Bliss_Gibbs_Sampler: Bliss_Gibbs_Sampler

In pmgrollemund/bliss: Bayesian Functional Linear Regression with Sparse Step Functions

Bliss_Gibbs_Sampler

Description

A Gibbs Sampler algorithm to sample the posterior distribution of the Bliss model.

Usage

Bliss_Gibbs_Sampler(data, param, verbose = FALSE, to_sample = "posterior")

Arguments

data	a list containing: y a numerical vector, the outcome values y_i. x a list of matrices, the qth matrix contains the observations of the qth functional covariate at time points given by grids. grids a list of numerical vectors, the qth vector is the grid of time points for the qth functional covariate.
param	a list containing: Q an integer, the number of functional covariates. iter an integer, the number of iterations of the Gibbs sampler algorithm. K a vector of integers, corresponding to the numbers of intervals for each covariate. p an integer, the number of time points. basis a character (optional). The possible values are "uniform" (default), "epanechnikov", "gauss" and "triangular" which correspond to different basis functions to expand the coefficient function and the functional covariates phi_l a numerical (optional). An hyperparameters related to the exponential prior on the length of the intervals. Lower values promotes wider intervals. verbose_cpp a boolean value (optional). Write stuff from the Rcpp scripts if TRUE.
verbose	write stuff if TRUE (optional).
to_sample	indicates to sample the posterior distribtion ("posterior") or the prior distribution ("prior").

Value

a list containing :

trace

a matrix, the trace of the Gibbs Sampler.

param

a list containing parameters used to run the function.

Examples

param_sim <- list(Q=1,n=25,p=50,grids_lim=list(c(0,1)),iter=2e2,K=2)
data_sim <- sim(param_sim,verbose=FALSE)
res_Bliss_Gibbs_Sampler <- Bliss_Gibbs_Sampler(data_sim,param_sim)
theta_1 <- res_Bliss_Gibbs_Sampler$trace[1,]
theta_1

pmgrollemund/bliss documentation built on July 15, 2024, 5:02 p.m.