krippalpha

0th

Percentile

Krippendorff's alpha

krippalpha computes Krippendorff's reliability coefficient alpha.

Usage
krippalpha(data, metric = "nominal", bootstrap = FALSE,
  bootnp = FALSE, nboot = 20000, nnp = 1000, cores = 1,
  seed = rep(12345, 6))
Arguments
data

a matrix or data frame (coercible to a matrix) of reliability data. Data of type character are converted to numeric via as.factor().

metric

metric difference function to be applied to disagreements. Supports nominal, ordinal, interval, ratio, bipolar. Defaults to nominal.

bootstrap

logical indicating whether uncertainty estimates should be obtained using the bootstrap algorithm defined by Krippendorff. Defaults to FALSE.

bootnp

logical indicating whether non-parametric bootstrap uncertainty estimates should be computed. Defaults to FALSE.

nboot

number of bootstraps used in Krippendorff's algorithm. Defaults to 20000.

nnp

number of non-parametric bootstraps. Defaults to 1000.

cores

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

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).

Details

krippalpha takes the seed vector to seed the internal random number generator of both bootstrap-routines. It does not advance R's RNG state.

When using the ratio metric with reliability data containing scales involving negative as well as positive values, krippalpha may return a value of NaN. The ratio metric difference function is defined as \(\Big(\frac{(c - k)}{(c + k)}\Big)^2\). Hence, if for any two scale values \(c = -k\), the fraction is not defined, resulting in \(\alpha =\) NaN. In order to avoid this issue, shift your reliability data to have strictly positive values.

Value

Returns a list of type icr with following elements:

alpha

value of inter-coder reliability coefficient

metric

integer representation of metric used to compute alpha: 1 nominal, 2 ordinal, 3 interval, 4 ratio, 6 bipolar

n_coders

number of coders

n_units

number of units to be coded

n_values

number of unique values in reliability data

coincidence_matrix

matrix containing coincidences within coder-value pairs

delta_matrix

matrix of metric differences depending on method

D_e

expected disagreement

D_o

observed disagreement

bootstrap

TRUE if Krippendorff bootstrapping algorithm was run, FALSE otherwise

nboot

number of bootstraps

bootnp

TRUE if nonparametric bootstrap was run, FALSE otherwise

nnp

number of non-parametric bootstraps

bootstraps

vector of bootstrapped values of alpha (Krippendorff's algorithm)

bootstrapsNP

vector of non-parametrically bootstrapped values of alpha

Note

krippalpha's bootstrap-routines use L'Ecuyer's CMRG random number generator (see L'Ecyuer et al. 2002) to create random numbers suitable for parallel computations. The routines interface to L'Ecuyer's C++ code, which can be found at https://pubsonline.informs.org/doi/abs/10.1287/opre.50.6.1073.358

References

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.

Aliases
  • krippalpha
Examples
# NOT RUN {
data(codings)

# compute alpha, without uncertainty estimates
krippalpha(codings)

# additionally compute bootstrapped uncertainty estimates for alpha
alpha <- krippalpha(codings, metric = "nominal", bootstrap = TRUE, bootnp = TRUE)
alpha

# plot bootstrapped alphas
plot(alpha)

# alternatively, use ggplot2
df <- plot(alpha, return_data = TRUE)

library(ggplot2)
ggplot() +
  geom_line(data = df[df$ci_limit == FALSE, ], aes(x, y, color = type)) +
  geom_area(data = df[df$ci == TRUE, ], aes(x, y, fill = type), alpha = 0.4) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position = "bottom", legend.title = element_blank()) +
  ggtitle(expression(paste("Bootstrapped ", alpha))) +
  xlab("value") + ylab("density") +
  guides(fill = FALSE)

# }
Documentation reproduced from package icr, version 0.6.1, License: GPL (>= 2)

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