posterior_params: Puntual Posterior Estimation of bpwpm2 beta parameters

In PaoloLuciano/bpwpm2: Bayesian Piece-wise Polynomial Package

Description

Given a model output by the function bpwpm_gibbs, it thins down the chain and makes puntual estimation for the parameters, given a type of estimation. This parameter object can later be used to calculate other results.

Usage

posterior_params(bpwpm, burn_in, thin, type = "mean")

Arguments

bpwpm	an object of the class bpwpm
burn_in	A burn in parameter for the MCMC Chain
thin	A thinning parameter for the MCMC Chain
type	The type of punctual estimation for the parameters. Options include: mean, mode or median.

Value

An object of the class bpwpm_params that contains:

β

The posterior estimation for β

τ

The corresponding nodes

F

The final F matrix

M

M parameter

J

J parameter

K

K parameter

d

number of dimentions

indep_terms

Logical

Examples

(model, 2, 2000, 'mean') (model, 0, 0, 'median')

PaoloLuciano/bpwpm2 documentation built on June 6, 2019, 5:47 p.m.