library(irr)

# two datasets available
# first: 6 raters classified 30 patients into 5 nominal categories 
data(diagnoses)
str(diagnoses)
head(diagnoses[, 1:3])

# second: 3 raters classified 20 subjects on ordinal scales. Values are ranging from 1 to 6
data(anxiety)
str(anxiety)
head(anxiety, 4)

# Cohen’s Kappa: it can be used for two nominal or two ordinal categorical variables
kappa2(diagnoses[, c("rater1", "rater2")], weight = "unweighted")
kappa2(diagnoses[, c("rater3", "rater4")], weight = "unweighted")

# Light’s kappa: it returns the average Cohen’s kappa when you have multiple raters 
# for example, average Cohen’s kappa between the first 3 raters
kappam.light(diagnoses[, 1:3])
# just to be sure
kappa2(diagnoses[, c("rater1", "rater2")], weight = "unweighted")
kappa2(diagnoses[, c("rater1", "rater3")], weight = "unweighted")
kappa2(diagnoses[, c("rater2", "rater3")], weight = "unweighted")
(0.651+0.384+0.631)/3

# Fleiss’ kappa: in this case the (multiple) raters are not assumed to be the same for all subjects/texts
kappam.fleiss(diagnoses[, 1:3])

# Weighed kappa: it can be considered only when ratings are performed in ordinal scale (& 2 coders)
# if more than 2 coders, you replicate the analysis and then you take the average as above
kappa2(anxiety[, c("rater1", "rater2")], weight = "equal")

# Intraclass correlation coefficients: continuous scales (by treating the previous ordinal scale as it were a continuous variable)
# and any number of raters
icc(
  anxiety, model = "twoway", 
  type = "agreement", unit = "single"
  )