compute_beta_posterior_density {bliss} R Documentation

## compute_beta_posterior_density

### Description

Compute the posterior density of the coefficient function.

### Usage

compute_beta_posterior_density(beta_sample, param, verbose = FALSE)


### Arguments

 beta_sample a matrix. Each row is a coefficient function computed from the posterior sample. param a list containing: grida numerical vector, the time points. lims_estimatea numerical vector, the time points. burninan integer (optional), the number of iteration to drop from the Gibbs sample. lims_kdean integer (optional), correspond to the lims option of the kde2d funtion. new_grida numerical vector (optional) to compute beta sample on a different grid. thinan integer (optional) to thin the posterior sample. verbose write stuff if TRUE (optional).

### Details

The posterior densities correponds to approximations of the marginal posterior distribitions (of beta(t) for each t). The sample is thinned in order to reduce the correlation and the computational time of the function kde2d.

### Value

An approximation of the posterior density on a two-dimensional grid (corresponds to the result of the kde2d function).

### Examples


library(RColorBrewer)
data(data1)
data(param1)
# result of res_bliss1<-fit_Bliss(data=data1,param=param1)
data(res_bliss1)
q <- 1
param_beta_density <- list(grid= data1[["grids"]][[q]],
iter= param1[["iter"]],
p   = param1[["p"]][q],
n        = length(data1[["y"]]),
thin     = param1[["thin"]],
burnin   = param1[["burnin"]],
lims_kde = param1[["lims_kde"]][[q]],
new_grid = param1[["new_grids"]][[q]],
lims_estimate = range(res_bliss1$Smooth_estimate[[q]])) density_estimate <- compute_beta_posterior_density(res_bliss1$beta_sample[[q]],param_beta_density)
image(density_estimate$grid_t, density_estimate$grid_beta_t,
density_estimate\$density,col=rev(heat.colors(100)))



[Package bliss version 1.0.4 Index]