kappa.rmd

---
title: "Calculate Kappa"
output: html_notebook
---



Kappa calculations
```{r}
#Use the pairwise confusion matrices
library(caret)

#Create dataframe that will be changed later
#https://stats.stackexchange.com/questions/124001/what-is-the-intuition-behind-the-kappa-statistical-value-in-classification#124019
results.pm <- data.frame(pel = rep(LETTERS[1:2], each=10), mhi = rep(sample(LETTERS[1:2], 20, replace=T)))
pm.results <- table(results.pm)

#Change values of table to actual results from saved data on ONEDRIVE
pm.results[1,1] <- 8648
pm.results[1,2] <- 6352
pm.results[2,1] <- 5059
pm.results[2,2] <- 9941

#for PvsN
pm.results[1,1] <- 8509
pm.results[1,2] <- 6491
pm.results[2,1] <- 6619
pm.results[2,2] <- 8381

pm.cmat <- confusionMatrix(pm.results)


#The following is adapted from Titus et al 1984
#Proportion accurate
Pa = pm.cmat$overall[['Accuracy']] #double [[]] to get just the number instead of named number


#Proportion expected
Pe = pm.cmat$byClass[['Detection Prevalence']]

#Total number of events (whistles classified)
N = sum(pm.results)

#standard error of Kappa
stdERR <- sqrt((Pa*(1-Pa))/(N*(1-Pe)^2))

#Confidence intercals for Kappa
CI_low <- pm.cmat$overall[['Kappa']]-stdERR
CI_upper <- pm.cmat$overall[['Kappa']]+stdERR

#Approximate standard error of Kappa / Standard deviation
SEk <- sqrt(Pe/(N*(1-Pe))) 

pm.cmat$overall[['Kappa']]/SEk


z.test()


```