Function for Interrater Reliability.
Usage
agreement(
data,
vars,
sft = FALSE,
heatmap = FALSE,
heatmapDetails = FALSE,
wght = "unweighted",
exct = FALSE,
kripp = FALSE,
krippMethod = "nominal"
)Arguments
- data
The data as a data frame. Each row represents a case/subject, and columns represent different raters/observers.
- vars
Variables representing different raters/observers. Each variable should contain the ratings/diagnoses given by each observer for the same set of cases.
- sft
Show frequency tables for each rater and cross-tabulation tables for pairwise comparisons.
- heatmap
Show agreement heatmap visualization with color-coded agreement levels.
- heatmapDetails
Show detailed heatmap with kappa values and confidence intervals for all rater pairs.
- wght
Weighting scheme for kappa analysis. Use 'squared' or 'equal' only with ordinal variables. Weighted kappa accounts for the degree of disagreement.
- exct
Use exact method for Fleiss' kappa calculation with 3 or more raters. More accurate but computationally intensive.
- kripp
Calculate Krippendorff's alpha, a generalized measure of reliability for any number of observers and data types.
- krippMethod
Measurement level for Krippendorff's alpha calculation. Choose based on your data type.
Value
A results object containing:
results$todo | a html | ||||
results$overviewTable | a table | ||||
results$kappaTable | a table | ||||
results$krippTable | a table | ||||
results$heatmapPlot | an image | ||||
results$frequencyTables | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overviewTable$asDF
as.data.frame(results$overviewTable)