library(tmaptools)
library(irr)## Ładowanie wymaganego pakietu: lpSolvelibrary(readxl)
dane_2011<- read_excel("C:/Users/Mateusz/Desktop/Semestr III/Wielowymiarowa analiza danych/Projekt/WAD_dane.xlsx", sheet ="2011")
dane_2021<- read_excel("C:/Users/Mateusz/Desktop/Semestr III/Wielowymiarowa analiza danych/Projekt/WAD_dane.xlsx", sheet ="2021")
## metoda bezwzrocowa - zmienne wagi
Imp_2011<-dane_2011$Import
Bezr_2011<-dane_2011$Bezrobocie
Wyd_2011<-dane_2011$Wydatki
Eks_zyw_2011<-dane_2011$Eksport_zwynosc
Eks_us_2011<-dane_2011$Eksport_uslugi
Imp_2021<-dane_2021$Import
Bezr_2021<-dane_2021$Bezrobocie
Wyd_2021<-dane_2021$Wydatki
Eks_zyw_2021<-dane_2021$Eksport_zwynosc
Eks_us_2021<-dane_2021$Eksport_uslugi
dane_TMR_11<-cbind(Imp_2011,Bezr_2011, Wyd_2011, Eks_zyw_2011, Eks_us_2011)
dane_TMR_11<-as.data.frame(dane_TMR_11)
dane_TMR_21<-cbind(Imp_2021,Bezr_2021,Wyd_2021,Eks_zyw_2021,Eks_us_2021)
dane_TMR_21<-as.data.frame(dane_TMR_21)
n<-ncol(dane_TMR_11)
m<-nrow(dane_TMR_11)
stin<-c(1,0,1,1,1)
for(i in 1:n) {
if(stin[i]==0)dane_TMR_11[,i]=1/dane_TMR_11[,i]
}
stin<-c(1,0,1,1,1)
for(i in 1:n) {
if(stin[i]==0)dane_TMR_21[,i]=1/dane_TMR_21[,i]
}# Wyznaczenie wektorów wag wg współczynika zmienności
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Wagi dla 2011
V_dane_11 <- dane_TMR_11 %>%
select(Imp_2011, Bezr_2011, Wyd_2011, Eks_zyw_2011, Eks_us_2011) %>%
summarise_all(~ sd(.) / mean(.))
w_11 <- V_dane_11 / sum(V_dane_11)
print(V_dane_11)## Imp_2011 Bezr_2011 Wyd_2011 Eks_zyw_2011 Eks_us_2011
## 1 0.4263571 0.3983554 0.1559059 0.7411408 1.157544print(w_11)## Imp_2011 Bezr_2011 Wyd_2011 Eks_zyw_2011 Eks_us_2011
## 1 0.1480765 0.1383513 0.05414709 0.2574029 0.4020222# Wagi dla 2021
V_dane_21 <- dane_TMR_21 %>%
select(Imp_2021, Bezr_2021, Wyd_2021, Eks_zyw_2021, Eks_us_2021) %>%
summarise_all(~ sd(.) / mean(.))
w_21 <- V_dane_21 / sum(V_dane_21)
print(V_dane_21)## Imp_2021 Bezr_2021 Wyd_2021 Eks_zyw_2021 Eks_us_2021
## 1 0.4719872 0.3902289 0.1519391 0.6797999 1.24032print(w_21)## Imp_2021 Bezr_2021 Wyd_2021 Eks_zyw_2021 Eks_us_2021
## 1 0.1608531 0.1329899 0.0517808 0.2316756 0.4227007# Definiowanie macierzy znormalizowanych wartosci
# Definiowanie parametrów a i b do normalizacji
# Wyznaczenie wartości zmiennych znormalizowanych - Unitaryzacja
TMR_norn_11<-matrix(0,m,n)
for(i in 1:n) {
a_11<-min(dane_TMR_11[,i])
b_11<-max(dane_TMR_11[,i])
TMR_norn_11[,i]=(dane_TMR_11[,i]-a_11)/(b_11-a_11)
}
TMR_norn_21<-matrix(0,m,n)
for(i in 1:n) {
a_21<-min(dane_TMR_21[,i])
b_21<-max(dane_TMR_21[,i])
TMR_norn_21[,i]=(dane_TMR_21[,i]-a_21)/(b_21-a_21)
}
# Macierz zmiennej syntetycznej - wyznaczenie taksonomicznego wskaźnika rozwoju TMR
TMR_11<-matrix(0,m,1)
for(i in 1:m) {
TMR_11[i,]<-weighted.mean(TMR_norn_11[i,],w_11)
}
TMR_21<-matrix(0,m,1)
for(i in 1:m) {
TMR_21[i,]<-weighted.mean(TMR_norn_21[i,],w_21)
}
# Przypisanie poszczególnym obiektom określonych rang dla metody z unitaryzacją
Rank_11_1<-rank(-TMR_11)
Rank_21_1<-rank(-TMR_21)# Wyznaczenie wartości zmiennych znormalizowanych - standaryzacja
TMR_stand_11<-scale(dane_TMR_11)
TMR_stand_21<-scale(dane_TMR_21)
TMR_dod_11<-matrix(0,m,n)
for(i in 1:n) {
delta_11=-min(TMR_stand_11[,i])+(1/5)*sd(TMR_stand_11[,i])
TMR_dod_11[,i]=TMR_stand_11[,i]+delta_11
}
TMR_dod_21<-matrix(0,m,n)
for(i in 1:n) {
delta_21=-min(TMR_stand_21[,i])+(1/5)*sd(TMR_stand_21[,i])
TMR_dod_21[,i]=TMR_stand_21[,i]+delta_21
}
TMR_s_11<-matrix(0,m,1)
for(i in 1:m) {
TMR_s_11[i,]<-weighted.mean(TMR_dod_11[i,],w_11)
}
TMR_s_21<-matrix(0,m,1)
for(i in 1:m) {
TMR_s_21[i,]<-weighted.mean(TMR_dod_21[i,],w_21)
}
# Wyznaczenie i znormalizowanie zmiennej syntetycznej
TMR_s_11<-TMR_s_11/max(TMR_s_11)
Rank_11_2<-rank(-TMR_s_11)
TMR_s_21<-TMR_s_21/max(TMR_s_21)
Rank_21_2<-rank(-TMR_s_21)# Wyniki oraz ranking po standaryzacji
Wyniki_11<-cbind(dane_2011$Państwo, TMR_11, Rank_11_1)
colnames(Wyniki_11)<-c("Państwo","TMR","Ranking 1")
Wyniki_11_s<-cbind(dane_2011$Państwo, TMR_s_11, Rank_11_2)
colnames(Wyniki_11_s)<-c("Państwo","TMR_2","Ranking 2")
Wyniki_21<-cbind(dane_2021$Państwo, TMR_21, Rank_21_1)
colnames(Wyniki_21)<-c("Państwo","TMR","Ranking 1")
Wyniki_21_s<-cbind(dane_2021$Państwo, TMR_s_21, Rank_21_2)
colnames(Wyniki_21_s)<-c("Państwo","TMR_2","Ranking 2")
wyniki_sort_11 <-Wyniki_11[order(Wyniki_11[,2], decreasing = TRUE),]
wyniki_sort_11_s <-Wyniki_11_s[order(Wyniki_11_s[,2], decreasing = TRUE),]
wyniki_sort_21 <-Wyniki_21[order(Wyniki_21[,2], decreasing = TRUE),]
wyniki_sort_21_s <-Wyniki_21_s[order(Wyniki_21_s[,2], decreasing = TRUE),]
print(wyniki_sort_11)## Państwo TMR Ranking 1
## [1,] "Germany" "0.555146752658724" "1"
## [2,] "Netherlands" "0.540748674469786" "2"
## [3,] "France" "0.523577795613295" "3"
## [4,] "Luxembourg" "0.419007963482722" "4"
## [5,] "Iceland" "0.407286920015772" "5"
## [6,] "Belgium" "0.397233928839173" "6"
## [7,] "Cyprus" "0.359178492495347" "7"
## [8,] "Denmark" "0.341255880279047" "8"
## [9,] "Ireland" "0.314805997711313" "9"
## [10,] "Spain" "0.3094416118989" "10"
## [11,] "Austria" "0.308747063242228" "11"
## [12,] "Italy" "0.285024885007278" "12"
## [13,] "Sweden" "0.236204532284437" "13"
## [14,] "Greece" "0.205166326204421" "14"
## [15,] "Czechia" "0.202302554426701" "15"
## [16,] "Hungary" "0.198199349293399" "16"
## [17,] "Poland" "0.196300374910953" "17"
## [18,] "Lithuania" "0.185002183475225" "18"
## [19,] "Bulgaria" "0.172263932079249" "19"
## [20,] "Latvia" "0.165168378643589" "20"
## [21,] "Estonia" "0.161136081239876" "21"
## [22,] "Slovenia" "0.15825150147438" "22"
## [23,] "Portugal" "0.155197112404035" "23"
## [24,] "Finland" "0.155050365511968" "24"
## [25,] "Croatia" "0.144866819640343" "25"
## [26,] "Romania" "0.141987190334443" "26"
## [27,] "Slovak Republic" "0.135167346406885" "27"print(wyniki_sort_11_s)## Państwo TMR_2 Ranking 2
## [1,] "Netherlands" "1" "1"
## [2,] "Germany" "0.987719832527842" "2"
## [3,] "France" "0.948841077678039" "3"
## [4,] "Iceland" "0.828229469142451" "4"
## [5,] "Luxembourg" "0.81976513439139" "5"
## [6,] "Belgium" "0.767245862755033" "6"
## [7,] "Cyprus" "0.741646344438549" "7"
## [8,] "Denmark" "0.677562780491631" "8"
## [9,] "Ireland" "0.631802328885134" "9"
## [10,] "Spain" "0.6073700964288" "10"
## [11,] "Austria" "0.603918493778107" "11"
## [12,] "Italy" "0.555283948321888" "12"
## [13,] "Sweden" "0.480974461696951" "13"
## [14,] "Greece" "0.450331052406068" "14"
## [15,] "Hungary" "0.442093574694112" "15"
## [16,] "Czechia" "0.435236700586486" "16"
## [17,] "Lithuania" "0.431327012440846" "17"
## [18,] "Poland" "0.425941961713011" "18"
## [19,] "Bulgaria" "0.401094130006805" "19"
## [20,] "Latvia" "0.394062653593395" "20"
## [21,] "Estonia" "0.38311505330825" "21"
## [22,] "Slovenia" "0.366159618612543" "22"
## [23,] "Portugal" "0.358860234110229" "23"
## [24,] "Finland" "0.347117689910775" "24"
## [25,] "Croatia" "0.345864315039994" "25"
## [26,] "Slovak Republic" "0.335478758748766" "26"
## [27,] "Romania" "0.330552852581491" "27"print(wyniki_sort_21)## Państwo TMR Ranking 1
## [1,] "Germany" "0.581262974741201" "1"
## [2,] "Ireland" "0.545703490629052" "2"
## [3,] "Netherlands" "0.491161974018424" "3"
## [4,] "France" "0.45631187527071" "4"
## [5,] "Luxembourg" "0.427851037110723" "5"
## [6,] "Iceland" "0.338626085035038" "6"
## [7,] "Belgium" "0.322501258465512" "7"
## [8,] "Denmark" "0.302345323788362" "8"
## [9,] "Poland" "0.294852752341856" "9"
## [10,] "Cyprus" "0.280539098797375" "10"
## [11,] "Czechia" "0.245842379170307" "11"
## [12,] "Spain" "0.232089093288165" "12"
## [13,] "Hungary" "0.222008801072701" "13"
## [14,] "Austria" "0.199530429751709" "14"
## [15,] "Bulgaria" "0.195608595036433" "15"
## [16,] "Sweden" "0.191792481388731" "16"
## [17,] "Lithuania" "0.188948188182286" "17"
## [18,] "Italy" "0.186909331749653" "18"
## [19,] "Latvia" "0.185959099421511" "19"
## [20,] "Greece" "0.179849026247905" "20"
## [21,] "Romania" "0.173044467768932" "21"
## [22,] "Croatia" "0.170212818240754" "22"
## [23,] "Estonia" "0.16674701202426" "23"
## [24,] "Slovenia" "0.163625718668483" "24"
## [25,] "Portugal" "0.16130347365309" "25"
## [26,] "Slovak Republic" "0.14894990380221" "26"
## [27,] "Finland" "0.113975051207146" "27"print(wyniki_sort_21_s)## Państwo TMR_2 Ranking 2
## [1,] "Germany" "1" "1"
## [2,] "Ireland" "0.953982448338234" "2"
## [3,] "Netherlands" "0.917540505087183" "3"
## [4,] "Luxembourg" "0.855096602855485" "4"
## [5,] "France" "0.81629314306335" "5"
## [6,] "Iceland" "0.761605154579551" "6"
## [7,] "Belgium" "0.651162567371065" "7"
## [8,] "Cyprus" "0.638423190047825" "8"
## [9,] "Denmark" "0.627228555287753" "9"
## [10,] "Poland" "0.612796715602737" "10"
## [11,] "Czechia" "0.537930167781058" "11"
## [12,] "Hungary" "0.503359192738625" "12"
## [13,] "Spain" "0.486695534067429" "13"
## [14,] "Bulgaria" "0.464892879615474" "14"
## [15,] "Lithuania" "0.454409727864465" "15"
## [16,] "Latvia" "0.454189466361816" "16"
## [17,] "Austria" "0.439859844340585" "17"
## [18,] "Greece" "0.426547276719227" "18"
## [19,] "Croatia" "0.414980775142145" "19"
## [20,] "Sweden" "0.414974124494963" "20"
## [21,] "Estonia" "0.410427037192365" "21"
## [22,] "Romania" "0.406653608323577" "22"
## [23,] "Italy" "0.398669416192781" "23"
## [24,] "Slovenia" "0.398478193708842" "24"
## [25,] "Portugal" "0.385854985959358" "25"
## [26,] "Slovak Republic" "0.375003526566143" "26"
## [27,] "Finland" "0.289075856194691" "27"# Dodatkowe badanie zbieżności rang
check_11<-data.frame(Rank_11_1, Rank_11_2)
check_21<-data.frame(Rank_21_1, Rank_21_2)
kendall(check_11,correct = FALSE)## Kendall's coefficient of concordance W
##
## Subjects = 27
## Raters = 2
## W = 0.998
##
## Chisq(26) = 51.9
## p-value = 0.00184kendall(check_21,correct = FALSE)## Kendall's coefficient of concordance W
##
## Subjects = 27
## Raters = 2
## W = 0.986
##
## Chisq(26) = 51.3
## p-value = 0.00221Wyniki wskazują na występowanie istotnej korelacji P-value < 0,05 wskazują na występowanie zbieżności rang (istotnej zależności pomiędzy rankingami)
###### Hellwig TMR
dane_TMR_11.stand<-scale(dane_TMR_11)
wzorzec_11<-rep(0,n)
odl_11<-matrix(0,m,n)
for(i in 1:n) {
ifelse(stin[i]==0, wzorzec_11[i]<-min(dane_TMR_11.stand[,i]),wzorzec_11[i]<-max(dane_TMR_11.stand[,i]))
odl_11[,i]<-(dane_TMR_11.stand[,i]-wzorzec_11[i])^2
}
dane_TMR_21.stand<-scale(dane_TMR_21)
wzorzec_21<-rep(0,n)
odl_21<-matrix(0,m,n)
for(i in 1:n) {
ifelse(stin[i]==0, wzorzec_21[i]<-min(dane_TMR_21.stand[,i]),wzorzec_21[i]<-max(dane_TMR_21.stand[,i]))
odl_21[,i]<-(dane_TMR_21.stand[,i]-wzorzec_21[i])^2
}
odl_11.wektor<-sqrt(rowSums(odl_11))
TMR_11_e=1-odl_11.wektor/(mean(odl_11.wektor)+2*sd(odl_11.wektor))
TMR_11_e<-as.matrix(TMR_11_e)
rank_11_e<-rank(-TMR_11_e)
odl_21.wektor<-sqrt(rowSums(odl_21))
TMR_21_e=1-odl_21.wektor/(mean(odl_21.wektor)+2*sd(odl_21.wektor))
TMR_21_e<-as.matrix(TMR_21_e)
rank_21_e<-rank(-TMR_21_e)
wzorzec_11_m<-rep(0,n)
odl_11_m<-matrix(0,m,n)
for(i in 1:n) {
ifelse(stin[i]==0, wzorzec_11_m[i]<-min(dane_TMR_11.stand[,i]),wzorzec_11_m[i]<-max(dane_TMR_11.stand[,i]))
odl_11_m[,i]<-abs(dane_TMR_11.stand[,i]-wzorzec_11_m[i])
}
wzorzec_21_m<-rep(0,n)
odl_21_m<-matrix(0,m,n)
for(i in 1:n) {
ifelse(stin[i]==0, wzorzec_21_m[i]<-min(dane_TMR_21.stand[,i]),wzorzec_21_m[i]<-max(dane_TMR_21.stand[,i]))
odl_21_m[,i]<-abs(dane_TMR_21.stand[,i]-wzorzec_21_m[i])
}
odl_11_m.wektor<-rowSums(odl_11_m)
TMR_11_m=1-odl_11_m.wektor/(mean(odl_11_m.wektor)+2*sd(odl_11_m.wektor))
TMR_11_m<-as.matrix(TMR_11_m)
rank_11_m<-rank(-TMR_11_m)
odl_21_m.wektor<-rowSums(odl_21_m)
TMR_21_m=1-odl_21_m.wektor/(mean(odl_21_m.wektor)+2*sd(odl_21_m.wektor))
TMR_21_m<-as.matrix(TMR_21_m)
rank_21_m<-rank(-TMR_21_m)# Wyniki metod wzorcowych
Wyniki_11_e<-cbind(dane_2011$Państwo, TMR_11_e, rank_11_e)
colnames(Wyniki_11_e)<-c("Państwo","TMR_e","Ranking 1")
Wyniki_11_m<-cbind(dane_2011$Państwo, TMR_11_m, rank_11_m)
colnames(Wyniki_11_m)<-c("Państwo","TMR_m","Ranking 2")
Wyniki_21_e<-cbind(dane_2021$Państwo, TMR_21_e, rank_21_e)
colnames(Wyniki_21_e)<-c("Państwo","TMR_e","Ranking 1")
Wyniki_21_m<-cbind(dane_2021$Państwo, TMR_21_m, rank_21_m)
colnames(Wyniki_21_m)<-c("Państwo","TMR_m","Ranking 2")
wyniki_sort_11_e <-Wyniki_11_e[order(Wyniki_11_e[,2], decreasing = TRUE),]
wyniki_sort_11_m <-Wyniki_11_m[order(Wyniki_11_m[,2], decreasing = TRUE),]
wyniki_sort_21_e <-Wyniki_21_e[order(Wyniki_21_e[,2], decreasing = TRUE),]
wyniki_sort_21_m <-Wyniki_21_m[order(Wyniki_21_m[,2], decreasing = TRUE),]
print(wyniki_sort_11_e)## Państwo TMR_e Ranking 1
## [1,] "Belgium" "0.307258269666811" "1"
## [2,] "Ireland" "0.299680513852538" "2"
## [3,] "Netherlands" "0.265204430609635" "3"
## [4,] "Denmark" "0.256212188282547" "4"
## [5,] "Iceland" "0.254951478283152" "5"
## [6,] "Cyprus" "0.247576226276359" "6"
## [7,] "France" "0.247385488115727" "7"
## [8,] "Lithuania" "0.218777595925621" "8"
## [9,] "Spain" "0.21325513402757" "9"
## [10,] "Hungary" "0.212143258454552" "10"
## [11,] "Latvia" "0.195006940259862" "11"
## [12,] "Greece" "0.190352446085672" "12"
## [13,] "Estonia" "0.173850077760484" "13"
## [14,] "Croatia" "0.147920135587852" "14"
## [15,] "Slovak Republic" "0.147107083260934" "15"
## [16,] "Luxembourg" "0.137086028792803" "16"
## [17,] "Sweden" "0.122571615811354" "17"
## [18,] "Bulgaria" "0.122074031196662" "18"
## [19,] "Portugal" "0.121234073995656" "19"
## [20,] "Germany" "0.120255568645362" "20"
## [21,] "Poland" "0.117580252188079" "21"
## [22,] "Slovenia" "0.117366783044626" "22"
## [23,] "Italy" "0.106462981633624" "23"
## [24,] "Czechia" "0.0981346732529923" "24"
## [25,] "Finland" "0.0649209044187152" "25"
## [26,] "Austria" "0.0310160545872049" "26"
## [27,] "Romania" "-0.0427990447797786" "27"print(wyniki_sort_11_m)## Państwo TMR_m Ranking 2
## [1,] "Iceland" "0.388030486590157" "1"
## [2,] "France" "0.374151892111106" "2"
## [3,] "Ireland" "0.343075622005007" "3"
## [4,] "Belgium" "0.331135536634502" "4"
## [5,] "Denmark" "0.330335283112524" "5"
## [6,] "Netherlands" "0.325563367761539" "6"
## [7,] "Spain" "0.318882276766429" "7"
## [8,] "Cyprus" "0.292793987476084" "8"
## [9,] "Greece" "0.283531378869978" "9"
## [10,] "Lithuania" "0.260380561728957" "10"
## [11,] "Hungary" "0.244375623274245" "11"
## [12,] "Latvia" "0.241054971539522" "12"
## [13,] "Croatia" "0.225424489503141" "13"
## [14,] "Estonia" "0.20749689358722" "14"
## [15,] "Germany" "0.206438622403734" "15"
## [16,] "Luxembourg" "0.204650549882571" "16"
## [17,] "Slovak Republic" "0.19808394530666" "17"
## [18,] "Sweden" "0.19126351211664" "18"
## [19,] "Portugal" "0.164361016245911" "19"
## [20,] "Bulgaria" "0.1395101822694" "20"
## [21,] "Italy" "0.138435172318498" "21"
## [22,] "Slovenia" "0.135197903455011" "22"
## [23,] "Poland" "0.126526025216975" "23"
## [24,] "Finland" "0.114703668656675" "24"
## [25,] "Czechia" "0.0942963955822731" "25"
## [26,] "Austria" "0.0154628053276626" "26"
## [27,] "Romania" "-0.0572682997262524" "27"print(wyniki_sort_21_e)## Państwo TMR_e Ranking 1
## [1,] "Cyprus" "0.304768814237589" "1"
## [2,] "Netherlands" "0.284514420514218" "2"
## [3,] "Luxembourg" "0.282166614318719" "3"
## [4,] "Belgium" "0.275902175512388" "4"
## [5,] "Iceland" "0.258888133701888" "5"
## [6,] "France" "0.224914267561364" "6"
## [7,] "Denmark" "0.214640249126291" "7"
## [8,] "Latvia" "0.213689530759301" "8"
## [9,] "Greece" "0.209393557505048" "9"
## [10,] "Spain" "0.182739362300037" "10"
## [11,] "Croatia" "0.179965933645935" "11"
## [12,] "Lithuania" "0.174285988702859" "12"
## [13,] "Estonia" "0.155632031568424" "13"
## [14,] "Slovak Republic" "0.149132741689271" "14"
## [15,] "Austria" "0.142516928575114" "15"
## [16,] "Bulgaria" "0.14224576502361" "16"
## [17,] "Sweden" "0.136297972851027" "17"
## [18,] "Ireland" "0.124109083549884" "18"
## [19,] "Hungary" "0.116838495712284" "19"
## [20,] "Portugal" "0.0985941635245534" "20"
## [21,] "Slovenia" "0.0926218869626543" "21"
## [22,] "Germany" "0.0915820763019873" "22"
## [23,] "Italy" "0.0908231622256679" "23"
## [24,] "Romania" "0.0810451662916164" "24"
## [25,] "Finland" "0.0648639987819484" "25"
## [26,] "Poland" "0.0568500602274636" "26"
## [27,] "Czechia" "-0.0160518608600797" "27"print(wyniki_sort_21_m)## Państwo TMR_m Ranking 2
## [1,] "Iceland" "0.451225490884121" "1"
## [2,] "Luxembourg" "0.372265322381122" "2"
## [3,] "France" "0.36506003373212" "3"
## [4,] "Netherlands" "0.345198166335735" "4"
## [5,] "Cyprus" "0.326822958296158" "5"
## [6,] "Belgium" "0.315842207597642" "6"
## [7,] "Greece" "0.300701055230571" "7"
## [8,] "Spain" "0.287452692983524" "8"
## [9,] "Sweden" "0.259244303189918" "9"
## [10,] "Denmark" "0.249371643156146" "10"
## [11,] "Latvia" "0.242101755715261" "11"
## [12,] "Ireland" "0.241804916947911" "12"
## [13,] "Croatia" "0.235698246072888" "13"
## [14,] "Germany" "0.205793891775842" "14"
## [15,] "Slovak Republic" "0.193129528450599" "15"
## [16,] "Lithuania" "0.185869169317986" "16"
## [17,] "Austria" "0.183673479246373" "17"
## [18,] "Estonia" "0.171361653118273" "18"
## [19,] "Finland" "0.169315180365437" "19"
## [20,] "Italy" "0.163688319244894" "20"
## [21,] "Bulgaria" "0.139816981978463" "21"
## [22,] "Portugal" "0.121779868660883" "22"
## [23,] "Hungary" "0.111785794854015" "23"
## [24,] "Slovenia" "0.102932948987088" "24"
## [25,] "Romania" "0.0875854393398751" "25"
## [26,] "Poland" "0.0381820989876963" "26"
## [27,] "Czechia" "-0.0118877196855918" "27"# Dodatkowe badanie zbieżności rang
check_11_em<-data.frame(rank_11_e, rank_11_m)
check_21_em<-data.frame(rank_21_e, rank_21_m)
kendall(check_11_em,correct = FALSE)## Kendall's coefficient of concordance W
##
## Subjects = 27
## Raters = 2
## W = 0.98
##
## Chisq(26) = 51
## p-value = 0.0024kendall(check_21_em,correct = FALSE)## Kendall's coefficient of concordance W
##
## Subjects = 27
## Raters = 2
## W = 0.94
##
## Chisq(26) = 48.9
## p-value = 0.00424