compute_chains_info {bliss} R Documentation

## compute_chains_info

### Description

Compute summaries of Gibbs Sampler chains.

### Usage

compute_chains_info(chain, param)


### Arguments

 chain a list given by the Bliss_Gibbs_Sampler function. param a list containing: Ka vector of integers, corresponding to the numbers of intervals for each covariate. gridsa numerical vector, the observation time points. basisa vector of characters (optional) among : "uniform" (default), "epanechnikov", "gauss" and "triangular" which correspond to different basis functions to expand the coefficient function and the functional covariates.

### Value

Return a list containing the estimates of mu and sigma_sq, the Smooth estimate and the chain autocorrelation for mu, sigma_sq and beta.

### Examples


param_sim <- list(Q=1,
n=100,
p=c(50),
grids_lim=list(c(0,1)))
data <- sim(param_sim,verbose=TRUE)

param <- list(iter=5e2,
K=c(3),
n_chains = 3)
res_bliss <- fit_Bliss(data,param,verbose=TRUE,compute_density=FALSE,sann=FALSE)

param$grids <- data$grids
chains_info1 <- compute_chains_info(res_bliss$chains[[1]],param) chains_info2 <- compute_chains_info(res_bliss$chains[[2]],param)
chains_info3 <- compute_chains_info(res_bliss$chains[[3]],param) # Smooth estimates ylim <- range(range(chains_info1$estimates$Smooth_estimate), range(chains_info2$estimates$Smooth_estimate), range(chains_info3$estimates$Smooth_estimate)) plot(data$grids[[1]],chains_info1$estimates$Smooth_estimate,type="l",ylim=ylim,
xlab="grid",ylab="")
lines(data$grids[[1]],chains_info2$estimates$Smooth_estimate,col=2) lines(data$grids[[1]],chains_info3$estimates$Smooth_estimate,col=3)

# Autocorrelation
plot(chains_info1\$autocorr_lag[,1],type="h")



[Package bliss version 1.0.4 Index]