krippendorffsalpha (version 2.0)

confint.krippendorffsalpha: Compute a confidence interval for Krippendorff's Alpha.

Description

Compute a confidence interval for Krippendorff's Alpha.

Usage

# S3 method for krippendorffsalpha
confint(object, parm = "alpha", level = 0.95, ...)

Value

A vector with entries giving lower and upper confidence limits. These will be labelled as (1 - level) / 2 and 1 - (1 - level) / 2.

Arguments

object

an object of class "krippendorffsalpha", the result of a call to krippendorffs.alpha.

parm

always ignored since there is only one parameter.

level

the desired confidence level for the interval. The default is 0.95.

...

additional arguments. These are passed to quantile.

Details

This function computes a confidence interval for alpha, assuming that krippendorffs.alpha was called with confint = TRUE.

For method = "analytical", a jackknife-based interval is computed. For smaller samples the jackknife interval offers a very substantial improvement over the bootstrap interval, the latter of which offers quite poor coverage. For larger samples method = "customary" can safely be used, in which case a bootstrap interval is provided. For sufficiently large datasets the two intervals will be nearly equal, but the bootstrap approach is preferred owing to its much faster execution speed.

References

Nissi, M. J., Mortazavi, S., Hughes, J., Morgan, P., and Ellermann, J. (2015). T2* relaxation time of acetabular and femoral cartilage with and without intra-articular Gd-DTPA2 in patients with femoroacetabular impingement. American Journal of Roentgenology, 204(6), W695.

See Also

krippendorffs.alpha

Examples

# Fit a subset of the cartilage data, using the customary methodology.
# Compute bootstrap confidence intervals using a bootstrap sample size
# of 1,000. Report the estimate of alpha, and produce a 99% interval.

data(cartilage)
cartilage = as.matrix(cartilage[1:100, ])
fit.cart = krippendorffs.alpha(cartilage, level = "ratio", method = "customary", confint = TRUE,
                               control = list(bootit = 1000, parallel = FALSE))
fit.cart$alpha.hat
confint(fit.cart, level = 0.99)