posterior_params: Puntual Posterior Estimation of bpwpm Parameters

In PaoloLuciano/bpwpm: Bayesian Piecewise Polinomial Model

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 the F matrix and other results.

Usage

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

Arguments

bpwpm	an object of the class bpwpm
thin	A thinning parameter for the MCMC Chain
burn_in	A burn in 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

w

The posterior estimation for w's on each dimention

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/bpwpm documentation built on May 20, 2019, 4:25 p.m.