agree.ccc {agRee} | R Documentation |
Concordance Correlation Coefficient
Description
Obtain confidence interval and point estimate of the concordance correlation coefficient (CCC) proposed in Lin (1989).
Usage
agree.ccc(ratings, conf.level=0.95,
method=c("jackknifeZ", "jackknife",
"bootstrap","bootstrapBC",
"mvn.jeffreys", "mvn.conjugate",
"mvt", "lognormalNormal", "mvsn", "mvst"),
nboot=999, nmcmc=10000,
mvt.para=list(prior=list(lower.v=4, upper.v=25,
Mu0=rep(0, ncol(ratings)),
Sigma0=diag(10000, ncol(ratings)),
p=ncol(ratings),
V=diag(1, ncol(ratings))),
initial=list(v=NULL, Sigma=NULL)),
NAaction=c("fail", "omit"))
Arguments
ratings |
a matrix of observations with one subject per row and one rater per column. |
conf.level |
confidence level of the interval. The default is 0.95. |
method |
a character string specifying the method used to obtain the estimate of the CCC. It must be one of "jackknifeZ", "jackknife", "bootstrap", "bootstrapBC", "mvn.jeffreys", "mvn.conjugate","mvt", "lognormalNormal", "mvsn", and "mvst". It can be abbreviated. The default is "jackknifeZ". |
nboot |
number of bootstrap replicates. The default value is 999. |
nmcmc |
number of iterations used in the Bayesian approach. The default value is 10000. |
mvt.para |
values of hyper-parameters and initial values of
parameters for multivariate t (MVT) distribution.
|
NAaction |
a character string specifying what should happen
when the data contain |
Details
To obtain point estimate and confidence interval, the methods available include the jackknife method with and without Z-transformation, the bootstrap, and the Bayesian approach for the multivariate normal, multivariate t, lognormal-normal, multivariate skew normal, and multivariate skew t distributions.
Value
Point estimate and lower and upper bounds of the confidence interval of the CCC.
References
Dai Feng, Richard Baumgartner and Vladimir Svetnik (2016) Estimating the concordance correlation coefficient using a unified Bayesian framework under review
Dai Feng, Richard Baumgartner and Vladimir Svetnik (2015) A Bayesian estimate of the concordance correlation coefficient with skewed data. Pharmaceutical Statistics, DOI: 10.1002/pst.1692
Dai Feng, Richard Baumgartner and Vladimir Svetnik (2015) A robust Bayesian estimate of the concordance correlation coefficient. Journal of Biopharmaceutical Statistics 25(3) 490-507, DOI: 10.1080/10543406.2014.920342
Dai Feng, Vladimir Svetnik, Alexandre Coimbra and Richard Baumgartner (2014) A comparison of confidence interval methods for the concordance correlation coefficient and intraclass correlation coefficient with small number of raters. Journal of Biopharmaceutical Statistics 24(2) 272-293, DOI: 10.1080/10543406.2013.863780.
Dai Feng, Richard Baumgartner and Vladimir Svetnik (2014) A short note on jackknifing the concordance correlation coefficient. Statistics in Medicine 33(3) 514-516, DOI: 10.1002/sim.5931
Lawrence I-Kuei Lin (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45 255-268
See Also
Examples
data(judgeRatings)
agree.ccc(judgeRatings[,2:3])