Sample size or precision for Cohen's kappa

prec_kappa returns the sample size or the precision for the provided Cohen's kappa coefficient.

Usage

prec_kappa(
  kappa,
  n = NULL,
  raters = 2,
  n_category = 2,
  props,
  conf.width = NULL,
  conf.level = 0.95
)

Arguments

kappa

expected value of Cohen's kappa.

n

sample size.

raters

number of raters (maximum of 6).

n_category

number of categories of outcomes (maximum of 5).

props

expected proportions of each outcome (should have length n_category).

conf.width

precision (the full width of the confidence interval).

conf.level

confidence level.

Value

Object of class "presize", a list of arguments (including the computed one) augmented with method and note elements.

Details

This function wraps the FixedN and CI functions in the kappaSize package. The FixedN functions in kappaSize return a one sided confidence interval. The values that are passed to kappaSize ensure that two-sided confidence intervals are returned, although we assume that confidence intervals are symmetrical.

See also

FixedNBinary, FixedN3Cats, CIBinary, CI3Cats

Examples

# precision based on sample size
#   two categories with proportions of 30 and 70\%, four raters
prec_kappa(kappa = .5, n = 200, raters = 4, n_category = 2, props = c(.3,.7))
#> 
#>      precision for Cohen's kappa 
#> 
#>   kappa   n   lwr   upr conf.width conf.level
#> 1   0.5 200 0.425 0.575       0.15       0.95
# sample size to get a given precision
prec_kappa(kappa = .5, conf.width = .15, raters = 4, n_category = 2,
           props = c(.3,.7))
#> 
#>      sample size for Cohen's kappa 
#> 
#>   kappa   n   lwr   upr conf.width conf.level
#> 1   0.5 198 0.425 0.575       0.15       0.95

# as above, but with two scenarios for kappa
prec_kappa(kappa = c(.5, .75), conf.width = .15, raters = 4, n_category = 2,
           props = c(.3,.7))
#> 
#>      sample size for Cohen's kappa 
#> 
#>   kappa   n   lwr   upr conf.width conf.level
#> 1  0.50 198 0.425 0.575       0.15       0.95
#> 2  0.75 155 0.675 0.825       0.15       0.95
prec_kappa(kappa = c(.5, .75), conf.width = c(.15, 0.3), raters = 4,
           n_category = 2, props = c(.3,.7))
#> 
#>      sample size for Cohen's kappa 
#> 
#>   kappa   n   lwr   upr conf.width conf.level
#> 1  0.50 198 0.425 0.575       0.15       0.95
#> 2  0.75  44 0.600 0.900       0.30       0.95