llHPD: Highest Posterior Density for the L-Logistic Bayesian...

In llbayesireg: 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 Fons<c3><aa>ca [e-mail], Rosineide Fernando da Paz [e-mail], Jorge Lu<c3><ad>s Baz<c3><a1>n

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 Baz<c3><a1>n, 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)
 

llbayesireg documentation built on May 1, 2019, 9:13 p.m.