Kappa_Lucas

Videos

#a https://www.youtube.com/watch?v=5R9_QydbEHc

#b https://www.youtube.com/watch?v=909pbQtpcb8

#c https://www.youtube.com/watch?v=Jbb0oJrNz3g

#d https://www.youtube.com/watch?v=L-v7u6U8YwM

#e https://www.youtube.com/watch?v=Ly27c1v-WGk

#f https://www.youtube.com/watch?v=MeCjvOmNvD0

#g https://www.youtube.com/watch?v=nimdaDovcp8

#h https://www.youtube.com/watch?v=rklDhiLhUX0

#i https://www.youtube.com/watch?v=rykXrptnhgk

#j https://www.youtube.com/watch?v=vkgLZJgEYIE

#l https://www.youtube.com/watch?v=Y3cQQV79jjI

if(!require(pacman)) install.packages("pacman")

## Loading required package: pacman

library(pacman)

pacman::p_load(dplyr, rel, irr)

Carregar o banco de dados

dados <- read.csv("kappa.csv", stringsAsFactors = TRUE)

#View(dados)                                 # Visualização dos dados em janela separada
#glimpse(dados)                              # Visualização de um resumo dos dados

Cálculo do kappa

irr::kappa2(dados[2:3]) #a

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 35 
##    Raters = 2 
##     Kappa = 0.726 
## 
##         z = 4.72 
##   p-value = 2.33e-06

irr::kappa2(dados[4:5]) #b

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.265 
## 
##         z = 1.77 
##   p-value = 0.0773

irr::kappa2(dados[6:7])  #c

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.647 
## 
##         z = 5.33 
##   p-value = 9.98e-08

irr::kappa2(dados[8:9]) #d

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.18 
## 
##         z = 1.21 
##   p-value = 0.228

irr::kappa2(dados[10:11]) #e

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.682 
## 
##         z = 4.45 
##   p-value = 8.61e-06

irr::kappa2(dados[12:13]) #f

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.405 
## 
##         z = 2.53 
##   p-value = 0.0114

irr::kappa2(dados[14:15]) #g

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 34 
##    Raters = 2 
##     Kappa = 0.456 
## 
##         z = 2.96 
##   p-value = 0.00304

irr::kappa2(dados[16:17]) #h

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.339 
## 
##         z = 2.5 
##   p-value = 0.0124

irr::kappa2(dados[18:19]) #i

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.392 
## 
##         z = 3.01 
##   p-value = 0.00264

irr::kappa2(dados[20:21]) #j

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 35 
##    Raters = 2 
##     Kappa = 0.399 
## 
##         z = 2.66 
##   p-value = 0.00775

irr::kappa2(dados[22:23]) #l

##  Cohen's Kappa for 2 Raters (Weights: unweighted)
## 
##  Subjects = 36 
##    Raters = 2 
##     Kappa = 0.681 
## 
##         z = 4.53 
##   p-value = 5.8e-06

Cálculo do IC 95%:

# https://www.youtube.com/watch?v=5R9_QydbEHc
rel::ckap(dados[2:3], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[2:3], conf.level = 0.95)
## 
##       Estimate  StdErr LowerCB UpperCB
## Const  0.75862 0.21201 0.32777  1.1895
## 
## Maximum kappa = 0.9
## Kappa/maximum kappa = 0.84
## Confidence level = 95%
## Observations = 2
## Sample size = 35

# https://www.youtube.com/watch?v=909pbQtpcb8
rel::ckap(dados[4:5], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[4:5], conf.level = 0.95)
## 
##       Estimate   StdErr  LowerCB UpperCB
## Const  0.27419  0.35475 -0.44598  0.9944
## 
## Maximum kappa = 0.91
## Kappa/maximum kappa = 0.3
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=Jbb0oJrNz3g
rel::ckap(dados[6:7], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[6:7], conf.level = 0.95)
## 
##       Estimate  StdErr LowerCB UpperCB
## Const  0.90909 0.25668 0.38800  1.4302
## 
## Maximum kappa = 0.94
## Kappa/maximum kappa = 0.97
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=L-v7u6U8YwM
rel::ckap(dados[8:9], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[8:9], conf.level = 0.95)
## 
##       Estimate   StdErr  LowerCB UpperCB
## Const  0.65505  0.41203 -0.18142  1.4915
## 
## Maximum kappa = 0.97
## Kappa/maximum kappa = 0.68
## Confidence level = 95%
## Observations = 2
## Sample size = 36

#https://www.youtube.com/watch?v=Ly27c1v-WGk
rel::ckap(dados[10:11], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[10:11], conf.level = 0.95)
## 
##       Estimate  StdErr LowerCB UpperCB
## Const  0.85246 0.23968 0.36589   1.339
## 
## Maximum kappa = 0.94
## Kappa/maximum kappa = 0.91
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=MeCjvOmNvD0
rel::ckap(dados[12:13], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[12:13], conf.level = 0.95)
## 
##       Estimate   StdErr  LowerCB UpperCB
## Const  0.30194  0.35453 -0.41779  1.0217
## 
## Maximum kappa = 0.97
## Kappa/maximum kappa = 0.31
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=nimdaDovcp8
rel::ckap(dados[14:15], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[14:15], conf.level = 0.95)
## 
##       Estimate   StdErr  LowerCB UpperCB
## Const  0.50903  0.33681 -0.17621  1.1943
## 
## Maximum kappa = 0.93
## Kappa/maximum kappa = 0.55
## Confidence level = 95%
## Observations = 2
## Sample size = 34

# https://www.youtube.com/watch?v=rklDhiLhUX0
rel::ckap(dados[16:17], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[16:17], conf.level = 0.95)
## 
##        Estimate    StdErr   LowerCB UpperCB
## Const  0.687410  0.357169 -0.037681  1.4125
## 
## Maximum kappa = 0.75
## Kappa/maximum kappa = 0.92
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=rykXrptnhgk
rel::ckap(dados[18:19], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[18:19], conf.level = 0.95)
## 
##        Estimate    StdErr   LowerCB UpperCB
## Const  0.638686  0.321225 -0.013435  1.2908
## 
## Maximum kappa = 0.71
## Kappa/maximum kappa = 0.89
## Confidence level = 95%
## Observations = 2
## Sample size = 36

# https://www.youtube.com/watch?v=vkgLZJgEYIE
rel::ckap(dados[20:21], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[20:21], conf.level = 0.95)
## 
##       Estimate   StdErr  LowerCB UpperCB
## Const  0.60938  0.36465 -0.13168  1.3504
## 
## Maximum kappa = 0.87
## Kappa/maximum kappa = 0.7
## Confidence level = 95%
## Observations = 2
## Sample size = 35

# https://www.youtube.com/watch?v=Y3cQQV79jjI
rel::ckap(dados[22:23], conf.level = 0.95)

## Call:
## rel::ckap(data = dados[22:23], conf.level = 0.95)
## 
##       Estimate  StdErr LowerCB UpperCB
## Const  0.85771 0.23115 0.38845   1.327
## 
## Maximum kappa = 0.87
## Kappa/maximum kappa = 0.98
## Confidence level = 95%
## Observations = 2
## Sample size = 36

Cálculo da concordância

irr::agree(dados[2:3]) #a

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 35 
##    Raters = 2 
##   %-agree = 85.7

irr::agree(dados[4:5]) #b

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 66.7

irr::agree(dados[6:7]) #c

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 94.4

irr::agree(dados[8:9]) #d

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 69.4

irr::agree(dados[10:11]) #e

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 83.3

irr::agree(dados[12:13]) #f

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 69.4

irr::agree(dados[14:15]) #g

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 34 
##    Raters = 2 
##   %-agree = 76.5

irr::agree(dados[16:17]) #h

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 66.7

irr::agree(dados[18:19]) #i

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 69.4

irr::agree(dados[20:21]) #j

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 35 
##    Raters = 2 
##   %-agree = 71.4

irr::agree(dados[22:23]) #l

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 36 
##    Raters = 2 
##   %-agree = 83.3