ordinalRR.sim: Simulate an ordinal R&R data set.

In ordinalRR: Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements

Description

This function accepts design parameters and model parameters for the random-effects model from Section 3 of Culp, Ryan, Chen, and Hamada (2018) and outputs a simulated R&R data set in preprocessed form.

Usage

ordinalRR.sim(H=4L,I=30L,J=3L,K=2L,mu.a=2.6,sigma.a=.2,lambda=c(11,44,29,40),seed=10L)

Arguments

H	‘positive integer’ length of the H-point ordinal scale on {1,...,H}.
I	‘positive integer’ number of parts.
J	‘positive integer’ number of raters.
K	‘positive integer’ number of repetitions per rater.
mu.a	‘scalar’ mean of log(alpha_j).
sigma.a	‘positive scalars’ standard deviation of log(alpha_j).
lambda	‘vector of length H of positive scalars’ Dirichlet parameters used to induce a distribution on cutpoints.
seed	‘positive integer’ used to set R's seed for pseudo-random number generation.

Value

I	‘positive integer’ number of parts.
J	‘positive integer’ number of raters.
K	‘positive integer’ number of repetitions per rater.
H	‘positive integer’ length of the H-point ordinal scale on {1,...,H}.
x	‘data.frame’ containing J*K columns and entries from the H-point ordinal scale 1:H. Each part is a row, and there are blocks of K adjacent columns for the repetitions of each of J raters, e.g., rater 1's columns are the first K and rater J's columns are the last K.
R	‘array’ x is expanded into a 3-dimensional array (i.e., part 1:I, operator 1:J, ordinal value 1:H) with multinomial counts.
preprocess	‘Boolean’ will be TRUE if the data are ready for input into function ordinaRR() for Bayesian analysis with JAGS.

Author(s)

Ken Ryan

References

Culp, S.L., Ryan, K.J., Chen, J., and Hamada, M.S. (2018). “Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements.” Technometrics, doi:10.1080/00401706.2018.1429317.

See Also

preprocess

Examples

ordinalRR.sim()

ordinalRR documentation built on March 31, 2020, 5:09 p.m.