Q_IS_BLR: Q Importance Sampling Step
In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion

Q_IS_BLR

R Documentation

Q Importance Sampling Step

Description

Q Importance Sampling weighting for Bayesian logistic regression

Usage

Q_IS_BLR(
  particle_set,
  m,
  time,
  dim,
  data_split,
  prior_means,
  prior_variances,
  C,
  proposal_cov,
  precondition_matrices,
  inv_precondition_matrices,
  cv_location = "hypercube_centre",
  diffusion_estimator,
  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

`particle_set`	particles set prior to Q importance sampling step
`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
`proposal_cov`	proposal covariance of Gaussian distribution for Fusion
`precondition_matrices`	list of length m, where precondition_matrices[[c]] is the precondition matrix for sub-posterior c
`inv_precondition_matrices`	list of length m, where inv_precondition_matrices[[c]] is the inverse precondition matrix for sub-posterior c
`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