krippalpha computes Krippendorff's reliability coefficient alpha.
krippalpha(data, metric = "nominal", bootstrap = FALSE, bootnp = FALSE,
nboot = 20000, nnp = 1000, cores = 1, custom_seed = NULL)a matrix of reliability data.
metric difference function to be applied to disagreements. Supports nominal, ordinal, interval, and ratio. Defaults to nominal.
a logical indicating whether uncertainty estimates should be obtained using the bootstrap algorithm defined by Krippendorff. Defaults to FALSE.
a 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.
seed vector of length 6 for the internal L'Ecuyer-CMRG random number generator. Defaults to NULL. When set to NULL, relies on R's .Random.seed vector.
Returns a list of type icr with following elements:
value of inter-coder reliability coefficient
data level of x
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
logical value. TRUE if Krippendorff bootstrapping algorithm was run
number of bootstraps
logical value. TRUE if nonparametric bootstrap was run
number of non-parametric bootstraps
vector of bootstrapped values of alpha (Krippendorff's algorithm)
vector of non-parametrically bootstrapped values of alpha
For proper seeding of krippalpha's bootstrap-routines via R, specify set.seed(seed, kind = "L'Ecuyer-CMRG"). Please note that krippalpha takes the .Random.seed vector generated by R to seed the internal random number generator of both bootstrap-routines. Furthermore, it does not advance R's RNG state. Hence, .Random.seed will be the same after krippalpha has been run.
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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