  talkative = FALSE,
  check_pos_def = FALSE,
  n_bullets = 16,
  n_best = 10,
  halfwidth = 0.5,
  epsilon = 0.001,
  rate = 0.1,
  max_iteration = 15,
  save_web = TRUE,
  use.iKernelABC = NULL
)

get.MaxWiK(
  psi = 40,
  t = 350,
  param,
  stat.sim,
  stat.obs,
  talkative = FALSE,
  check_pos_def = TRUE,
  Matrix_Voronoi = NULL
)

Arguments

psi

Integer number. Size of each Voronoi diagram or number of areas/points in the Voronoi diagrams

t

Integer number of trees in the Isolation Forest

param

or par.sim - data frame of parameters of the model

stat.sim

Summary statistics of the simulations (model output)

stat.obs

Summary statistics of the observation point

talkative

Logical parameter to print or do not print messages

check_pos_def

Logical parameter to check the Gram matrix is positive definite or do not check

n_bullets

Number of generating points between two

n_best

Number of the best points to construct the next web net

halfwidth

Parameter for the algorithm of deleting of generated points

epsilon

Criterion to stop meta-sampling

rate

Rate to renew points in the web net of generated points

max_iteration

Maximum of iterations during meta-sampling

save_web

Logical to save all the generated points (web net)

use.iKernelABC

The iKernelABC object to use for meta-sampling. By default it is NULL and is generated.

new.param

New parameter for the predictor input

Matrix_Voronoi

is a predefined matrix of information about Voronoi trees (rows - trees, columns - Voronoi points/areas IDs). By default it is NULL and is generated randomly.

Value

The function meta_sampling() returns the list of the next objects:

The function MaxWiK.predictor() returns the list of the next objects:

The function get.MaxWiK() returns the list of :

Functions

Examples

MaxWiK::MaxWiK_templates(dir = tempdir()) # See the template 'MaxWiK.ABC.R' and 
# vignettes for usage.
MaxWiK::MaxWiK_templates(dir = tempdir()) # See the template 'MaxWiK.Predictor.R' 
# and vignettes for usage. 
MaxWiK::MaxWiK_templates(dir = tempdir()) # See the template 'MaxWiK.ABC.R' and 
# vignettes for usage.

[Package MaxWiK version 1.0.5 Index]