plot.krippendorffsalpha: Plot the results of a Krippendorff's Alpha analysis.

Description

Plot the results of a Krippendorff's Alpha analysis.

Usage

# S3 method for krippendorffsalpha
plot(
  x,
  y = NULL,
  level = 0.95,
  type = 7,
  density = TRUE,
  lty.density = 1,
  lty.estimate = 1,
  lty.interval = 2,
  col.density = "black",
  col.estimate = "orange",
  col.interval = "blue",
  lwd.density = 3,
  lwd.estimate = 3,
  lwd.interval = 3,
  ...
)

Arguments

x: an object of class "krippendorffsalpha", the result of a call to krippendorffs.alpha.
y: always ignored.
level: the desired confidence level for the interval. The default is 0.95.
type: the method used to compute sample quantiles. This argument is passed to quantile. The default is 7.
density: logical; if TRUE, a kernel density estimate is plotted.
lty.density: the line type for the kernel density estimate. The default is 1.
lty.estimate: the line type for the estimate of alpha. The default is 1.
lty.interval: the line type for the confidence limits. The default is 2.
col.density: the color for the kernel density estimate. The default is black.
col.estimate: the color for the estimate of alpha. The default is orange.
col.interval: the color for the confidence limits. The default is blue.
lwd.density: the line width for the kernel density estimate. The default is 3.
lwd.estimate: the line width for the estimate of alpha. The default is 3.
lwd.interval: the line width for the confidence limits. The default is 3.
...: additional arguments. These are passed to hist.

Details

This function plots the results of a Krippendorff's Alpha analysis, assuming that krippendorffs.alpha was called with method = "customary" and confint = TRUE. Otherwise there is no bootstrap sample to work with. The plot is highly customizable.

This function plots a histogram of the bootstrap sample, (optionally) a kernel density estimate, and vertical lines marking the lower and upper confidence limits.

References

Krippendorff, K. (2013). Computing Krippendorff's alpha-reliability. Technical report, University of Pennsylvania.

Examples

Run this code

# The following data were presented in Krippendorff (2013).

nominal = matrix(c(1,2,3,3,2,1,4,1,2,NA,NA,NA,
                   1,2,3,3,2,2,4,1,2,5,NA,3,
                   NA,3,3,3,2,3,4,2,2,5,1,NA,
                   1,2,3,3,2,4,4,1,2,5,1,NA), 12, 4)
fit.nom = krippendorffs.alpha(nominal, level = "nominal", method = "customary", confint = TRUE,
                              verbose = TRUE, control = list(bootit = 1000, parallel = FALSE))
dev.new()
plot(fit.nom, main = "Results for Nominal Data", xlab = "Bootstrap Estimates", density = FALSE)