bangdiwala.table: Bangdiwala B coefficient for 2 raters
In irrCAC: Computing the Extent of Agreement among Raters with Chance-Corrected Agreement Coefficient (CAC)
bangdiwala.table R Documentation
Bangdiwala B coefficient for 2 raters
Description
Bangdiwala B coefficient for 2 raters
Usage
bangdiwala.table(ratings, conflev = 0.95, N = Inf)
Arguments
ratings
A square table of ratings (assume no missing ratings).
conflev
An optional parameter that specifies the confidence level used for constructing confidence intervals. By default the function assumes the standard value of 95%.
N
An optional parameter representing the finite population size if any. It is used to perform the finite population correction to the standard error. It's default value is infinity.
Value
A data frame containing the following 5 variables: coeff.name coeff.val coeff.se coeff.ci coeff.pval.
Examples
#The dataset "cont3x3abstractors" comes with this package. Analyze it as follows:
bangdiwala.table(cont3x3abstractors) #Yields Bangdiwala coefficient along with precision measures
Bcoeff <- bangdiwala.table(cont3x3abstractors)$coeff.val #Yields Bangdiwala's coefficient alone.
Bcoeff
q <- nrow(cont3x3abstractors) #Number of categories
irrCAC documentation built on April 27, 2026, 9:08 a.m.
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| bangdiwala.table | R Documentation |
Bangdiwala B coefficient for 2 raters
Description
Bangdiwala B coefficient for 2 raters
Usage
bangdiwala.table(ratings, conflev = 0.95, N = Inf)
Arguments
ratings |
A square table of ratings (assume no missing ratings). |
conflev |
An optional parameter that specifies the confidence level used for constructing confidence intervals. By default the function assumes the standard value of 95%. |
N |
An optional parameter representing the finite population size if any. It is used to perform the finite population correction to the standard error. It's default value is infinity. |
Value
A data frame containing the following 5 variables: coeff.name coeff.val coeff.se coeff.ci coeff.pval.
Examples
#The dataset "cont3x3abstractors" comes with this package. Analyze it as follows:
bangdiwala.table(cont3x3abstractors) #Yields Bangdiwala coefficient along with precision measures
Bcoeff <- bangdiwala.table(cont3x3abstractors)$coeff.val #Yields Bangdiwala's coefficient alone.
Bcoeff
q <- nrow(cont3x3abstractors) #Number of categories
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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