agree.ccc {agRee}

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. lower.v is the lower bound of degrees of freedom (df) of the MVT. upper.v is the upper bound of df of the MVT. Mu0 is the mean vector of multivariate normal prior of the location of the MVT and the default value is 0. Sigma0 is the variance matrix of multivariate normal prior of the location of the MVT and the default value is a diagonal matrix with diagonal entries equal to 10000. p is the df of wishart prior of inverse of the scale matrix of the MVT and the default value is the number of raters. V is the scale matrix of wishart prior of inverse of the scale matrix of the MVT and the default value is identity matrix. v is the initial value of the df of the MVT. Its default is NULL and for the default, the value will be generated by using the ECME Algorithm. Sigma is the initial value of the scale matrix of the MVT. Its default is NULL and for the default, the value will be generated by using the ECME Algorithm.

NAaction

a character string specifying what should happen when the data contain NAs. It must be one of "fail" and "omit" and may be abbreviated. The default is "fail" that causes the function to print an error message and terminate if there are any incomplete observations. If it is "omit", then the entire row(s) containing incomplete observation(s) will be deleted.

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

epi.ccc, cccvc, mvt.ecme

Examples

  data(judgeRatings)
  agree.ccc(judgeRatings[,2:3])