bqror | R Documentation |

Package provides functions for estimating Bayesian quantile regression with ordinal outcomes, computing the covariate effects, model comparison measures, and inefficiency factor. The generic ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using Gibbs sampling only. For each model framework, there is a specific function for estimation. The summary output produces estimates for regression quantiles and two measures of model comparison — log of marginal likelihood and Deviance Information Criterion (DIC). The package also has specific functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models

*Package: bqror*

*Type: Package*

*Version: 1.4.0*

*License: GPL (>=2)*

Package **bqror** provides the following functions:

For an ordinal model with three or more outcomes:

`quantregOR1`

, `covEffectOR1`

,
`logMargLikeOR1`

, `devianceOR1`

,
`qrnegLogLikensumOR1`

, `infactorOR1`

,
`qrminfundtheorem`

, `drawbetaOR1`

,
`drawwOR1`

, `drawlatentOR1`

,
`drawdeltaOR1`

, `alcdfstd`

,
`alcdf`

, `logLik.bqrorOR1`

For an ordinal model with three outcomes:

`quantregOR2`

, `covEffectOR2`

,
`logMargLikeOR2`

, `devianceOR2`

,
`qrnegLogLikeOR2`

, `infactorOR2`

,
`drawlatentOR2`

, `drawbetaOR2`

,
`drawsigmaOR2`

, `drawnuOR2`

,
`rndald`

Mohammad Arshad Rahman

Prajual Maheshwari <prajual1391@gmail.com>

Rahman, M. A. (2016). “Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939

Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447. DOI: 10.1016/S0167-7152(01)00124-9

Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50. DOI: 10.2307/1913643

Greenberg, E. (2012). “Introduction to Bayesian Econometrics.” Cambridge University Press. Cambridge, DOI: 10.1017/CBO9781139058414

rgig, mvrnorm, ginv, rtruncnorm, mvnpdf, rinvgamma, mldivide, rand, qnorm, rexp, rnorm, std, sd, acf, Reshape, progress_bar, dinvgamma, logLik

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