| Title: | Sample Size Planning Based on Intrinsic Kappa Value |
|---|---|
| Description: | Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>. |
| Authors: | David Schindler [aut, cre], Rafael Sanchez-Marquez [aut], Frank Gerhorst [aut] |
| Maintainer: | David Schindler <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1 |
| Built: | 2024-12-11 07:07:32 UTC |
| Source: | CRAN |
Providing functions for the computation of the intrinsic kappa value.
David Schindler [email protected], Rafael Sanchez-Marquez, Frank Gerhorst
R. Sanchez-Marquez, F. Gerhorst and D. Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." Computers & Industrial Engineering, 109006.
For the computation of the intrinsic kappa value, see intrinsicKappa.
Compute Statistics
computeStat(n1, n2, alpha)computeStat(n1, n2, alpha)
n1 |
integer |
n2 |
integer |
alpha |
one-sided significance level |
Intrinsic Kappa
intrinsicKappa(M, alpha = 0.05, alpha_adjusted = TRUE)intrinsicKappa(M, alpha = 0.05, alpha_adjusted = TRUE)
M |
matrix to be assessed |
alpha |
one-sided significance level |
alpha_adjusted |
logical, whether the significance level shall be adjusted |
Computation of intrinsic kappa with a dichotomous response and known relation of the input frequencies.
Intrinsic kappa value
R. Sanchez-Marquez, F. Gerhorst and D. Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." Computers & Industrial Engineering, 109006.
M <- matrix(c(2375, 25, 10, 2390), ncol = 2) rownames(M) <- c('ok-rating', 'nok-rating') colnames(M) <- c('ok-standard', 'nok-standard') alpha <- 0.05 alpha_adjusted <- FALSE intrinsicKappa(M, alpha, alpha_adjusted)M <- matrix(c(2375, 25, 10, 2390), ncol = 2) rownames(M) <- c('ok-rating', 'nok-rating') colnames(M) <- c('ok-standard', 'nok-standard') alpha <- 0.05 alpha_adjusted <- FALSE intrinsicKappa(M, alpha, alpha_adjusted)