Nothing
#' @name data25j3
#' @title Simulated data from the OR2 model for \eqn{p = 0.25} (i.e., 25th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 3 outcomes at the 25th quantile (i.e., \eqn{p = 0.25}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 6, 5)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.25}). The cut-points \eqn{(0, 3)} are used to classify the
#' continuous values of the dependent variable into 3 categories, which form the ordinal outcomes.
#'
#' @docType data
#'
#' @usage data(data25j3)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name data50j3
#' @title Simulated data from the OR2 model for \eqn{p = 0.5} (i.e., 50th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 3 outcomes at the 50th quantile (i.e., \eqn{p = 0.5}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 6, 5)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.5}). The cut-points \eqn{(0, 3)} are used to classify the
#' continuous values of the dependent variable into 3 categories, which form the ordinal outcomes.
#'
#' @docType data
#' @usage data(data50j3)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name data75j3
#' @title Simulated data from the OR2 model for \eqn{p = 0.75} (i.e., 75th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 3 outcomes at the 75th quantile (i.e., \eqn{p = 0.75}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 6, 5)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.75}). The cut-points \eqn{(0, 3)} are used to classify the
#' continuous values of the dependent variable into 3 categories, which form the ordinal outcomes.
#'
#' @docType data
#' @usage data(data75j3)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name data25j4
#' @title Simulated data from the OR1 model for \eqn{p = 0.25} (i.e., 25th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 4 outcomes at the 25th quantile (i.e., \eqn{p = 0.25}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 5, 6)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.25}). The cut-points \eqn{(0, 2, 4)} are used to classify the
#' continuous values of the dependent variable into 4 categories, which form the ordinal outcomes.
#'
#' @docType data
#' @usage data(data25j4)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name data50j4
#' @title Simulated data from the OR1 model for \eqn{p = 0.5} (i.e., 50th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 4 outcomes at the 50th quantile (i.e., \eqn{p = 0.5}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 5, 6)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.5}). The cut-points \eqn{(0, 2, 4)} are used to classify the
#' continuous values of the dependent variable into 4 categories, which form the ordinal outcomes.
#'
#' @docType data
#' @usage data(data50j4)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name data75j4
#' @title Simulated data from the OR1 model for \eqn{p = 0.75} (i.e., 75th quantile)
#'
#' @details
#'
#' This data contains 500 observations generated from a quantile
#' ordinal model with 4 outcomes at the 75th quantile (i.e., \eqn{p = 0.75}).
#' The model specifics for generating the data are as follows: \eqn{\beta = (-4, 5, 6)}, X ~ Unif(0, 1), and
#' \eqn{\epsilon} ~ AL(\eqn{0, \sigma = 1, p = 0.75}). The cut-points \eqn{(0, 2, 4)} are used to classify the
#' continuous values of the dependent variable into 4 categories, which form the ordinal outcomes.
#'
#' @docType data
#' @usage data(data75j4)
#'
#' @return Returns a list with components
#' \item{\code{x}: }{a matrix of covariates, including a column of ones.}
#' \item{\code{y}: }{a column vector of ordinal outcomes.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Zhang, J. (2005). `"A Three-Parameter Asymmetric Laplace Distribution."`
#' Communications in Statistics - Theory and Methods, 34(9-10), 1867-1879. DOI: 10.1080/03610920500199018
#'
#' @keywords datasets
#'
#' @seealso \link[MASS]{mvrnorm}, Asymmetric Laplace Distribution
#'
NULL
#' @name Educational_Attainment
#' @title Educational Attainment study based on data from the National Longitudinal Study of Youth (NLSY, 1979) survey.
#'
#' @details
#'
#' This data is taken from the National Longitudinal Study of Youth (NLSY, 1979)
#' survey and corresponds to 3,923 individuals. The objective is to study the
#' effect of family background, individual, and school level variables on the
#' quantiles of educational attainment conditional on the covariates. The dependent variable
#' i.e. the educational degree, has four categories given as less than high school, high school degree,
#' some college or associate's degree, and college or graduate degree. The independent
#' variables include intercept, square root of family income, mother's education,
#' father's education, mother's working status, gender, race, and whether the youth
#' lived in an urban area at the age of 14, and indicator variables to control for age-cohort
#' effects.
#'
#' @docType data
#'
#' @usage data(Educational_Attainment)
#'
#' @return Returns data with components
#' \item{\code{mother_work}: }{Indicator for working female at the age of 14.}
#' \item{\code{urban}: }{Indicator for the youth living in urban area at the age of 14.}
#' \item{\code{south}: }{Indicator for the youth living in South at the age of 14.}
#' \item{\code{father_educ}: }{Number of years of father's education.}
#' \item{\code{mother_educ}: }{Number of years of mother's education.}
#' \item{\code{fam_income}: }{Family income of the household in $1000.}
#' \item{\code{female}: }{Indicator for individual's gender.}
#' \item{\code{black}: }{Indicator for black race.}
#' \item{\code{age_cohort_2}: }{Indicator variable for age 15.}
#' \item{\code{age_cohort_3}: }{Indicator variable for age 16.}
#' \item{\code{age_cohort_4}: }{Indicator variable for age 17.}
#' \item{\code{dep_edu_level}: }{Four categories of educational attainment: less than high school, high school degree,
#' some college or associate's degree, and college or graduate degree.}
#'
#' @references 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
#'
#' @keywords datasets
#'
#' @seealso \href{https://www.bls.gov/nls/nlsy97.htm}{Survey Process}.
#'
NULL
#' @name Policy_Opinion
#' @title Data contains public opinion on the proposal to raise federal income taxes for couples (individuals) earning more than $250,000 ($200,000) per year and a host of other covariates. The data is taken from the 2010-2012 American National Election Studies (ANES) on the Evaluation of Government and Society Study I (EGSS 1)
#'
#' @details
#'
#' The data consists of 1,164 observations taken from the 2010-2012 American National Election
#' Studies (ANES) on the Evaluations of Government and Society Study 1 (EGSS 1). The objective
#' is to analyze public opinion on the proposal to raise federal income taxes for couples (individuals)
#' earning more than $250,000 ($200,000) per year. The responses were recorded as oppose, neither
#' favor nor oppose, or favor the tax increase, and forms the dependent variable in the study. The
#' independent variables include indicator variables (or dummy) for employment, income above
#' $75,000, bachelor's and post-bachelor's degree, computer ownership, cellphone ownership, and white race.
#'
#' @docType data
#'
#' @usage data(Policy_Opinion)
#'
#' @return Returns data with components
#' \item{\code{Intercept}: }{Column of ones.}
#' \item{\code{EmpCat}: }{Indicator for employment status.}
#' \item{\code{IncomeCat}: }{Indicator for household income > $75,000.}
#' \item{\code{Bachelors}: }{Individual's highest degree is Bachelors.}
#' \item{\code{Post.Bachelors}: }{Indicator for highest degree is Masters, Professional or Doctorate.}
#' \item{\code{Computers}: }{Indicator for computer ownership by individual or household.}
#' \item{\code{CellPhone}: }{Indicator for cellphone ownership by individual or household.}
#' \item{\code{White}: }{Indicator for White race.}
#' \item{\code{y}: }{Public opinion on the proposal to raise federal income taxes. The three categories are: oppose, neither
#' favor nor oppose, or favor the tax increase.}
#'
#' @references Rahman, M. A. (2016). `"Bayesian Quantile Regression for Ordinal Models."`
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' @keywords datasets
#'
#' @seealso \href{https://electionstudies.org/data-center/}{ANES}, \href{https://georgewbush-whitehouse.archives.gov/cea/progrowth.html}{Tax Policy}
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.