library(rio)
p3=import("p1.xlsx")
## New names:
## • `` -> `...4`
p3=p3[-1,]
p3=na.omit(p3)
names2=c("safe","mili ","co ","Country")
names(p3)=names2
P3.1=import("ETR.xlsx")
## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
## • `` -> `...20`
## • `` -> `...21`
## • `` -> `...22`
## • `` -> `...23`
## • `` -> `...24`
## • `` -> `...25`
P3.1[,c(6:25)]=NULL
names1=c("Country","Food ","Natural ","Rapid ","Water ")
names(P3.1)=names1
P3.1=P3.1[-1,]
setdiff(p3$Country,P3.1$Country)
## character(0)
merge(p3,P3.1)
## Country safe mili co Food Natural Rapid
## 1 Afghanistan 4.127 2.472 3.650 4.0 5.0 5.0
## 2 Albania 2.120 1.666 1.403 2.0 4.0 1.0
## 3 Algeria 2.302 2.041 2.068 2.0 5.0 3.0
## 4 Angola 2.413 1.706 1.666 5.0 3.0 5.0
## 5 Argentina 2.656 1.611 1.201 2.0 4.0 2.0
## 6 Armenia 1.977 2.041 1.990 2.0 2.0 1.0
## 7 Australia 1.657 1.848 1.266 1.0 4.0 3.0
## 8 Austria 1.452 1.331 1.097 1.0 3.0 1.0
## 9 Azerbaijan 2.528 2.108 2.579 2.0 1.0 1.0
## 10 Bahrain 2.405 1.717 1.955 2.0 1.0 3.0
## 11 Bangladesh 2.438 1.534 2.004 4.0 4.0 2.0
## 12 Belarus 2.526 1.896 2.208 2.0 1.0 1.0
## 13 Belgium 1.806 1.602 1.141 1.0 2.0 1.0
## 14 Benin 2.574 2.022 1.666 5.0 4.0 5.0
## 15 Bhutan 1.601 1.336 1.436 2.0 5.0 3.0
## 16 Bolivia 2.503 1.934 1.403 3.0 4.0 3.0
## 17 Bosnia and Herzegovina 2.044 1.526 1.835 2.0 2.0 1.0
## 18 Botswana 2.441 1.812 1.000 4.0 5.0 3.0
## 19 Brazil 3.284 1.965 1.853 2.0 4.0 1.0
## 20 Bulgaria 1.929 1.633 1.000 2.0 4.0 1.0
## 21 Burkina Faso 2.993 2.189 3.023 4.0 3.0 5.0
## 22 Burundi 3.024 1.556 2.443 5.0 5.0 5.0
## 23 Cambodia 2.080 1.933 1.638 4.0 5.0 3.0
## 24 Cameroon 3.014 1.877 2.967 5.0 4.0 5.0
## 25 Canada 1.637 1.465 1.030 1.0 4.0 2.0
## 26 Central African Republic 3.579 2.273 2.874 5.0 5.0 5.0
## 27 Chad 2.903 2.189 2.552 5.0 5.0 5.0
## 28 Chile 2.361 1.585 1.403 1.0 4.0 1.0
## 29 China 2.107 2.022 1.858 1.0 4.0 2.0
## 30 Colombia 3.596 2.052 2.099 3.0 4.0 1.0
## 31 Costa Rica 2.229 1.614 1.201 2.0 4.0 1.0
## 32 Cote d' Ivoire 2.764 1.724 1.694 4.0 3.0 5.0
## 33 Croatia 1.564 1.565 1.201 1.0 5.0 1.0
## 34 Cuba 2.614 1.533 1.805 3.0 5.0 1.0
## 35 Cyprus 2.251 1.605 1.604 1.0 2.0 1.0
## 36 Czech Republic 1.566 1.263 1.034 1.0 3.0 1.0
## 37 Democratic Republic of the Congo 3.747 1.881 3.413 5.0 4.0 5.0
## 38 Denmark 1.295 1.368 1.242 1.0 1.0 1.0
## 39 Djibouti 2.561 2.140 1.856 3.0 5.0 3.0
## 40 Dominican Republic 2.614 1.720 1.403 3.0 5.0 2.0
## 41 Ecuador 2.509 1.866 1.426 4.0 5.0 3.0
## 42 Egypt 2.503 2.014 2.426 3.0 5.0 4.0
## 43 El Salvador 3.062 1.879 1.436 4.0 5.0 1.0
## 44 Equatorial Guinea 2.200 1.638 1.604 5.0 1.0 5.0
## 45 Eritrea 3.359 1.914 1.705 5.0 1.0 5.0
## 46 Estonia 1.703 1.607 1.638 1.0 2.0 1.0
## 47 Eswatini 2.576 1.706 1.604 5.0 2.0 3.0
## 48 Ethiopia 2.917 1.809 3.457 4.0 4.0 5.0
## 49 Finland 1.349 1.598 1.436 1.0 1.0 1.0
## 50 France 1.867 2.773 1.305 1.0 2.0 1.0
## 51 Gabon 2.378 1.807 1.604 5.0 1.0 4.0
## 52 Georgia 2.413 1.790 1.805 2.0 5.0 1.0
## 53 Germany 1.619 1.750 1.062 1.0 2.0 1.0
## 54 Ghana 2.026 1.726 1.472 4.0 3.0 5.0
## 55 Greece 1.773 2.036 1.805 1.0 4.0 1.0
## 56 Guatemala 2.819 1.698 1.626 4.0 5.0 4.0
## 57 Guinea 2.587 2.069 2.262 5.0 3.0 5.0
## 58 Guinea-Bissau 2.586 2.218 1.615 5.0 4.0 5.0
## 59 Guyana 2.895 1.551 1.604 2.0 5.0 1.0
## 60 Haiti 2.735 1.740 2.077 5.0 5.0 3.0
## 61 Honduras 3.064 1.812 1.604 4.0 4.0 3.0
## 62 Hungary 1.528 1.184 1.421 1.0 2.0 1.0
## 63 Iceland 1.237 1.020 1.000 1.0 1.0 1.0
## 64 India 2.373 2.461 2.982 3.0 4.0 2.0
## 65 Indonesia 2.107 1.496 1.644 2.0 5.0 2.0
## 66 Iran 2.838 2.123 2.923 3.0 5.0 1.0
## 67 Iraq 3.646 2.298 3.207 3.0 5.0 5.0
## 68 Ireland 1.525 1.259 1.015 1.0 3.0 2.0
## 69 Israel 2.237 3.813 2.167 1.0 3.0 4.0
## 70 Italy 1.927 2.013 1.034 1.0 3.0 1.0
## 71 Jamaica 2.791 1.721 1.201 3.0 3.0 1.0
## 72 Japan 1.283 1.308 1.403 1.0 5.0 1.0
## 73 Jordan 2.037 1.964 1.542 3.0 3.0 3.0
## 74 Kazakhstan 2.492 1.781 1.805 2.0 4.0 2.0
## 75 Kenya 2.599 1.794 2.345 5.0 5.0 5.0
## 76 Kosovo 2.266 1.575 1.805 2.0 1.0 1.0
## 77 Kuwait 1.762 2.184 1.431 2.0 1.0 3.0
## 78 Kyrgyz Republic 2.282 1.671 2.006 2.0 4.0 3.0
## 79 Laos 2.076 1.949 1.403 4.0 5.0 3.0
## 80 Latvia 1.798 1.479 1.634 1.0 2.0 1.0
## 81 Lebanon 2.748 2.317 2.701 3.0 4.0 1.0
## 82 Lesotho 2.528 1.748 1.805 5.0 4.0 3.0
## 83 Liberia 2.491 1.804 1.462 5.0 1.0 5.0
## 84 Libya 3.093 2.433 3.160 3.0 5.0 5.0
## 85 Lithuania 1.775 1.735 1.638 1.0 1.0 1.0
## 86 Madagascar 2.612 1.444 1.630 5.0 3.0 5.0
## 87 Malawi 2.144 1.723 1.739 5.0 5.0 5.0
## 88 Malaysia 1.973 1.205 1.015 2.0 4.0 3.0
## 89 Mali 3.589 1.873 2.861 4.0 3.0 5.0
## 90 Mauritania 2.782 1.969 1.638 4.0 4.0 5.0
## 91 Mauritius 2.132 1.373 1.000 3.0 4.0 1.0
## 92 Mexico 3.158 1.736 2.642 2.0 4.0 2.0
## 93 Moldova 2.142 1.243 2.006 2.0 3.0 1.0
## 94 Mongolia 2.459 1.348 1.201 2.0 5.0 3.0
## 95 Montenegro 2.243 1.602 1.403 2.0 2.0 1.0
## 96 Morocco 2.158 1.752 1.891 3.0 4.0 3.0
## 97 Mozambique 2.803 1.677 2.215 5.0 5.0 5.0
## 98 Myanmar 3.116 1.744 2.700 4.0 5.0 2.0
## 99 Namibia 2.569 1.755 1.201 5.0 5.0 4.0
## 100 Nepal 2.493 1.706 1.436 4.0 5.0 2.0
## 101 Netherlands 1.525 2.206 1.045 1.0 2.0 1.0
## 102 New Zealand 1.530 1.168 1.008 1.0 4.0 2.0
## 103 Nicaragua 2.966 1.723 2.006 4.0 4.0 2.0
## 104 Niger 2.957 1.957 2.839 5.0 5.0 5.0
## 105 Nigeria 3.107 2.059 2.806 5.0 4.0 5.0
## 106 North Korea 3.113 3.120 2.610 2.0 4.0 1.0
## 107 North Macedonia 1.928 1.771 1.403 2.0 5.0 1.0
## 108 Norway 1.266 2.184 1.235 1.0 4.0 2.0
## 109 Oman 1.892 2.651 1.403 2.0 4.0 3.0
## 110 Pakistan 2.653 2.581 3.172 3.0 5.0 4.0
## 111 Palestine 2.950 1.991 2.612 3.0 1.0 5.0
## 112 Panama 2.524 1.547 1.403 2.0 3.0 3.0
## 113 Papua New Guinea 2.802 1.697 1.605 2.0 5.0 4.0
## 114 Paraguay 2.534 2.004 1.403 3.0 4.0 2.0
## 115 Peru 2.625 1.938 1.420 3.0 5.0 3.0
## 116 Philippines 2.855 1.700 2.410 4.0 5.0 3.0
## 117 Poland 1.933 1.527 1.403 1.0 3.0 1.0
## 118 Portugal 1.390 1.312 1.007 1.0 4.0 1.0
## 119 Qatar 1.495 2.196 1.246 2.0 1.0 3.0
## 120 Republic of the Congo 2.903 2.194 1.620 5.0 5.0 5.0
## 121 Romania 1.837 1.773 1.007 2.0 5.0 1.0
## 122 Russia 2.912 3.234 2.844 2.0 5.0 1.0
## 123 Rwanda 2.374 1.512 1.858 5.0 3.0 5.0
## 124 Saudi Arabia 2.254 2.614 2.425 2.0 3.0 3.0
## 125 Senegal 2.337 1.701 1.418 4.0 4.0 5.0
## 126 Serbia 1.976 1.796 1.604 2.0 4.0 1.0
## 127 Sierra Leone 2.313 1.605 1.413 5.0 4.0 4.0
## 128 Singapore 1.238 1.943 1.000 1.0 1.0 1.0
## 129 Slovakia 1.835 1.274 1.403 2.0 2.0 1.0
## 130 Slovenia 1.407 1.129 1.403 1.0 5.0 1.0
## 131 Somalia 3.624 2.155 3.474 5.0 5.0 5.0
## 132 South Africa 3.234 1.758 1.613 3.0 2.0 3.0
## 133 South Korea 1.591 2.370 1.805 2.0 4.0 1.0
## 134 South Sudan 3.957 2.594 3.267 5.0 5.0 5.0
## 135 Spain 1.808 1.898 1.218 1.0 3.0 1.0
## 136 Sri Lanka 2.377 2.047 1.855 4.0 5.0 1.0
## 137 Sudan 3.303 2.312 3.040 4.0 4.0 5.0
## 138 Sweden 1.475 1.802 1.221 1.0 4.0 1.0
## 139 Switzerland 1.236 1.929 1.005 1.0 4.0 1.0
## 140 Syria 3.741 2.287 3.828 5.0 5.0 5.0
## 141 Taiwan 1.731 1.704 1.604 2.0 3.0 1.0
## 142 Tajikistan 2.428 1.707 2.057 4.0 3.0 5.0
## 143 Tanzania 2.353 1.687 1.479 4.0 4.0 5.0
## 144 Thailand 2.863 1.562 1.916 2.0 5.0 1.0
## 145 The Gambia 2.373 1.830 1.409 4.0 5.0 5.0
## 146 Timor-Leste 2.369 1.667 1.403 2.0 5.0 5.0
## 147 Togo 2.615 2.020 1.818 4.0 3.0 5.0
## 148 Trinidad and Tobago 2.736 2.098 1.201 3.0 3.0 1.0
## 149 Tunisia 2.508 1.769 1.820 2.0 4.0 2.0
## 150 Turkey 3.217 2.058 3.159 3.0 5.0 1.0
## 151 Turkmenistan 2.501 2.383 1.604 1.0 1.0 3.0
## 152 Uganda 2.762 1.760 1.765 5.0 5.0 5.0
## 153 Ukraine 3.230 2.044 2.765 2.0 4.0 1.0
## 154 United Arab Emirates 1.712 2.652 1.464 2.0 2.0 3.0
## 155 United Kingdom 1.729 2.489 1.042 1.0 1.0 1.0
## 156 United States of America 2.225 3.172 1.770 1.0 4.0 2.0
## 157 Uruguay 2.323 1.582 1.000 2.0 5.0 1.0
## 158 Uzbekistan 2.215 2.213 1.805 3.0 2.0 3.0
## 159 Venezuela 4.007 2.002 2.103 5.0 4.0 3.0
## 160 Vietnam 2.289 1.876 1.403 2.0 5.0 2.0
## 161 Yemen 3.771 2.272 3.559 5.0 5.0 5.0
## 162 Zambia 2.511 1.519 1.604 5.0 3.0 5.0
## 163 Zimbabwe 2.990 2.044 2.112 5.0 4.0 5.0
## Water
## 1 5.0
## 2 3.0
## 3 3.0
## 4 5.0
## 5 3.0
## 6 4.0
## 7 3.0
## 8 1.0
## 9 5.0
## 10 3.0
## 11 2.0
## 12 2.0
## 13 1.0
## 14 5.0
## 15 4.0
## 16 5.0
## 17 1.0
## 18 5.0
## 19 5.0
## 20 1.0
## 21 5.0
## 22 5.0
## 23 5.0
## 24 5.0
## 25 2.0
## 26 5.0
## 27 5.0
## 28 3.0
## 29 3.0
## 30 5.0
## 31 4.0
## 32 5.0
## 33 1.0
## 34 4.0
## 35 1.0
## 36 2.0
## 37 5.0
## 38 1.0
## 39 5.0
## 40 5.0
## 41 5.0
## 42 2.0
## 43 4.0
## 44 5.0
## 45 5.0
## 46 2.0
## 47 5.0
## 48 5.0
## 49 2.0
## 50 1.0
## 51 5.0
## 52 4.0
## 53 1.0
## 54 5.0
## 55 2.0
## 56 4.0
## 57 5.0
## 58 5.0
## 59 4.0
## 60 5.0
## 61 5.0
## 62 1.0
## 63 1.0
## 64 4.0
## 65 5.0
## 66 4.0
## 67 5.0
## 68 1.0
## 69 2.0
## 70 1.0
## 71 4.0
## 72 1.0
## 73 3.0
## 74 4.0
## 75 5.0
## 76 1.0
## 77 2.0
## 78 4.0
## 79 5.0
## 80 2.0
## 81 1.0
## 82 5.0
## 83 5.0
## 84 3.0
## 85 4.0
## 86 5.0
## 87 5.0
## 88 4.0
## 89 5.0
## 90 5.0
## 91 5.0
## 92 3.0
## 93 5.0
## 94 5.0
## 95 1.0
## 96 5.0
## 97 5.0
## 98 5.0
## 99 5.0
## 100 5.0
## 101 1.0
## 102 3.0
## 103 5.0
## 104 5.0
## 105 5.0
## 106 2.0
## 107 2.0
## 108 1.0
## 109 1.0
## 110 4.0
## 111 3.0
## 112 5.0
## 113 5.0
## 114 4.0
## 115 5.0
## 116 5.0
## 117 3.0
## 118 1.0
## 119 1.0
## 120 5.0
## 121 3.0
## 122 4.0
## 123 5.0
## 124 3.0
## 125 5.0
## 126 1.0
## 127 5.0
## 128 1.0
## 129 2.0
## 130 1.0
## 131 5.0
## 132 5.0
## 133 1.0
## 134 5.0
## 135 1.0
## 136 4.0
## 137 5.0
## 138 1.0
## 139 1.0
## 140 2.0
## 141 3.0
## 142 5.0
## 143 5.0
## 144 4.0
## 145 5.0
## 146 5.0
## 147 5.0
## 148 4.0
## 149 3.0
## 150 3.0
## 151 2.0
## 152 5.0
## 153 3.0
## 154 3.0
## 155 1.0
## 156 3.0
## 157 3.0
## 158 3.0
## 159 5.0
## 160 5.0
## 161 5.0
## 162 5.0
## 163 5.0
DATA=merge(p3,P3.1)
str(DATA)
## 'data.frame': 163 obs. of 8 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ safe : num 4.13 2.12 2.3 2.41 2.66 ...
## $ mili : num 2.47 1.67 2.04 1.71 1.61 ...
## $ co : num 3.65 1.4 2.07 1.67 1.2 ...
## $ Food : chr "4.0" "2.0" "2.0" "5.0" ...
## $ Natural : chr "5.0" "4.0" "5.0" "3.0" ...
## $ Rapid : chr "5.0" "1.0" "3.0" "5.0" ...
## $ Water : chr "5.0" "3.0" "3.0" "5.0" ...
DATA$`Food `=as.numeric(DATA$`Food `)
DATA$`Natural `=as.numeric(DATA$`Natural `)
DATA$`Rapid `=as.numeric(DATA$`Rapid `)
DATA$`Water `=as.numeric(DATA$`Water `)
dontselect=c("Country")
select=setdiff(names(DATA),dontselect)
theData=DATA[,select]
theData[!complete.cases(theData),]
## [1] safe mili co Food Natural Rapid Water
## <0 rows> (or 0-length row.names)
library(polycor)
corMatrix=polycor::hetcor(theData)$correlations
library(ggcorrplot)
## Loading required package: ggplot2
ggcorrplot(corMatrix)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## The following object is masked from 'package:polycor':
##
## polyserial
psych::KMO(corMatrix)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.76
## MSA for each item =
## safe mili co Food Natural Rapid Water
## 0.73 0.69 0.71 0.79 0.87 0.76 0.79
cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05
## [1] FALSE
library(matrixcalc)
is.singular.matrix(corMatrix)
## [1] FALSE
fa.parallel(theData, fa = 'fa',correct = T,plot = F)
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Parallel analysis suggests that the number of factors = 2 and the number of components = NA
library(GPArotation)
resfa <- fa(theData,
nfactors = 3,
cor = 'mixed',
rotate = "varimax",
fm="minres")
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
print(resfa$loadings)
##
## Loadings:
## MR3 MR1 MR2
## safe 0.308 0.735 0.502
## mili 0.586
## co 0.210 0.402 0.749
## Food 0.690 0.577 0.129
## Natural 0.116 0.431
## Rapid 0.943 0.204 0.266
## Water 0.668 0.720
##
## MR3 MR1 MR2
## SS loadings 1.965 1.780 1.255
## Proportion Var 0.281 0.254 0.179
## Cumulative Var 0.281 0.535 0.714
print(resfa$loadings,cutoff = 0.5)
##
## Loadings:
## MR3 MR1 MR2
## safe 0.735 0.502
## mili 0.586
## co 0.749
## Food 0.690 0.577
## Natural
## Rapid 0.943
## Water 0.668 0.720
##
## MR3 MR1 MR2
## SS loadings 1.965 1.780 1.255
## Proportion Var 0.281 0.254 0.179
## Cumulative Var 0.281 0.535 0.714
fa.diagram(resfa,main = "Resultados del EFA")
