zitobitQR | R Documentation |
This function estimates a bayesian quantile regression model with a discrete component at zero, where all zero observations are assumed to distributed according to a mixed discrete-continuous distribution.
zitobitQR(
formula,
tau = 0.5,
data,
itNum,
thin = 1,
betaValue = NULL,
sigmaValue = 1,
gammaValue = NULL,
sigmaGamma = 0.5,
link = 1,
priorVar = 100,
refresh = 100,
quiet = FALSE,
burnin = 50
)
formula |
a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right. |
tau |
Quantile of interest. |
data |
a data.frame from which to find the variables defined in the formula |
itNum |
Number of iterations. |
thin |
Thinning parameter. |
betaValue |
Initial values for the parameter beta for the continuous part. |
sigmaValue |
Initial value for the scale parameter. |
gammaValue |
Initial value for the parameter gamma of the discrete part. |
sigmaGamma |
Tuning parameter for the Metropolis-Hastings step. |
link |
Integer defining the link function used for the probability model. Default is 1. for the logit link function. |
priorVar |
Value that multiplies a identity matrix in the elicition process of the prior variance of the regression parameters. |
refresh |
Interval between printing a message during the iteration process. Default is set to 100. |
quiet |
Logical. If FALSE (default) it will print messages depending on the refresh parameter to show that the Markov chain is updating. If TRUE it will not print messages during the iteration process. |
burnin |
Size of the burnin only for the indicator variable of the censoring mechanism. For all other chains, this number will not be used. Default value is 50. |
A list with the chains of all parameters of interest.
Santos and Bolfarine (2015) - Bayesian quantile regression analysis for continuous data with a discrete component at zero. Preprint. http://arxiv.org/abs/1511.05925
## Not run:
set.seed(1)
data("BrazilDurableGoods")
# Change the number of iterations for better results.
model <- zitobitQR(expenditure ~ age + education, tau=0.5,
data=BrazilDurableGoods, itNum=100,
sigmaGamma=0.10, refresh=20)
## End(Not run)
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