Description Usage Arguments Details Value References See Also Examples

This function computes the logarithm of marginal likelihood for ordinal quantile model with 3 outcomes using Gibbs output from the complete and reduced runs.

1 2 | ```
logMargLikelihood_or2(y, x, b0, B0, n0, d0, postMeanbeta, postMeansigma,
btildeStore, BtildeStore, gamma, p)
``` |

`y` |
observed ordinal outcomes, column vector of dimension |

`x` |
covariate matrix of dimension |

`b0` |
prior mean for normal distribution to sample |

`B0` |
prior variance for normal distribution to sample |

`n0` |
prior for shape parameter to sample |

`d0` |
prior for scale parameter to sample |

`postMeanbeta` |
a vector with mean of sampled |

`postMeansigma` |
a vector with mean of sampled |

`btildeStore` |
a storage matrix for posterior mean of |

`BtildeStore` |
a storage matrix for posterior variance of |

`gamma` |
one and only cut-point other than 0. |

`p` |
quantile level or skewness parameter, p in (0,1). |

Function computes the logarithm of marginal likelihood for ordinal model with 3 outcomes using a Gibbs sampling procedure.

Returns a scalar for logarithm of marginal likelihood

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

Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. DOI: 10.1080/01621459.1995.10476635

Greenberg, E. (2012). “Introduction to Bayesian Econometrics.” Cambridge University Press, Cambridge. DOI: 10.1017/CBO9780511808920

dinvgamma, mvnpdf, dnorm, Gibbs sampling

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