predict_pg_mvgp_univariate: Bayesian Polya-gamma regression prediction
In jtipton25/pgR: Bayesian Polya-Gamma Regression

View source: R/predict-pg_mvgp_univariate.R

predict_pg_mvgp_univariate

R Documentation

Bayesian Polya-gamma regression prediction

Description

this function generates predictions from the Bayesian multinomial regression using Polya-gamma data augmentation

Usage

predict_pg_mvgp_univariate(
  out,
  X,
  X_pred,
  locs,
  locs_pred,
  corr_fun = "exponential",
  n_cores = 1L,
  progress = TRUE,
  verbose = FALSE,
  posterior_mean_only = TRUE
)

Arguments

`out`	is a list of MCMC outputs from pgSPLM
`X`	is a n x p matrix of covariates at the observed locations.
`X_pred`	is a n_{pred} x p matrix of covariates at the locations where predictions are to be made.
`locs`	is a n x 2 matrix of locations where observations were taken.
`locs_pred`	is a n_pred x 2 matrix of locations where predictions are to be made.
`corr_fun`	is a character that denotes the correlation function form. Current options include "matern" and "exponential".
`n_cores`	is the number of cores for parallel computation using openMP.
`progress`	is a logicial input that determines whether to print a progress bar.
`verbose`	is a logicial input that determines whether to print more detailed messages.
`posterior_mean_only`	is a logical input that flags whether to generate the full posterior predictive distribution (`posterior_mean_only = FALSE`) or just the posterior predictive distribution of the mean response (`posterior_mean_only = TRUE`). For large dataset, the full posterior predictive distribution can be expensive to compute and the posterior distribution of the mean response is much faster to calculte.