confIntKappa: Confidence intervals for weighted kappa and m >= 2 raters
In biostatUZH: Misc Tools of the Department of Biostatistics, EBPI, University of Zurich

Description Usage Arguments Details Value References See Also Examples

Compute confidence intervals for the coefficient of agreement for two nominal or ordered variables and two or more raters.

1 2	confIntKappa(dat, type = "not Cohen", weights = c("absolute", "squared")[1], M = 1000, conf.level = 0.95)

`dat`	Data frame that contains the ratings as columns.
`type`	Defines the type of confidence interval that is computed. If equal to "Cohen", then Cohen's unweighted kappa is computed, i.e. ratings are assumed to be nominal. If not equal to "Cohen", the weighted version for ordered ratings is computed.
`weights`	Define weights to be used if ordered ratings are compared. Only used if `type != "Cohen"`.
`M`	Number of bootstrap samples to be generated.
`conf.level`	Confidence level for confidence interval.

This function computes bootstrap confidence intervals for an unweighted or weight kappa coefficient, based on all pairwise complete observations in dat.

A list containing:

`n`	Number of observations used to compute confidence intervals.
`kappa`	Computed `kappa`.
`boot.quant`	Confidence interval based on quantiles of the bootstrap distribution.

Conger, A.J. (1980), Integration and generalisation of Kappas for multiple raters, Psychological Bulletin, 88, 322-328.

This function basically implements the example given for lkappa in package psy.

if (requireNamespace("psy")) {
    ## example comparable to that when called ?lkappa
    data("expsy", package = "psy")
    set.seed(1)
    confIntKappa(dat = expsy[,c(11,13,15)], type = "not Cohen", weights = "absolute", 
                 M = 200, conf.level = 0.95)
}