krippalpha computes Krippendorff's reliability coefficient alpha.
krippalpha(data, metric = "nominal", bootstrap = FALSE, bootnp = FALSE,
nboot = 20000, nnp = 1000, cores = 1, seed = rep(12345, 6))a matrix or data frame (coercible to a matrix) of reliability data. Data of type character are converted to numeric via as.factor().
metric difference function to be applied to disagreements. Supports nominal, ordinal, interval, and ratio. Defaults to nominal.
logical indicating whether uncertainty estimates should be obtained using the bootstrap algorithm defined by Krippendorff. Defaults to FALSE.
logical indicating whether non-parametric bootstrap uncertainty estimates should be computed. Defaults to FALSE.
number of bootstraps used in Krippendorff's algorithm. Defaults to 20000.
number of non-parametric bootstraps. Defaults to 1000.
number of cores across which bootstrap-computations are distributed. Defaults to 1. If more cores are specified than available, the number will be set to the maximum number of available cores.
numeric vector of length 6 for the internal L'Ecuyer-CMRG random number generator (see details). Defaults to c(12345, 12345, 12345, 12345, 12345, 12345).
Returns a list of type icr with following elements:
value of inter-coder reliability coefficient
integer representation of metric used to compute alpha: 1 nominal, 2 ordinal, 3 interval, 4 ratio
number of coders
number of units to be coded
number of unique values in reliability data
matrix containing coincidences within coder-value pairs
matrix of metric differences depending on method
expected disagreement
observed disagreement
TRUE if Krippendorff bootstrapping algorithm was run, FALSE otherwise
number of bootstraps
TRUE if nonparametric bootstrap was run, FALSE otherwise
number of non-parametric bootstraps
vector of bootstrapped values of alpha (Krippendorff's algorithm)
vector of non-parametrically bootstrapped values of alpha
krippalpha takes the seed vector to seed the internal random number generator of both bootstrap-routines. It does not advance R's RNG state.
Krippendorff, K. (2004) Content Analysis: An Introduction to Its Methodology. Beverly Hills: Sage.
Krippendorff, K. (2011) Computing Krippendorff's Alpha Reliability. Departmental Papers (ASC) 43. http://repository.upenn.edu/asc_papers/43.
Krippendorff, K. (2016) Bootstrapping Distributions for Krippendorff's Alpha. http://web.asc.upenn.edu/usr/krippendorff/boot.c-Alpha.pdf.
L'Ecuyer, P. (1999) Good Parameter Sets for Combined Multiple Recursive Random Number Generators. Operations Research, 47 (1), 159--164. https://pubsonline.informs.org/doi/10.1287/opre.47.1.159.
L'Ecuyer, P., Simard, R, Chen, E. J., and Kelton, W. D. (2002) An Objected-Oriented Random-Number Package with Many Long Streams and Substreams. Operations Research, 50 (6), 1073--1075. http://www.iro.umontreal.ca/~lecuyer/myftp/streams00/c++/streams4.pdf.
# NOT RUN {
data(codings)
krippalpha(codings)
krippalpha(codings, metric = "nominal", bootstrap = TRUE, bootnp = TRUE)
# }
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