Package 'intrinsicKappa'

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: 2024-11-11 07:11:29 UTC
Source: CRAN

Help Index


Sample Size Planning Based on Intrinsic Kappa Value

Description

Providing functions for the computation of the intrinsic kappa value.

Author(s)

David Schindler [email protected], Rafael Sanchez-Marquez, Frank Gerhorst

References

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.

See Also

For the computation of the intrinsic kappa value, see intrinsicKappa.


Compute Statistics

Description

Compute Statistics

Usage

computeStat(n1, n2, alpha)

Arguments

n1

integer

n2

integer

alpha

one-sided significance level


Intrinsic Kappa

Description

Intrinsic Kappa

Usage

intrinsicKappa(M, alpha = 0.05, alpha_adjusted = TRUE)

Arguments

M

matrix to be assessed

alpha

one-sided significance level

alpha_adjusted

logical, whether the significance level shall be adjusted

Details

Computation of intrinsic kappa with a dichotomous response and known relation of the input frequencies.

Value

Intrinsic kappa value

References

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.

Examples

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)