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ordinalRR (version 1.1)

Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements

Description

Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.

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Version

Install

install.packages('ordinalRR')

Monthly Downloads

209

Version

1.1

License

GPL-2

Maintainer

Ken Ryan

Last Published

March 30th, 2020

Functions in ordinalRR (1.1)

preprocess

Format an ordinal R&R data frame into object required by function ordinalRR.
summary.ordinalRR

Summarize an object of class ordinalRR.
print.ordinalRR

Print function for an object of class ordinalRR.
ordinalRR.control

Set control parameters for a Bayesian ordinal R&R model.
ordinalRR.sim

Simulate an ordinal R&R data set.
compute.q

Compute the probabilities for a single rater at a fixed part quality.
density.ordinalRR

Plot densities of the latent part distributions.
make.rater

Format the parameters for a single rater.
hist.ordinalRR

Histogram for the latent part distributions from a Bayesian ordinal R&R analysis.
followup

Followup data from experiment on soldered joints.
ordinalRR

Fit a Bayesian ordinal R&R model using JAGS.