#packages laden
library('reshape2')Warning: Paket 'reshape2' wurde unter R Version 4.3.3 erstelltlibrary('vcd')Warning: Paket 'vcd' wurde unter R Version 4.3.3 erstelltLade nötiges Paket: gridlibrary(ggplot2)Warning: Paket 'ggplot2' wurde unter R Version 4.3.3 erstellt##Alter Code Steff
##old chunk steffi
# Inter Rater Reliability - item wise
library("irr")Lade nötiges Paket: lpSolvedat_item <- read.table("ItemwiseCorrelation.csv", sep = ";", header = T)
Somatic_all <- na.omit(cbind(melt(dat_item[c(1:6)], id.vars = c("ID", "Tag_HSZT")),
melt(dat_item[c(1:2, 15:18)], id.vars = c("ID", "Tag_HSZT"))))
names(Somatic_all) <- c("ID", "Tag_HSZT", "Frage", "Staff","ID2", "Tag_HSZT2", "Frage2", "Parents")
Somatic_all <- Somatic_all[c("ID", "Tag_HSZT", "Frage", "Staff", "Parents")]
Somatic_all$Frage <- sub("P_", "", Somatic_all$Frage)
Mental_all <- na.omit(cbind(melt(dat_item[c(1:2, 7:14)], id.vars = c("ID", "Tag_HSZT")),
melt(dat_item[c(1:2, 19:26)], id.vars = c("ID", "Tag_HSZT"))))
names(Mental_all) <- c("ID", "Tag_HSZT", "Frage", "Staff","ID2", "Tag_HSZT2", "Frage2", "Parents")
Mental_all <- Mental_all[c("ID", "Tag_HSZT", "Frage", "Staff", "Parents")]
Mental_all$Frage <- sub("P_", "", Mental_all$Frage)
# calculate weighted kappa for each child for mental an somatic subscale
# see Cohen (1968)
wkappa_somatic <- NULL
wkappa_mental <- NULL
for(id in unique(Mental_all$ID)){
wkappa_mental <- c(wkappa_mental, as.numeric(unlist(kappa2(Mental_all[Mental_all$ID == id,4:5],
weight = "squared"))["value"]))
wkappa_somatic <- c(wkappa_somatic, as.numeric(unlist(kappa2(Somatic_all[Mental_all$ID == id,4:5],
weight = "squared"))["value"]))
}##Für Mental
wkappa_mental <- NULL
for(id in unique(Mental_all$ID)){
wkappa_mental <- c(wkappa_mental, as.numeric(unlist(kappa2(Mental_all[Mental_all$ID == id,4:5],
weight = "squared"))["value"]))
}
# Teststatistik aus Kappa2 ausgeben Z-Wert
wz_value_mental <- NULL
for(id in unique(Mental_all$ID)){
wz_value_mental <- c(wz_value_mental, as.numeric(unlist(kappa2(Mental_all[Mental_all$ID == id,4:5],
weight = "squared"))["statistic"]))
}
# P-Wert aus Kappa2 ausgeben
wp_value_mental <- NULL
for(id in unique(Mental_all$ID)){
wp_value_mental <- c(wp_value_mental, as.numeric(unlist(kappa2(Mental_all[Mental_all$ID == id,4:5],
weight = "squared"))["p.value"]))
}
#Data Frame mit Test-Statistiken erstellen
kappa_results_mental <- data.frame(
ID = unique(Mental_all$ID),
Kappa = wkappa_mental,
P_Value = wp_value_mental,
Z_Value = wz_value_mental
)#Signifkanz Yes No hinzufügen - Tabelle ausgeben Mean berechnen
kappa_results_mental$Signifikant <- ifelse(kappa_results_mental$P_Value < 0.05, "Yes", "No")
print(kappa_results_mental) ID Kappa P_Value Z_Value Signifikant
1 1 0.083277367 3.858149e-01 0.8672321 No
2 2 0.349945829 4.422063e-03 2.8463711 Yes
3 3 0.408378044 9.012064e-05 3.9157579 Yes
4 4 0.303977273 1.141584e-02 2.5297053 Yes
5 5 0.018154312 8.883564e-01 0.1403843 No
6 6 -0.123165284 3.790909e-01 -0.8795726 No
7 7 0.030303030 7.873264e-01 0.2697840 No
8 8 -0.078629032 4.044062e-01 -0.8337778 No
9 9 0.338983051 1.709277e-02 2.3847061 Yes
10 10 0.383512545 1.140372e-03 3.2533891 Yes
11 11 0.123213070 3.181806e-01 0.9982037 No
12 12 0.527447833 2.208423e-05 4.2427043 Yes
13 13 0.391983968 8.332622e-03 2.6382862 Yes
14 14 0.163218391 1.402489e-01 1.4748647 No
15 15 0.005497526 8.786925e-01 0.1526269 No
16 16 -0.014196104 8.962361e-01 -0.1304175 No
17 18 0.054187192 6.305166e-01 0.4809998 No
18 20 0.257480315 5.939405e-02 1.8852651 No
19 21 0.055214724 6.396652e-01 0.4681670 No
20 22 -0.123018868 3.073479e-01 -1.0208028 No
21 23 0.151404406 1.405858e-01 1.4736130 No
22 24 0.323876315 9.683680e-03 2.5869245 Yes
23 25 -0.119724376 3.273281e-01 -0.9795099 No
24 26 0.209242619 7.210904e-02 1.7984291 No
25 27 0.352682498 1.731127e-03 3.1328588 Yes
26 28 -0.042682927 6.441681e-01 -0.4618789 No
27 29 0.549019608 6.879740e-03 2.7026101 Yes
28 31 0.351373855 2.117050e-02 2.3049299 Yes
29 32 0.244168881 1.012097e-02 2.5716687 Yes
30 33 -0.073053892 5.740724e-01 -0.5620641 No
31 34 0.295681063 2.650012e-03 3.0056646 Yes
32 35 0.020270270 8.979903e-01 0.1282004 No
33 36 0.155368926 3.855721e-02 2.0688814 Yes
34 37 0.059753086 2.422209e-01 1.1694536 No
35 38 0.370309684 3.169505e-03 2.9508015 Yes
36 39 0.045280263 5.320414e-01 0.6248928 No
37 40 0.345268542 3.826175e-03 2.8921482 Yes
38 41 0.432295438 4.375451e-04 3.5163371 Yes
39 42 0.336065574 5.579882e-03 2.7714991 Yesmean(kappa_results_mental$Kappa)[1] 0.1836504sd(kappa_results_mental$Kappa)[1] 0.1915899hist(kappa_results_mental$Kappa, freq = FALSE, xlim = c(-1,1))
x<-seq(-1,+1,by=0.01)#Histogramm Kappa Werte - Signifikante in Rot
library(ggplot2)
plot_histogram_kappa <- function(data = kappa_results_mental, column = "Kappa", signif_column = "Signifikant") {
ggplot(data, aes_string(x = column, fill = signif_column)) +
geom_histogram(bins = nrow(data), color = "black", alpha = 0.7) +
scale_fill_manual(values = c("Yes" = "red", "No" = "skyblue")) +
labs(title = paste("Histogram of", column),
x = column,
y = "Frequency") +
theme_minimal()
}
plot_histogram_kappa()Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
#Anzahl Signifkanter Werte Mental
signifikante_anzahl <- sum(kappa_results_mental$Signifikant == "Yes")
print(signifikante_anzahl)[1] 18#Anzahl nicht signifikanter Werte Mental
signifikante_anzahl <- sum(kappa_results_mental$Signifikant == "No")
print(signifikante_anzahl)[1] 21##Für Somatic
# Teststatistik aus Kappa2 ausgeben Z-Wert
wz_value_somatic <- NULL
for(id in unique(Somatic_all$ID)){
wz_value_somatic <- c(wz_value_somatic, as.numeric(unlist(kappa2(Somatic_all[Somatic_all$ID == id,4:5],
weight = "squared"))["statistic"]))
}
# P-Wert aus Kappa2 ausgeben
wp_value_somatic <- NULL
for(id in unique(Somatic_all$ID)){
wp_value_somatic <- c(wp_value_somatic, as.numeric(unlist(kappa2(Somatic_all[Somatic_all$ID == id,4:5],
weight = "squared"))["p.value"]))
}
#Data Frame mit Test-Statistiken erstellen
kappa_results_somatic <- data.frame(
ID = unique(Mental_all$ID),
Kappa = wkappa_somatic,
P_Value = wp_value_somatic,
Z_Value = wz_value_somatic
)#Signifkanz Yes No hinzufügen - Tabelle ausgeben Mean berechnen
kappa_results_somatic$Signifikant <- ifelse(kappa_results_somatic$P_Value < 0.05, "Yes", "No")
print(kappa_results_somatic) ID Kappa P_Value Z_Value Signifikant
1 1 0.11322249 6.546590e-01 0.4472993 No
2 2 0.57559958 1.290430e-02 2.4863995 Yes
3 3 0.61205767 6.136255e-04 3.4255192 Yes
4 4 0.41471572 4.393085e-02 2.0147500 Yes
5 5 0.72046589 4.337923e-02 2.0200401 Yes
6 6 0.32608696 6.347115e-03 2.7292885 Yes
7 7 0.48314607 2.719007e-02 2.2087782 Yes
8 8 0.31178311 4.578045e-02 1.9974119 Yes
9 9 0.46496815 6.750958e-03 2.7088858 Yes
10 10 0.44536549 5.155950e-02 1.9467935 No
11 11 0.47500000 1.506679e-03 3.1733941 Yes
12 12 0.66180758 1.651436e-02 2.3973451 Yes
13 13 0.39389736 1.035634e-01 1.6278183 No
14 14 0.52116788 5.699558e-01 0.5681166 No
15 15 0.62975027 1.217502e-01 1.5474689 No
16 16 0.19298246 2.672575e-01 1.1094004 No
17 18 0.09090909 7.592395e-01 0.3064797 No
18 20 -0.11111111 1.536795e-04 3.7850471 Yes
19 21 0.44501279 6.210622e-03 2.7364490 Yes
20 22 0.59029650 8.502448e-03 2.6314376 Yes
21 23 0.65190268 5.611398e-03 2.7696650 Yes
22 24 0.57295474 7.955439e-03 2.6539552 Yes
23 25 0.42442563 2.213819e-02 2.2879885 Yes
24 26 0.49077021 3.453391e-01 0.9436684 No
25 27 0.41520468 3.947559e-02 2.0591947 Yes
26 28 0.26898734 2.851411e-02 2.1901397 Yes
27 29 -0.04651163 3.110147e-04 3.6059486 Yes
28 31 0.30990415 7.636491e-01 -0.3006924 No
29 32 0.40412044 1.174661e-02 2.5196665 Yes
30 33 0.19474117 5.257600e-01 -0.6344916 No
31 34 -0.02057613 6.834710e-03 2.7047924 Yes
32 35 0.45833333 7.065065e-04 3.3870417 Yes
33 36 0.51100244 4.735782e-04 3.4952724 Yes
34 37 0.47324415 3.298711e-01 0.9743736 No
35 38 0.49019608 3.524315e-01 0.9298833 No
36 39 0.33717105 1.146217e-01 1.5777558 No
37 40 0.38170347 1.684737e-02 2.3900218 Yes
38 41 0.19672131 4.044859e-02 2.0491384 Yes
39 42 0.15244408 6.938522e-05 3.9783841 Yesmean(kappa_results_somatic$Kappa)[1] 0.3852273sd(kappa_results_somatic$Kappa)[1] 0.2013546hist(kappa_results_somatic$Kappa, freq = FALSE, xlim = c(-1,1))
x<-seq(-1,+1,by=0.01)#Histogramm Kappa Werte - Signifikante in Rot
library(ggplot2)
plot_histogram_kappa <- function(data = kappa_results_somatic, column = "Kappa", signif_column = "Signifikant") {
ggplot(data, aes_string(x = column, fill = signif_column)) +
geom_histogram(bins = nrow(data), color = "black", alpha = 0.7) +
scale_fill_manual(values = c("Yes" = "red", "No" = "skyblue")) +
labs(title = paste("Histogram of", column),
x = column,
y = "Frequency") +
theme_minimal()
}
plot_histogram_kappa()
#Anzahl Signifkanter Werte Somatic
signifikante_anzahl <- sum(kappa_results_somatic$Signifikant == "Yes")
print(signifikante_anzahl)[1] 26#Anzahl nicht signifikanter Werte Somatic
signifikante_anzahl <- sum(kappa_results_somatic$Signifikant == "No")
print(signifikante_anzahl)[1] 13