parallel_fusion_SMC_BLR: Generalised Monte Carlo Fusion [parallel]

In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion

Generalised Monte Carlo Fusion [parallel]

Description

Generalised Monte Carlo Fusion for Bayesian Logistic Regression

Usage

parallel_fusion_SMC_BLR(
  particles_to_fuse,
  N,
  m,
  time,
  dim,
  data_split,
  prior_means,
  prior_variances,
  C,
  precondition_matrices,
  resampling_method = "multi",
  ESS_threshold = 0.5,
  cv_location = "hypercube_centre",
  diffusion_estimator = "Poisson",
  beta_NB = 10,
  gamma_NB_n_points = 2,
  local_bounds = TRUE,
  seed = NULL,
  n_cores = parallel::detectCores(),
  cl = NULL,
  level = 1,
  node = 1,
  print_progress_iters = 1000
)

Arguments

particles_to_fuse	list of length m, where particles_to_fuse[c] contains the particles for the c-th sub-posterior. Can initialise a this from list of sub-posterior samples by using the initialise_particle_sets function
N	number of samples
m	number of sub-posteriors to combine
time	time T for fusion algorithm
dim	dimension of the predictors (= p+1)
data_split	list of length m where each item is a list of length 4 where for c=1,...,m, data_split[[c]]$y is the vector for y responses and data_split[[c]]$X is the design matrix for the covariates for sub-posterior c, data_split[[c]]$full_data_count is the unique rows of the full data set with their counts and data_split[[c]]$design_count is the unique rows of the design matrix and their counts
prior_means	prior for means of predictors
prior_variances	prior for variances of predictors
C	overall number of sub-posteriors
precondition_matrices	list of length m, where precondition_matrices[[c]] is the precondition matrix for sub-posterior c
resampling_method	method to be used in resampling, default is multinomial resampling ('multi'). Other choices are stratified resampling ('strat'), systematic resampling ('system'), residual resampling ('resid')
ESS_threshold	number between 0 and 1 defining the proportion of the number of samples that ESS needs to be lower than for resampling (i.e. resampling is carried out only when ESS < N*ESS_threshold)
cv_location	string to determine what the location of the control variate should be. Must be either 'mode' where the MLE estimator will be used or 'hypercube_centre' (default) to use the centre of the simulated hypercube
diffusion_estimator	choice of unbiased estimator for the Exact Algorithm between "Poisson" (default) for Poisson estimator and "NB" for Negative Binomial estimator
beta_NB	beta parameter for Negative Binomial estimator (default 10)
gamma_NB_n_points	number of points used in the trapezoidal estimation of the integral found in the mean of the negative binomial estimator (default is 2)
local_bounds	logical value indicating if local bounds for the phi function are used (default is TRUE)
seed	seed number - default is NULL, meaning there is no seed
n_cores	number of cores to use
cl	an object of class "cluster" for parallel computation in R. If none is passed, then one is created and used within this function
level	indicates which level this is for the hierarchy (default 1)
node	indicates which node this is for the hierarchy (default 1)
print_progress_iters	number of iterations between each progress update (default is 1000). If NULL, progress will only be updated when importance sampling is finished

Value

A list with components:

particles

particles returned from fusion sampler

proposed_samples

proposal samples from fusion sampler

time

run-time of fusion sampler

ESS

effective sample size of the particles after each step

CESS

conditional effective sample size of the particles after each step

resampled

boolean value to indicate if particles were resampled after each time step

precondition_matrices

list of length 2 where precondition_matrices[[2]] are the pre-conditioning matrices that were used and precondition_matrices[[1]] are the combined precondition matrices

combined_data

combined data for the fusion density

rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.