llHPD {llbayesireg} | R Documentation |
Highest Posterior Density for the L-Logistic Bayesian Regression
Description
Compute the highest posterior density for the L-Logistic Bayesian Regression intervals of betas and deltas.
Usage
llHPD(fitll, prob = 0.95, chain = 1)
Arguments
fitll |
Object of class matrix with the llbayesireg function result. |
prob |
A number of quantiles of interest. The default is 0.95. |
chain |
Chain chosen for construction. The default is 1. |
Details
This function compute the highest posterior density intervals for a Bayesian posterior distribution.
Value
Object of class matrix with:
betas |
The highest posterior density intervals of betas. |
deltas |
The highest posterior density intervals of deltas. |
Author(s)
Sara Alexandre Fonsêca saralexandre@alu.ufc.br, Rosineide Fernando da Paz rfpaz2@gmail.com, Jorge Luís Bazán
Source
The L-Losgistic distribution was introduced by Tadikamalla and Johnson (1982), which refer to this distribution as Logit-Logistic distribution. Here, we have a new parameterization of the Logit-Logistic with the median as a parameter.
References
Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-Logistic Distribution: Properties, Inference and an Application to Study Poverty and Inequality in Brazil.
Examples
# Modelation the coeficient with generated data
library(llbayesireg)
library(llogistic)
# Number of elements to be generated
n=50
# Generated response
bin=2005
set.seed(bin)
y=rllogistic(n,0.5, 2)
fitll = llbayesireg(y, niter=100, jump=10)
llHPD(fitll)
# Modelation the coeficient with real data
library(llbayesireg)
data("Votes","MHDI")
y = Votes[,4]
X = MHDI
fitll = llbayesireg(y,X)
llHPD(fitll)