Description Usage Arguments Details Value References Examples

This function computes the average covariate effect for different outcomes of the ORII model at the specified quantiles. The covariate effects are calculated marginally of the parameters and the remaining covariates.

1 | ```
covariateEffect_or2(model, y, x, modX, gamma, p)
``` |

`model` |
outcome of the ORII (quantreg_or2) model. |

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

`x` |
covariate matrix of dimension |

`modX` |
matrix x with suitable modification to an independent variable including a column of ones with or without column names. If the covariate of interest is continuous, then add the incremental change to each observation in the column for the covariate of interest. If the covariate is an indicator variable, then replace the column for the covariate of interest with a column of ones. |

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

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

This function computes the average covariate effect for different outcomes of the ORII model at the specified quantiles. The covariate effects are calculated marginally of the parameters and the remaining covariates. The computation of covariate effects utilizes the MCMC outputs from estimation.

Returns a list with components:

`avgDiffProb`

: vector with change in predicted probabilities for each outcome category.

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

Jeliazkov, I., Graves, J., and Kutzbach, M. (2008). “Fitting and Comparison of Models for Multivariate Ordinal Outcomes.” Advances in Econometrics: Bayesian Econometrics, 23: 115–156. DOI: 10.1016/S0731-9053(08)23004-5

Jeliazkov, I., and Rahman, M. A. (2012). “Binary and Ordinal Data Analysis in Economics: Modeling and Estimation” in Mathematical Modeling with Multidisciplinary Applications, edited by X.S. Yang, 123-150. John Wiley & Sons Inc, Hoboken, New Jersey. DOI: 10.1002/9781118462706.ch6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
set.seed(101)
data("data25j3")
x <- data25j3$x
y <- data25j3$y
k <- dim(x)[2]
output <- quantreg_or2(y, x, b0 = 0, B0 = 10*diag(k), n0 = 5, d0 = 8, gamma = 3,
mcmc = 50, p = 0.25, display = FALSE)
modX <- x
modX[,3] <- modX[,3] + 0.02
res <- covariateEffect_or2(output, y, x, modX, gamma = 3, p = 0.25)
# Summary of Covariate Effect:
# Covariate Effect
# Category_1 -0.0074
# Category_2 -0.0029
# Category_3 0.0104
``` |

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