pa.coeff.raw: Percent agreement among multiple raters (2, 3, +) when the...
In irrCAC: Computing Chance-Corrected Agreement Coefficients (CAC)

Description Usage Arguments Value Examples

Percent agreement among multiple raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.

1 2	pa.coeff.raw(ratings, weights = "unweighted", categ.labels = NULL, conflev = 0.95, N = Inf)

`ratings`	An nxr matrix / data frame of ratings where each column represents one rater and each row one subject.
`weights`	is a mandatory parameter that is either a string variable or a matrix. The string describes one of the predefined weights and must take one of the values ("quadratic", "ordinal", "linear", "radical", "ratio", "circular", "bipolar"). If this parameter is a matrix then it must be a square matri qxq where q is the number of posssible categories where a subject can be classified. If some of the q possible categories are not used, then it is strobgly advised to specify the complete list of possible categories as a vector in parametr categ.labels. Otherwise, the program may not work.
`categ.labels`	An optional vector parameter containing the list of all possible ratings. It may be useful in case some of the possibe ratings are not used by any rater, they will still be used when calculating agreement coefficients. The default value is NULL. In this case, only categories reported by the raters are used in the calculations.
`conflev`	An optional parameter representing the confidence level associated with the confidence interval. Its default value is 0.95.
`N`	An optional parameter representing the population size (if any). It may be use to perform the final population correction to the variance. Its default value is infinity.

A data list containing 3 objects: (1) a one-row data frame containing the estimates, (2) the weight matrix used in the calculations, and (3) the categories used in the analysis. The data frame of estimates contains the following variables "coeff.name" (coefficient name), "pa" (the percent agreement), "pe" (percent chance-agreement-always equals 0), "coeff.val" (agreement coefficient = pa), coeff.se (the percent agreement standard error), "conf.int" (the percent agreement confidence interval), "p.value"(the percent agreement p-value), "w.name"(the weights' identification).

#The dataset "cac.raw4raters" comes with this package. Analyze it as follows:
cac.raw4raters
pa.coeff.raw(cac.raw4raters) #Percent agreement, precision measures, weights & categories
pa.coeff.raw(cac.raw4raters)$est #Yields percent agreement with precision measures
pa <- pa.coeff.raw(cac.raw4raters)$est$coeff.val #Yields percent agreement alone.
pa
pa.coeff.raw(cac.raw4raters, weights = "quadratic") #weighted percent agreement/quadratic weights