OrdNor: AnRPackage for Concurrent Generation of Correlated Ordinal and Normal Data

A Unified Bayesian Approach for Analyzing Correlated Ordinal Response Data

Generalized linear models with scale mixture of multivariate normal (SMMVN) link functions are considered to model correlated ordinal response data. A unified Bayesian approach, which includes prior elicitation and model comparison, is proposed. In order to incorporate available prior information, we propose a class of informative prior distributions on model parameters. The propriety of the pr...

Generating correlated discrete ordinal data using R and SAS IML

Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the perfo...

Modeling Paired Ordinal Response Data

 About 25 years ago, McCullagh proposed a method for modeling univariate ordinal responses. After publishing this paper, other statisticians gradually extended his method, such that we are now able to use more complicated but efficient methods to analyze correlated multivariate ordinal data, and model the relationship between these responses and host of covariates. In this paper, we aim to...

Simulating Correlated Multivariate Normal Data

Transition Models for Analyzing Longitudinal Data with Bivariate Mixed Ordinal and Nominal Responses

In many longitudinal studies, nominal and ordinal mixed bivariate responses are measured. In these studies, the aim is to investigate the effects of explanatory variables on these time-related responses. A regression analysis for these types of data must allow for the correlation among responses during the time. To analyze such ordinal-nominal responses, using a proposed weighting approach, an ...