gac: Gwet's AC1 and AC2

In rel: Reliability Coefficients

Description

Calculates Gwet's AC as index of agreement for two observations of nominal, ordinal, or ratio scale data.

Usage

gac(data = NULL, kat = NULL, weight = c("unweighted","linear","quadratic","ratio"),
    conf.level = 0.95)

Arguments

data	A matrix with n subjects and two observations (n*2 matrix)
kat	Number of possible categories
weight	A character string specifying "unweighted", "linear", "quadratic" or "ratio", or a numeric kat*kat matrix with custom weights (see details).
conf.level	Confidence level of the interval.

Details

Gwet's AC has the advantage of not relying on independence between observations (Gwet, 2008), making it suitable for data with dependent measurements. Weights are based on weighted dissimilarities (diagonal = 1, off-diagonal < 1). Linear weights decrease equally with distance from the diagonal and quadratic weights decrease exponentially with distance from the diagonal. Custom weights should be specified as a kat*kat matrix with values <= 1. Incomplete cases are omitted listwise and the confidence interval is based on a t distribution.

Value

method	Analysis name
obs	Number of observations
sample	Sample size
est	Point estimate
se	Standard error
lb	Lower confidence boundary
ub	Upper confidence boundary
cont.table	contingency table
data	analyzed data

Author(s)

Riccardo Lo Martire

References

Gwet, K. L. (2008). Computing inter-rater reliability and its variance in the presence of high agreement. British Journal of Mathematical and Statistical Psychology 61, 29-48.

Examples

#Sample data: 200 subjects and one 5-category item.
data <- cbind(sample(1:5,200, replace=TRUE),sample(1:5,200, replace=TRUE))

#A numeric kat*kat matrix with custom weights 
cw <- diag(ncol(matrix(0,5,5)))
cw[cw!=diag(cw)] <- runif(20,0,1)

#AC1
gac(data=data, kat=5, weight="unweighted", conf.level = 0.95)

#AC2 with custom weights
gac(data=data, kat=5, weight=cw, conf.level = 0.95)

Example output

Call:
gac(data = data, kat = 5, weight = "unweighted", conf.level = 0.95)

       Estimate    StdErr   LowerCB UpperCB
Const -0.067695  0.031294 -0.129406  -0.006

Confidence level = 95%
Sample size = 200Call:
gac(data = data, kat = 5, weight = cw, conf.level = 0.95)

        Estimate     StdErr    LowerCB UpperCB
Const  0.0010905  0.0565333 -0.1103906  0.1126

Confidence level = 95%
Sample size = 200

rel documentation built on March 3, 2020, 9:07 a.m.