llHPD function

Highest Posterior Density for the L-Logistic Bayesian Regression

Compute the highest posterior density for the L-Logistic Bayesian Regression intervals of betas and deltas.

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.

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.

Returns

Object of class matrix with: - betas: The highest posterior density intervals of betas.

• deltas: The highest posterior density intervals of deltas.

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.

Author(s)

Sara Alexandre Fonsêca saralexandre@alu.ufc.br , Rosineide Fernando da Paz rfpaz2@gmail.com , Jorge Luís Bazán

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)