epiR (version 0.9-40)

epi.kappa: Kappa statistic

Description

Computes the kappa statistic and its confidence interval.

Usage

epi.kappa(dat, method = "fleiss", alternative = c("two.sided", "less", "greater"), conf.level = 0.95)

Arguments

dat

an object of class table with the individual cell frequencies.

method

a character string indicating the method to use. Options are fleiss or altman.

alternative

a character string specifying the alternative hypothesis, must be one of two.sided, greater or less.

conf.level

magnitude of the returned confidence interval. Must be a single number between 0 and 1.

Value

• A list containing the following:
• kappa
  a data frame with the kappa statistic, the standard error of the kappa statistic and the lower and upper bounds of the confidence interval for the kappa statistic.
• z
  a data frame containing the z test statistic and its associated P-value.

Details

Kappa is a measure of agreement beyond the level of agreement expected by chance alone. The observed agreement is the proportion of samples for which both methods (or observers) agree. Common interpretations for the kappa statistic are as follows: < 0.2 slight agreement, 0.2 - 0.4 fair agreement, 0.4 - 0.6 moderate agreement, 0.6 - 0.8 substantial agreement, > 0.8 almost perfect agreement. alternative = "greater" tests the hypothesis that kappa is greater than 0.

References

Altman DG, Machin D, Bryant TN, Gardner MJ (2000). Statistics with Confidence, second edition. British Medical Journal, London, pp. 116 - 118. Dohoo I, Martin W, Stryhn H (2010). Veterinary Epidemiologic Research, second edition. AVC Inc, Charlottetown, Prince Edward Island, Canada, pp. 98 - 99. Fleiss JL, Levin B, Paik MC (2003). Statistical Methods for Rates and Proportions, third edition. John Wiley & Sons, London, 598 - 626.

Examples

## Kidney samples from 291 salmon were split with one half of the 
## samples sent to each of two laboratories where an IFAT test 
## was run on each sample. The following results were obtained:

## Lab 1 positive, lab 2 positive: 19
## Lab 1 negative, lab 2 positive: 6
## Lab 1 positive, lab 2 negative: 10
## Lab 1 negative, lab 2 negative: 256

dat <- as.table(matrix(c(19,6,10,256), nrow = 2, byrow = TRUE))
colnames(dat) <- c("L1-pos","L1-neg")
rownames(dat) <- c("L2-pos","L2-neg")

epi.kappa(dat, method = "fleiss", alternative = "greater", conf.level = 0.95)

## FIX
## The z test statistic is 11.53 (P < 0.01). We accept the alternative
## hypothesis that the kappa statistic is greater than zero.

## The proportion of agreements after chance has been excluded is 
## 0.67 (95\% CI 0.56 to 0.79). We conclude that, on the basis of 
## this sample, that there is substantial agreement between the two
## laboratories.