Bliss_Gibbs_Sampler: Bliss_Gibbs_Sampler

In 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)

Arguments

data	a list containing: Q an integer, the number of functional covariates. 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: 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 vector (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	write stuff if TRUE (optional).

Value

a list containing :

trace

a matrix, the trace of the Gibbs Sampler.

param

a list containing parameters used to run the function.

Examples

# May take a while
param_sim <- list(Q=1,n=25,p=50,grids_lim=list(c(0,1)),iter=1e4,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$trace[1,]
theta_1
# Resultat for few iterations
param_sim <- list(Q=1,n=25,p=50,grids_lim=list(c(0,1)),iter=5e2,K=2)
data_sim <- sim(param_sim,verbose=FALSE)
res_Bliss_Gibbs_Sampler <- Bliss_Gibbs_Sampler(data_sim,param_sim)
theta_1 <- res_Bliss_Gibbs_Sampler$trace[1,]
theta_1

bliss documentation built on March 18, 2022, 5:46 p.m.