Resultados Heuristicos
Laura Lucía Domínguez
2022-06-28
Resultados [19:34] ojito
\[ECM(Estimador)=\frac{n+2}{(n-1)*(n-2)}parámetro^2 \]
library(bootstrap)
library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)
library(haven)
library(ade4)##
## Attaching package: 'ade4'## The following object is masked from 'package:FactoMineR':
##
## reconstlibrary(xtable)
library(readr)
library(data.table)
library(bigmemory)
library(foreach)
library(doParallel)## Loading required package: iterators## Loading required package: parallellibrary(biglm)## Loading required package: DBIlibrary(Factoshiny)## Loading required package: shiny## Loading required package: FactoInvestigatelibrary(dplyr) #para filtrar##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readxl)
CaliyPalmira<- read_excel("C:/LAURA LUCIA/U/9/Tesis/MARZO/CaliyPalmira-TAINA.xlsx")
names(CaliyPalmira)## [1] "Total_act_sociales" "Total_lug_act_sociales" "conoce_enf"
## [4] "p26" "S_sintomas" "conoce_preven"
## [7] "S_prevención" "creenvirus" "contac_covid"
## [10] "dx_covid" "conf_presi" "conf_alcaldia"
## [13] "conf_gobern" "conf_mensgobierno" "p40"
## [16] "p42" "medios" "conf_mediocomu"
## [19] "p46" "p47" "p48"
## [22] "p49" "p50_1" "p50_2"
## [25] "p50_3" "p51" "p52"
## [28] "p53" "p54" "p55"
## [31] "p56" "p57" "p58"
## [34] "p60" "cumple_lavamanos" "cumple_tapaboca"
## [37] "cumple_distancia" "cumple_desinfecmano" "Total_tapaboca"
## [40] "Total_distancia" "ID" "Municipio"#CaliyPalmira$p41<-as.factor(CaliyPalmira$p41)
CaliyPalmira$creenvirus<-as.factor(CaliyPalmira$creenvirus)
CaliyPalmira$contac_covid<-as.factor(CaliyPalmira$contac_covid)
CaliyPalmira$dx_covid<-as.factor(CaliyPalmira$dx_covid)
#CaliyPalmira$mensaje<-as.factor(CaliyPalmira$mensaje)
#gubernamental
#CaliyPalmira$conf_presi<-as.factor(CaliyPalmira$conf_presi)
#CaliyPalmira$conf_alcaldia<-as.factor(CaliyPalmira$conf_alcaldia)
#CaliyPalmira$conf_gobern<-as.factor(CaliyPalmira$conf_gobern)
#CaliyPalmira$conf_mensgobierno<-as.factor(CaliyPalmira$conf_mensgobierno)
#summary(CaliyPalmira)
names(CaliyPalmira)<-c(
#1.voluntariedad
"x11",
"x12",
#2.conocimiento
"x21",
"x22", #p26
"x23",
"x24",
"x25", #p30
#3.incertidumbre
"x31", #p33
"x32", #p35
"x33", #p36
#4.gubernamental
"x41",
"x42",
"x43",
"x44",#"recomen_efectiva",
#5.salud
"x51",
#"p41", #factor
"x52",
#6.medios de comunicación
"x61", #total_medios_comu
"x62", #p43
#"mensaje", #categórica p72
#7.probabilidad de contagio
"x71",
"x72",
"x73",
"x74",
"x75",
"x76",
"x77",
#8.severidad
"x81",
"x82",
"x83",
"x84",
#9.susceptibilidad
"x91",
"x92",
"x93",
"x94",
#"p59_1", #factor
#"p59_2", #factor
#"p59_3", #factor
#"p59_4", #factor
"x95",
#10.cumplimiento
"x101", #p61
"x102",
"x103",
"x104",
"x105", #p76
"x106",
#otras
"Id",
"Ciudad"
)#recodificar la voluntariedad
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodesummary(CaliyPalmira$x11) #de 0 a 5## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.265 2.000 5.000CaliyPalmira$x11 <- recode(CaliyPalmira$x11,"5=0; 4=1; 3=2; 2=3; 1=4; 0=5")
summary(CaliyPalmira$x12) #de 0 a 8## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.633 2.000 8.000CaliyPalmira$x12 <- recode(CaliyPalmira$x12,"8=0; 7=1; 6=2; 5=3; 4=4; 3=5; 2=6; 1=7; 0=8")CALI Y PALMIRA
IPRG
# INDICE DE PERCEPCIÓN DE RIESGO GLOBAL
Datos=CaliyPalmira[,c(19:34)]
pesos=c(
#probabilidad de contagio
0.061,0.055,0.061,0.048,0.041,0.055,0.055,
#severidad
0.080,0.092,0.092,0.069,
#susceptibilidad
0.065,0.065,0.057,0.049,0.057
)
temp_K<-c()
lista_vacia <- vector("list", length = 1443)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(IHPRG1[ ,1:16]);IH## [1] 2.989 2.811 3.660 3.388 3.935 4.516 3.120 4.300 4.037 2.458 4.252 3.276
## [13] 3.867 4.218 4.008 3.363 2.897 3.650 4.681 3.647 4.657 2.602 3.219 3.121
## [25] 4.016 3.400 2.790 3.570 3.052 3.474 4.376 4.193 2.956 4.904 4.195 2.481
## [37] 3.439 5.158 4.389 3.782 5.093 2.690 3.661 3.864 3.252 1.962 2.883 3.365
## [49] 2.829 3.639 3.223 3.086 3.196 4.886 3.901 3.744 2.954 3.481 2.824 2.953
## [61] 5.384 3.204 2.119 2.686 3.815 3.376 4.443 3.350 5.557 5.340 3.774 2.824
## [73] 3.642 1.592 2.803 4.274 3.172 4.119 5.707 3.602 4.580 3.842 4.519 3.568
## [85] 2.928 4.831 2.803 3.471 4.745 3.421 3.000 3.589 3.471 4.108 3.211 4.774
## [97] 3.618 2.388 2.965 3.845 5.105 2.532 3.171 3.461 6.322 5.003 4.523 5.142
## [109] 4.027 4.097 4.838 4.785 4.427 4.973 3.260 3.296 3.128 4.467 3.173 4.531
## [121] 4.556 5.259 4.398 4.402 1.786 3.806 4.855 4.283 5.114 2.444 4.702 4.462
## [133] 4.632 2.256 4.072 2.644 3.106 5.422 3.929 2.621 5.467 3.615 4.198 3.896
## [145] 3.319 3.432 3.750 2.373 4.009 3.600 2.587 3.196 5.801 3.305 5.029 3.330
## [157] 4.304 3.421 3.574 5.658 3.699 5.760 3.637 3.524 4.352 5.805 3.986 3.089
## [169] 4.879 3.156 6.101 5.042 3.493 2.617 4.076 4.035 3.014 4.002 3.708 4.011
## [181] 4.466 3.386 6.158 3.176 4.845 3.954 3.324 3.516 4.219 3.537 4.142 4.246
## [193] 2.908 3.440 6.093 3.340 3.823 3.742 2.405 3.855 3.517 3.768 3.611 5.125
## [205] 3.917 4.673 3.700 4.304 3.401 4.731 5.757 3.029 4.503 3.975 3.926 4.132
## [217] 4.073 4.365 3.392 4.904 3.749 3.774 3.781 4.389 4.335 3.238 3.721 3.072
## [229] 3.063 3.627 4.688 4.117 3.917 3.987 3.846 1.982 1.550 3.572 2.935 2.254
## [241] 3.949 3.648 4.144 3.091 3.029 2.253 3.623 4.963 3.365 4.756 3.141 2.878
## [253] 4.212 3.849 4.125 3.077 3.897 2.392 4.861 4.012 4.756 3.626 3.718 2.563
## [265] 3.444 3.863 2.997 3.808 4.379 2.913 4.532 3.199 2.547 2.998 2.949 3.699
## [277] 4.150 4.662 4.910 4.190 3.667 4.468 4.722 3.541 2.992 3.670 4.858 3.112
## [289] 4.407 4.599 4.301 4.361 3.108 3.802 2.843 2.100 3.102 4.057 4.277 5.751
## [301] 3.052 3.987 2.395 3.500 4.879 4.004 3.231 3.669 4.405 4.862 3.937 5.265
## [313] 2.823 3.199 4.283 3.860 3.618 1.733 4.598 2.980 4.554 3.826 2.908 2.425
## [325] 3.625 4.819 4.640 3.403 3.880 2.992 4.254 3.793 4.893 5.360 4.119 3.159
## [337] 3.650 4.950 3.219 1.335 2.978 3.609 3.706 3.226 3.754 3.373 2.564 4.023
## [349] 4.304 4.929 4.834 3.513 3.294 4.259 4.089 2.910 5.610 3.373 3.192 4.884
## [361] 4.223 4.822 3.086 2.663 3.879 3.412 5.009 2.671 5.474 3.205 3.836 3.556
## [373] 5.153 3.787 4.960 3.948 5.019 2.284 3.103 4.226 3.519 5.340 3.219 4.535
## [385] 4.714 3.526 5.090 2.649 3.278 3.753 3.279 2.857 4.161 2.072 3.557 3.659
## [397] 3.016 3.965 3.188 3.244 3.911 3.330 2.261 3.417 3.435 2.083 2.730 3.207
## [409] 4.534 3.597 5.791 4.532 3.433 3.395 4.431 5.152 3.784 4.173 2.607 3.943
## [421] 3.772 2.469 4.771 4.880 5.227 4.528 5.376 4.223 3.648 4.041 4.408 5.633
## [433] 3.169 3.266 6.048 5.844 4.053 3.158 3.717 3.034 3.392 2.614 4.119 5.495
## [445] 3.105 3.438 4.755 4.443 4.652 4.772 3.557 5.621 5.645 4.379 3.609 4.275
## [457] 4.555 5.533 3.798 5.262 2.584 3.734 4.005 2.962 3.708 3.919 3.520 4.090
## [469] 6.064 4.379 6.322 4.041 3.320 5.495 3.635 3.942 5.632 4.391 3.547 3.796
## [481] 3.492 5.184 3.820 3.812 3.708 2.913 4.781 4.891 5.156 3.301 4.254 4.441
## [493] 2.982 3.576 4.891 4.173 4.203 4.391 3.619 4.718 2.201 5.572 4.171 2.906
## [505] 3.419 3.595 3.908 2.814 2.650 3.066 2.653 4.552 3.932 3.668 2.645 2.650
## [517] 5.032 3.764 3.376 3.335 3.149 4.833 3.059 3.085 4.446 4.501 4.831 2.072
## [529] 3.373 3.834 3.916 3.165 3.656 3.543 3.616 2.864 2.738 2.486 4.462 3.459
## [541] 3.032 3.648 5.207 4.011 5.342 4.585 4.045 4.341 4.325 3.323 3.973 4.810
## [553] 3.970 2.762 3.129 4.697 3.456 3.125 4.417 1.248 3.252 3.225 4.109 3.063
## [565] 4.245 2.772 4.308 2.858 3.558 3.284 3.778 5.214 3.969 4.110 2.211 4.696
## [577] 3.276 3.817 4.644 4.229 4.352 4.682 4.314 3.218 3.566 3.049 3.291 3.842
## [589] 2.060 3.759 3.741 3.510 3.741 4.218 3.666 4.360 3.764 2.765 4.290 4.220
## [601] 4.278 3.867 3.793 3.224 1.854 2.245 3.973 2.660 3.740 4.879 2.145 3.774
## [613] 4.127 4.894 4.250 4.577 2.591 5.688 4.566 2.844 5.299 4.771 1.902 4.246
## [625] 3.933 3.562 4.676 4.889 2.683 3.406 5.008 4.091 2.925 5.495 4.069 3.596
## [637] 3.265 5.501 2.621 2.352 0.937 3.308 4.089 3.880 2.623 3.363 3.750 4.091
## [649] 3.140 6.387 4.847 4.607 4.239 3.719 4.389 5.975 3.858 5.151 4.594 4.347
## [661] 4.955 2.804 5.796 3.907 5.353 4.526 3.017 4.727 2.742 4.108 6.202 4.491
## [673] 4.651 4.135 5.720 4.301 3.953 1.932 5.517 4.098 4.099 4.052 3.657 4.156
## [685] 4.511 2.597 2.604 4.414 2.555 4.245 3.363 4.388 3.530 3.866 4.783 4.064
## [697] 5.162 4.389 4.627 3.359 3.365 4.225 4.266 4.006 5.576 4.856 4.111 3.910
## [709] 2.456 3.805 3.993 5.275 2.828 2.864 3.812 3.557 2.616 4.532 3.696 4.168
## [721] 3.811 2.768 3.836 4.285 3.944 4.823 3.501 3.149 3.711 3.328 2.823 3.444
## [733] 2.771 4.395 3.920 2.945 2.835 3.698 4.249 3.414 4.395 4.626 4.810 4.914
## [745] 4.278 5.038 3.825 5.594 3.749 4.169 4.803 4.146 6.135 5.131 4.215 3.660
## [757] 5.149 4.481 3.802 4.068 3.648 3.831 2.911 3.542 3.793 3.169 3.436 3.181
## [769] 3.522 1.521 3.577 4.863 4.813 5.375 1.481 3.865 3.383 3.746 4.172 5.135
## [781] 5.378 4.520 3.831 3.880 5.728 3.293 4.442 4.502 2.431 5.079 3.761 4.163
## [793] 4.127 4.586 3.231 4.940 3.212 3.946 2.222 3.190 3.796 4.580 2.980 3.297
## [805] 3.665 4.124 5.202 5.996 3.151 3.046 3.123 3.954 4.953 4.734 4.351 4.158
## [817] 4.785 4.613 4.059 3.568 2.531 3.879 2.486 2.797 3.603 4.378 3.614 3.803
## [829] 4.810 4.209 2.713 3.945 4.710 4.260 5.266 5.205 3.030 5.753 3.617 3.861
## [841] 3.976 3.154 5.173 4.024 4.003 4.386 3.689 3.696 4.144 5.622 4.768 3.983
## [853] 2.643 5.329 5.073 4.076 3.083 3.576 4.430 2.992 4.851 2.426 3.510 5.247
## [865] 5.065 2.570 3.524 5.512 3.576 2.728 4.503 4.382 3.429 3.382 5.679 6.070
## [877] 3.259 4.931 1.539 4.196 4.297 5.221 3.631 2.772 3.581 4.140 4.360 5.603
## [889] 4.443 3.130 2.695 2.797 4.132 5.089 4.190 2.911 3.893 1.842 3.400 4.416
## [901] 3.998 2.746 3.189 2.996 4.846 5.292 3.164 5.356 2.484 3.838 4.037 3.668
## [913] 2.666 4.271 4.433 5.399 4.111 4.759 4.095 4.458 4.111 4.528 3.084 4.234
## [925] 4.141 4.754 3.786 4.209 1.588 5.047 4.216 3.335 3.878 4.688 3.622 2.386
## [937] 5.020 3.913 2.696 5.213 3.156 3.055 5.992 4.663 6.342 4.072 3.904 2.921
## [949] 3.363 4.684 5.105 5.131 5.619 4.224 4.378 4.585 2.433 4.633 2.621 4.653
## [961] 5.255 3.716 3.128 2.333 2.958 2.782 4.328 4.413 3.634 4.854 5.255 3.953
## [973] 4.217 4.867 5.730 2.900 5.608 4.587 4.800 3.791 4.378 2.682 3.428 2.423
## [985] 3.072 3.239 4.078 3.974 5.238 4.753 5.635 4.556 3.249 3.362 3.598 2.856
## [997] 4.371 5.548 4.400 4.350 2.781 2.929 4.577 5.868 5.030 4.184 4.820 3.904
## [1009] 3.624 4.206 3.543 4.142 3.252 5.401 5.828 3.965 1.754 2.075 5.170 6.108
## [1021] 3.240 4.630 4.550 4.891 4.513 3.540 3.238 5.640 4.875 3.360 4.186 5.334
## [1033] 5.168 3.308 4.752 4.063 2.471 3.248 3.718 2.832 3.269 5.298 4.343 5.378
## [1045] 5.232 5.243 4.756 3.470 2.787 4.797 3.800 3.559 4.366 3.745 5.408 3.880
## [1057] 5.242 3.830 3.719 4.187 4.068 3.110 4.075 3.149 5.914 3.401 5.849 6.322
## [1069] 4.330 4.329 4.853 3.553 2.800 3.240 4.642 3.316 3.865 5.108 3.436 3.193
## [1081] 5.050 3.973 3.759 4.127 6.241 5.105 4.641 4.324 3.181 3.097 4.117 4.492
## [1093] 5.170 3.019 4.809 3.367 3.474 3.385 3.443 5.078 2.598 3.372 5.932 3.370
## [1105] 3.812 3.375 3.938 4.958 3.887 5.573 3.890 3.679 3.534 3.498 4.898 3.792
## [1117] 5.408 2.826 2.821 3.556 3.788 4.477 3.400 6.086 4.438 6.090 3.576 4.086
## [1129] 3.675 4.116 3.177 4.039 3.701 4.320 5.548 4.261 4.688 5.574 4.485 4.493
## [1141] 1.290 2.494 2.973 3.912 4.265 5.197 2.956 4.588 3.848 5.558 3.438 3.726
## [1153] 3.862 4.857 4.326 1.084 3.399 4.416 3.706 3.124 3.639 5.469 4.232 4.397
## [1165] 1.843 2.904 3.930 5.864 5.376 3.350 3.278 3.015 4.286 3.549 3.571 2.651
## [1177] 4.169 4.552 4.277 3.605 2.878 3.773 2.908 2.923 2.698 2.449 5.857 3.115
## [1189] 4.572 3.583 5.524 3.936 0.937 3.370 3.201 4.254 3.889 3.716 3.935 4.152
## [1201] 4.842 4.362 3.366 4.942 6.322 2.941 3.547 4.944 5.111 3.112 4.633 3.087
## [1213] 4.591 3.521 5.863 3.378 5.240 5.149 3.222 4.786 2.656 3.382 3.262 5.545
## [1225] 4.794 4.686 4.607 3.930 4.312 4.551 3.818 5.143 4.131 5.036 3.783 3.961
## [1237] 4.147 4.006 3.216 4.311 4.338 5.102 4.549 4.638 3.269 4.908 4.650 5.246
## [1249] 4.196 4.390 5.254 4.200 5.600 3.869 5.581 4.185 2.659 4.344 4.480 3.765
## [1261] 3.036 5.254 5.896 5.623 4.915 5.173 2.990 3.278 3.737 4.101 2.177 3.564
## [1273] 3.481 3.995 3.487 4.302 4.680 2.744 4.626 2.657 3.334 3.445 4.674 5.465
## [1285] 3.000 5.162 4.168 3.044 5.752 4.548 5.582 5.033 3.968 5.232 3.183 4.579
## [1297] 4.832 4.233 2.811 4.621 4.157 2.883 3.345 3.549 3.782 4.964 5.557 4.173
## [1309] 4.725 4.488 5.090 4.041 4.439 3.680 3.988 4.770 3.875 5.043 3.004 2.541
## [1321] 4.743 2.044 4.961 4.421 5.022 4.059 3.512 5.980 5.149 4.895 4.966 3.314
## [1333] 4.732 6.176 5.061 5.210 4.336 4.594 3.507 5.514 3.694 3.466 3.997 4.809
## [1345] 4.334 2.590 5.090 4.124 5.057 2.058 5.192 3.886 3.409 3.648 3.165 2.539
## [1357] 6.322 5.002 2.778 4.064 6.127 3.302 5.102 3.748 4.008 2.902 3.350 5.407
## [1369] 4.467 4.410 2.785 3.434 4.246 3.732 3.068 3.542 4.117 4.005 3.970 3.282
## [1381] 4.817 4.876 4.959 5.401 2.901 4.257 4.569 1.938 4.038 3.199 3.917 3.895
## [1393] 4.094 4.261 3.686 3.381 4.619 3.678 4.722 3.227 5.344 3.171 1.898 3.801
## [1405] 3.112 4.903 3.601 3.935 1.102 4.283 4.277 3.847 4.685 4.572 2.716 4.763
## [1417] 6.322 4.302 4.374 3.544 4.945 4.272 4.002 3.669 3.815 4.748 5.130 6.387
## [1429] 4.589 3.975 4.926 3.950 5.923 3.925 4.074 4.972 4.208 2.891 3.502 4.306
## [1441] 4.088 4.574 3.693#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans3=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 43.79 54.97 55.55 66.98 100.00round(sd(IH_trans3),2)## [1] 17.07ECM
#ERROR DE ESTIMACIÓN (EE)
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=CaliyPalmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:16])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana=mean(NfinalMedia);I_mediana## [1] 54.94283#I_ref=57.01
#EE=I_ref-round(I_mediana,2);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana)^2,2);EE## [1] 0Índices por grupos
Datos=CaliyPalmira[,c(19:34)]
pesos=c(
#voluntariedad
#0.50,0.50,
#conocimiento
#0.171,0.220,0.171,0.195,0.244,
#incertidumbre
#0.032,0.024,0.024,
#confianza gubernamental
#0.241,0.276,0.241,0.241,
#confianza sector salud
#0.438,0.563,
#confianza medios
#0.467,0.533,
#probabilidad de contagio
0.164,0.145,0.164,0.127,0.109,0.145,0.145,
#severidad
0.241,0.276,0.276,0.207,
#susceptibilidad
0.222,0.222,0.194,0.167,0.194
#,
#cumplimiento
#0.190,0.190,0.190,0.143,0.143,0.143
)PROBABILIDAD DE CONTAGIO
temp_K<-c()
lista_vacia <- vector("list", length = 1443)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};ProbabilidadContagio=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(ProbabilidadContagio[ ,1:7])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans63=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans63)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 49.98 62.57 62.52 75.69 100.00round(sd(IH_trans63),2)## [1] 18.68#x11();hist(IH_trans6,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=CaliyPalmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:7])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
I_mediana63=median(NfinalMedia);
#I_ref=62.61
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana63)^2,2);EE## [1] 0SEVERIDAD
temp_K<-c()
lista_vacia <- vector("list", length = 1443)
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};severidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(severidad[ ,1:4]) #;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans73=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans73)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 40.80 54.02 55.12 70.41 100.00round(sd(IH_trans73),2)## [1] 22.08#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=CaliyPalmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:4])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
I_mediana73=median(NfinalMedia);
#I_ref=54.02
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana73)^2,2);EE## [1] 23.97SUSCEPTIBILIDAD
temp_K<-c()
lista_vacia <- vector("list", length = 1443)
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};susceptibilidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(susceptibilidad[ ,1:5])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans83=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans83)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 22.31 33.46 34.43 45.41 100.00round(sd(IH_trans83),2)## [1] 17.35#hist(IH_trans8,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))ecm
#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=CaliyPalmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:5])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
hist(NfinalMedia)
I_mediana83=median(NfinalMedia);
#I_ref=33.44
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana83)^2,2);EE## [1] 0.11CALI
CaliyPalmira$Ciudad<-as.factor(CaliyPalmira$Ciudad)
Cali<-filter(CaliyPalmira, CaliyPalmira$Ciudad=="Cali")IPRG
# INDICE DE PERCEPCIÓN DE RIESGO GLOBAL
Datos=Cali[,c(19:34)]
pesos=c(
#probabilidad de contagio
0.061,0.055,0.061,0.048,0.041,0.055,0.055,
#severidad
0.080,0.092,0.092,0.069,
#susceptibilidad
0.065,0.065,0.057,0.049,0.057
)
temp_K<-c()
lista_vacia <- vector("list", length = 797)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(IHPRG1[ ,1:16]);IH## [1] 2.989 2.811 3.660 3.388 3.935 4.516 3.120 4.300 4.037 2.458 4.252 3.276
## [13] 3.867 4.218 4.008 3.363 2.897 3.650 4.681 3.647 4.657 2.602 3.219 3.121
## [25] 4.016 3.400 2.790 3.570 3.052 3.474 4.376 4.193 2.956 4.904 4.195 2.481
## [37] 3.439 5.158 4.389 3.782 5.093 2.690 3.661 3.864 3.252 1.962 2.883 3.365
## [49] 2.829 3.639 3.223 3.086 3.196 4.886 3.901 3.744 2.954 3.481 2.824 2.953
## [61] 5.384 3.204 2.119 2.686 3.815 3.376 4.443 3.350 5.557 5.340 3.774 2.824
## [73] 3.642 1.592 2.803 4.274 3.172 4.119 5.707 3.602 4.580 3.842 4.519 3.568
## [85] 2.928 4.831 2.803 3.471 4.745 3.421 3.000 3.589 3.471 4.108 3.211 4.774
## [97] 3.618 2.388 2.965 3.845 5.105 2.532 3.171 3.461 6.322 5.003 4.523 5.142
## [109] 4.027 4.097 4.838 4.785 4.427 4.973 3.260 3.296 3.128 4.467 3.173 4.531
## [121] 4.556 5.259 4.398 4.402 1.786 3.806 4.855 4.283 5.114 2.444 4.702 4.462
## [133] 4.632 2.256 4.072 2.644 3.106 5.422 3.929 2.621 5.467 3.615 4.198 3.896
## [145] 3.319 3.432 3.750 2.373 4.009 3.600 2.587 3.196 5.801 3.305 5.029 3.330
## [157] 4.304 3.421 3.574 5.658 3.699 5.760 3.637 3.524 4.352 5.805 3.986 3.089
## [169] 4.879 3.156 6.101 5.042 3.493 2.617 4.076 4.035 3.014 4.002 3.708 4.011
## [181] 4.466 3.386 6.158 3.176 4.845 3.954 3.324 3.516 4.219 3.537 4.142 4.246
## [193] 2.908 3.440 6.093 3.340 3.823 3.742 2.405 3.855 3.517 3.768 3.611 5.125
## [205] 3.917 4.673 3.700 4.304 3.401 4.731 5.757 3.029 4.503 3.975 3.926 4.132
## [217] 4.073 4.365 3.392 4.904 3.749 3.774 3.781 4.389 4.335 3.238 3.721 3.072
## [229] 3.063 3.627 4.688 4.117 3.917 3.987 3.846 1.982 1.550 3.572 2.935 2.254
## [241] 3.949 3.648 4.144 3.091 3.029 2.253 3.623 4.963 3.365 4.756 3.141 2.878
## [253] 4.212 3.849 4.125 3.077 3.897 2.392 4.861 4.012 4.756 3.626 3.718 2.563
## [265] 3.444 3.863 2.997 3.808 4.379 2.913 4.532 3.199 2.547 2.998 2.949 3.699
## [277] 4.150 4.662 4.910 4.190 3.667 4.468 4.722 3.541 2.992 3.670 4.858 3.112
## [289] 4.407 4.599 4.301 4.361 3.108 3.802 2.843 2.100 3.102 4.057 4.277 5.751
## [301] 3.052 3.987 2.395 3.500 4.879 4.004 3.231 3.669 4.405 4.862 3.937 5.265
## [313] 2.823 3.199 4.283 3.860 3.618 1.733 4.598 2.980 4.554 3.826 2.908 2.425
## [325] 3.625 4.819 4.640 3.403 3.880 2.992 4.254 3.793 4.893 5.360 4.119 3.159
## [337] 3.650 4.950 3.219 1.335 2.978 3.609 3.706 3.226 3.754 3.373 2.564 4.023
## [349] 4.304 4.929 4.834 3.513 3.294 4.259 4.089 2.910 5.610 3.373 3.192 4.884
## [361] 4.223 4.822 3.086 2.663 3.879 3.412 5.009 2.671 5.474 3.205 3.836 3.556
## [373] 5.153 3.787 4.960 3.948 5.019 2.284 3.103 4.226 3.519 5.340 3.219 4.535
## [385] 4.714 3.526 5.090 2.649 3.278 3.753 3.279 2.857 4.161 2.072 3.557 3.659
## [397] 3.016 3.965 3.188 3.244 3.911 3.330 2.261 3.417 3.435 2.083 2.730 3.207
## [409] 4.534 3.597 5.791 4.532 3.433 3.395 4.431 5.152 3.784 4.173 2.607 3.943
## [421] 3.772 2.469 4.771 4.880 5.227 4.528 5.376 4.223 3.648 4.041 4.408 5.633
## [433] 3.169 3.266 6.048 5.844 4.053 3.158 3.717 3.034 3.392 2.614 4.119 5.495
## [445] 3.105 3.438 4.755 4.443 4.652 4.772 3.557 5.621 5.645 4.379 3.609 4.275
## [457] 4.555 5.533 3.798 5.262 2.584 3.734 4.005 2.962 3.708 3.919 3.520 4.090
## [469] 6.064 4.379 6.322 4.041 3.320 5.495 3.635 3.942 5.632 4.391 3.547 3.796
## [481] 3.492 5.184 3.820 3.812 3.708 2.913 4.781 4.891 5.156 3.301 4.254 4.441
## [493] 2.982 3.576 4.891 4.173 4.203 4.391 3.619 4.718 2.201 5.572 4.171 2.906
## [505] 3.419 3.595 3.908 2.814 2.650 3.066 2.653 4.552 3.932 3.668 2.645 2.650
## [517] 5.032 3.764 3.376 3.335 3.149 4.833 3.059 3.085 4.446 4.501 4.831 2.072
## [529] 3.373 3.834 3.916 3.165 3.656 3.543 3.616 2.864 2.738 2.486 4.462 3.459
## [541] 3.032 3.648 5.207 4.011 5.342 4.585 4.045 4.341 4.325 3.323 3.973 4.810
## [553] 3.970 2.762 3.129 4.697 3.456 3.125 4.417 1.248 3.252 3.225 4.109 3.063
## [565] 4.245 2.772 4.308 2.858 3.558 3.284 3.778 5.214 3.969 4.110 2.211 4.696
## [577] 3.276 3.817 4.644 4.229 4.352 4.682 4.314 3.218 3.566 3.049 3.291 3.842
## [589] 2.060 3.759 3.741 3.510 3.741 4.218 3.666 4.360 3.764 2.765 4.290 4.220
## [601] 4.278 3.867 3.793 3.224 1.854 2.245 3.973 2.660 3.740 4.879 2.145 3.774
## [613] 4.127 4.894 4.250 4.577 2.591 5.688 4.566 2.844 5.299 4.771 1.902 4.246
## [625] 3.933 3.562 4.676 4.889 2.683 3.406 5.008 4.091 2.925 5.495 4.069 3.596
## [637] 3.265 5.501 2.621 2.352 0.937 3.308 4.089 3.880 2.623 3.363 3.750 4.091
## [649] 3.140 6.387 4.847 4.607 4.239 3.719 4.389 5.975 3.858 5.151 4.594 4.347
## [661] 4.955 2.804 5.796 3.907 5.353 4.526 3.017 4.727 2.742 4.108 6.202 4.491
## [673] 4.651 4.135 5.720 4.301 3.953 1.932 5.517 4.098 4.099 4.052 3.657 4.156
## [685] 4.511 2.597 2.604 4.414 2.555 4.245 3.363 4.388 3.530 3.866 4.783 4.064
## [697] 5.162 4.389 4.627 3.359 3.365 4.225 4.266 4.006 5.576 4.856 4.111 3.910
## [709] 2.456 3.805 3.993 5.275 2.828 2.864 3.812 3.557 2.616 4.532 3.696 4.168
## [721] 3.811 2.768 3.836 4.285 3.944 4.823 3.501 3.149 3.711 3.328 2.823 3.444
## [733] 2.771 4.395 3.920 2.945 2.835 3.698 4.249 3.414 4.395 4.626 4.810 4.914
## [745] 4.278 5.038 3.825 5.594 3.749 4.169 4.803 4.146 6.135 5.131 4.215 3.660
## [757] 5.149 4.481 3.802 4.068 3.648 3.831 2.911 3.542 3.793 3.169 3.436 3.181
## [769] 3.522 1.521 3.577 4.863 4.813 5.375 1.481 3.865 3.383 3.746 4.172 5.135
## [781] 5.378 4.520 3.831 3.880 5.728 3.293 4.442 4.502 2.431 5.079 3.761 4.163
## [793] 4.127 4.586 3.231 4.940 3.212#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans1=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 42.48 52.90 53.75 64.29 100.00round(sd(IH_trans1),2)## [1] 16.43#hist(IH_trans,col="azure2",xlab="IPRG",main = "Distribución de IPRG",freq = FALSE,ylim = c(0,0.035))ECM
#ERROR DE ESTIMACIÓN (EE)
n=round(797*0.80,0);n## [1] 638NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Cali[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:16])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
hist(NfinalMedia)
I_mediana1=median(NfinalMedia);I_mediana1## [1] 52.89908#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana1)+(I_mediana1-I_mediana)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana)^2+(I_mediana1-I_mediana)^2,2);EE## [1] 4009.58grupos
#Datos=Cali[,c(19:34)]
#falta activar incertidumbre
pesos=c(
#voluntariedad
0.50,0.50,
#conocimiento
0.171,0.220,0.171,0.195,0.244,
#incertidumbre
#0.032,0.024,0.024,
#confianza gubernamental
0.241,0.276,0.241,0.241,
#confianza sector salud
0.438,0.563,
#confianza medios
0.467,0.533,
#probabilidad de contagio
0.164,0.145,0.164,0.127,0.109,0.145,0.145,
#severidad
0.241,0.276,0.276,0.207,
#susceptibilidad
0.222,0.222,0.194,0.167,0.194,
#cumplimiento
0.190,0.190,0.190,0.143,0.143,0.143)PROBABILIDAD DE CONTAGIO
#ProbabilidadContagio=Datos[,c(16:22)]*pesos[c(16:22)];ProbabilidadContagio
temp_K<-c()
lista_vacia <- vector("list", length = 797)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};ProbabilidadContagio=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(ProbabilidadContagio[ ,1:7]);IH## [1] 7.504 6.528 7.735 7.443 7.089 7.760 7.163 10.322 11.932 4.882
## [11] 6.052 8.393 7.418 6.881 10.517 5.393 7.882 8.138 9.102 11.408
## [21] 10.517 5.137 7.504 6.834 9.907 5.712 10.883 9.358 7.590 8.736
## [31] 9.395 9.187 6.894 10.492 6.978 4.930 7.614 10.737 8.090 9.663
## [41] 10.897 5.467 5.357 6.174 6.077 5.210 7.748 7.175 6.821 7.858
## [51] 7.114 7.260 9.469 11.396 7.138 11.432 5.979 9.126 7.566 8.199
## [61] 10.371 7.102 3.686 7.528 9.029 7.298 8.322 7.931 10.213 10.445
## [71] 7.760 8.846 7.858 4.002 8.248 7.711 7.931 8.431 10.712 6.614
## [81] 9.126 9.968 9.297 9.430 5.503 7.809 7.736 10.468 11.615 10.322
## [91] 4.808 6.065 6.638 7.528 7.614 10.786 8.833 5.808 5.552 11.932
## [101] 9.370 7.199 7.589 9.200 12.787 10.737 4.687 9.249 7.004 7.833
## [111] 8.590 9.615 10.737 10.005 6.589 6.247 10.273 11.371 4.638 10.517
## [121] 5.858 10.884 8.322 8.334 4.150 5.907 9.761 9.273 11.567 4.881
## [131] 10.102 11.322 12.103 5.784 7.809 5.723 7.199 12.787 12.103 7.662
## [141] 9.761 7.772 5.748 8.052 10.347 9.151 6.760 9.761 8.884 7.724
## [151] 7.150 8.761 11.103 7.309 7.566 7.174 8.175 7.370 7.125 12.127
## [161] 8.260 10.919 6.321 8.260 8.553 12.103 8.590 6.174 11.908 7.467
## [171] 11.103 10.225 8.406 5.332 10.700 10.908 9.761 8.077 7.565 7.687
## [181] 8.638 6.076 12.103 5.918 10.005 7.052 5.077 7.089 7.540 5.663
## [191] 11.103 10.493 11.811 4.870 12.103 6.150 9.638 3.345 6.552 8.711
## [201] 11.761 9.785 9.821 9.968 7.029 9.370 10.932 5.468 7.369 8.175
## [211] 11.103 5.797 8.431 9.053 8.724 9.346 8.541 7.577 8.761 11.261
## [221] 7.333 6.980 7.931 8.931 9.468 11.103 7.980 7.760 8.530 7.516
## [231] 5.223 6.869 7.127 7.199 8.760 3.942 3.478 6.564 7.613 6.248
## [241] 9.358 8.773 8.920 7.345 7.052 4.577 6.979 12.103 8.249 8.151
## [251] 10.055 5.955 7.760 5.698 6.833 5.540 7.809 8.223 10.005 9.151
## [261] 10.517 7.882 7.125 5.321 5.613 8.882 7.320 9.785 7.589 6.467
## [271] 7.956 6.601 7.345 7.247 8.103 8.516 12.103 10.712 10.103 11.323
## [281] 8.833 9.639 10.932 7.297 6.382 8.479 10.493 10.639 8.151 8.273
## [291] 8.175 7.418 6.589 6.126 7.063 4.442 6.296 9.383 9.675 12.103
## [301] 5.028 8.273 8.346 10.688 12.103 9.968 6.101 7.784 9.040 10.713
## [311] 9.115 10.541 7.711 8.638 11.006 6.614 9.126 3.417 8.322 8.626
## [321] 7.869 7.516 6.589 5.052 7.540 7.517 11.883 7.566 7.955 5.186
## [331] 8.151 7.882 10.726 10.371 6.028 7.065 8.004 8.809 7.979 2.978
## [341] 7.297 9.492 8.321 7.809 7.516 9.614 9.443 8.846 10.273 12.787
## [351] 10.250 7.394 9.651 11.323 8.992 7.931 11.274 11.603 8.041 8.591
## [361] 7.711 8.980 5.759 7.333 8.590 7.748 11.432 9.346 12.103 7.467
## [371] 7.833 11.761 10.542 7.126 6.784 8.285 12.103 6.454 9.615 7.650
## [381] 7.004 7.674 7.760 8.785 10.213 7.639 10.248 7.431 7.431 7.809
## [391] 7.638 6.101 9.627 5.650 10.615 8.969 6.625 10.493 7.211 7.980
## [401] 8.187 7.005 5.565 9.358 7.089 4.591 6.954 9.273 10.895 7.394
## [411] 12.103 7.773 6.175 4.053 9.419 10.079 6.077 9.419 5.127 6.699
## [421] 6.249 4.493 10.249 8.870 8.419 8.664 12.787 9.224 6.517 8.420
## [431] 9.187 11.177 5.637 6.711 11.713 12.103 7.442 9.369 9.649 6.907
## [441] 7.382 5.088 8.273 9.371 7.712 6.687 6.150 8.688 9.420 7.969
## [451] 10.250 11.421 9.311 12.787 6.589 8.517 8.469 11.201 8.297 12.067
## [461] 8.139 7.931 9.810 6.748 7.395 8.956 9.346 9.371 12.567 11.762
## [471] 12.787 9.456 9.115 10.639 10.079 12.787 8.884 8.748 11.396 10.933
## [481] 8.846 10.250 8.724 11.005 8.248 10.444 8.273 9.944 10.859 8.114
## [491] 8.445 11.396 9.445 11.676 12.287 8.980 7.347 8.883 8.982 10.359
## [501] 5.357 11.859 7.273 10.347 8.615 7.918 8.968 6.956 6.613 8.797
## [511] 6.687 8.395 8.957 6.711 5.492 6.028 10.615 11.652 7.102 9.102
## [521] 9.200 7.882 7.883 6.322 8.883 12.616 12.152 3.124 10.787 8.969
## [531] 8.834 7.822 8.249 8.200 8.493 6.931 6.638 6.272 10.419 7.736
## [541] 9.542 8.468 7.882 9.482 10.725 10.054 8.615 8.249 10.248 7.309
## [551] 9.395 12.787 8.273 5.553 6.882 7.870 10.688 7.553 11.128 3.124
## [561] 6.382 8.566 12.787 9.542 7.249 7.127 9.395 8.310 6.920 10.835
## [571] 10.359 10.872 3.661 8.419 4.357 10.677 8.565 9.895 9.201 9.663
## [581] 10.115 11.201 8.200 9.054 8.615 8.151 10.859 9.640 6.662 7.688
## [591] 8.493 11.225 8.469 9.810 6.930 9.298 8.127 7.187 9.810 8.578
## [601] 11.750 9.603 9.335 8.614 3.491 8.077 10.835 8.371 9.810 7.273
## [611] 7.906 10.139 11.055 9.274 11.055 7.480 6.075 9.396 10.432 9.346
## [621] 6.857 9.116 4.272 11.055 12.787 6.541 10.664 9.273 7.407 6.467
## [631] 11.372 8.492 6.919 12.787 8.419 9.213 9.274 12.787 7.920 5.663
## [641] 2.001 9.932 9.371 8.736 5.661 6.272 7.078 10.957 10.957 12.787
## [651] 9.164 8.664 10.701 9.078 7.176 11.274 9.859 9.616 10.005 12.787
## [661] 10.225 9.566 12.007 9.395 11.006 10.030 6.676 12.616 7.358 9.444
## [671] 12.592 5.176 10.737 12.787 12.787 8.469 8.444 5.247 9.615 7.223
## [681] 8.152 9.140 8.786 7.419 10.750 4.917 8.530 9.639 8.786 10.200
## [691] 8.884 10.787 8.297 8.664 9.615 8.993 9.786 6.565 9.371 8.688
## [701] 5.126 11.226 7.028 9.225 9.956 7.724 12.445 10.250 6.614 9.224
## [711] 11.592 11.616 5.981 8.321 11.201 4.441 8.725 9.737 8.224 9.224
## [721] 8.102 6.603 9.725 10.933 8.395 10.164 9.530 5.576 12.787 8.382
## [731] 6.492 7.992 4.638 8.394 8.224 7.602 7.347 9.347 9.175 8.736
## [741] 10.030 10.371 7.797 9.615 9.018 10.737 8.835 9.687 10.201 9.420
## [751] 10.957 8.469 12.787 9.249 8.395 9.663 9.273 10.030 7.713 9.420
## [761] 7.687 8.895 9.566 9.395 6.589 7.359 7.394 6.443 6.883 4.370
## [771] 6.639 8.273 9.810 11.250 3.661 10.968 8.871 12.787 11.567 12.116
## [781] 10.445 10.359 10.201 9.420 10.787 10.457 11.225 8.554 5.712 10.530
## [791] 4.821 10.445 8.224 8.395 7.882 10.555 7.881#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans61=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans61)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 49.10 60.53 61.01 74.21 100.00round(sd(IH_trans61),2)## [1] 18.75#hist(IH_trans6,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
#Datos=Cali[,-c(8:10,41,42)]
Datos=Cali[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:7])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
}#; NfinalMedia
hist(NfinalMedia)
I_mediana61=median(NfinalMedia);
I_ref=61.22
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana61)+(I_mediana61-I_mediana63)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana63)^2+(I_mediana61-I_mediana63)^2,2);EE## [1] 4221.07SEVERIDAD
#--------------------------------severidad-----------------------------------
#severidad=Datos[,c(23:26)]*pesos[c(23:26)];severidad
temp_K<-c()
lista_vacia <- vector("list", length = 797)
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};severidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(severidad[ ,1:4]) #;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans71=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans71)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.35 50.58 52.76 66.67 100.00round(sd(IH_trans71),2)## [1] 22.01#hist(IH_trans7,col="azure2",xlab="IPSE",main = "Distribución de IPSE",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
#Datos=Cali[,-c(8:10,41,42)]
Datos=Cali[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:4])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
hist(NfinalMedia)
I_mediana71=median(NfinalMedia);
#I_ref=50.60
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana71)+(I_mediana71-I_mediana73)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana73)^2+(I_mediana71-I_mediana73)^2,2);EE## [1] 8784.49SUSCEPTIBILIDAD
#--------------------------------susceptibilidad-----------------------------------
#susceptibilidad=Datos[,c(27:31)]*pesos[c(27:31)];susceptibilidad
temp_K<-c()
lista_vacia <- vector("list", length = 797)
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};susceptibilidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(susceptibilidad[ ,1:5])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans81=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans81)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 17.26 29.06 30.93 42.53 100.00round(sd(IH_trans81),2)## [1] 17.11#hist(IH_trans8,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Cali[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:5])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
hist(NfinalMedia)
I_mediana81=median(NfinalMedia);
#I_ref=31.86
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana81)+(I_mediana81-I_mediana83)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana83)^2+(I_mediana81-I_mediana83)^2,2);EE## [1] 20039.18PALMIRA
Palmira<-filter(CaliyPalmira, CaliyPalmira$Ciudad=="Palmira")IPRG
# INDICE DE PERCEPCIÓN DE RIESGO GLOBAL
Datos=Palmira[,c(19:34)]
pesos=c(
#probabilidad de contagio
0.061,0.055,0.061,0.048,0.041,0.055,0.055,
#severidad
0.080,0.092,0.092,0.069,
#susceptibilidad
0.065,0.065,0.057,0.049,0.057
)
temp_K<-c()
lista_vacia <- vector("list", length = 646)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(IHPRG1[ ,1:16]);IH## [1] 3.946 2.222 3.190 3.796 4.580 2.980 3.297 3.665 4.124 5.202 5.996 3.151
## [13] 3.046 3.123 3.954 4.953 4.734 4.351 4.158 4.785 4.613 4.059 3.568 2.531
## [25] 3.879 2.486 2.797 3.603 4.378 3.614 3.803 4.810 4.209 2.713 3.945 4.710
## [37] 4.260 5.266 5.205 3.030 5.753 3.617 3.861 3.976 3.154 5.173 4.024 4.003
## [49] 4.386 3.689 3.696 4.144 5.622 4.768 3.983 2.643 5.329 5.073 4.076 3.083
## [61] 3.576 4.430 2.992 4.851 2.426 3.510 5.247 5.065 2.570 3.524 5.512 3.576
## [73] 2.728 4.503 4.382 3.429 3.382 5.679 6.070 3.259 4.931 1.539 4.196 4.297
## [85] 5.221 3.631 2.772 3.581 4.140 4.360 5.603 4.443 3.130 2.695 2.797 4.132
## [97] 5.089 4.190 2.911 3.893 1.842 3.400 4.416 3.998 2.746 3.189 2.996 4.846
## [109] 5.292 3.164 5.356 2.484 3.838 4.037 3.668 2.666 4.271 4.433 5.399 4.111
## [121] 4.759 4.095 4.458 4.111 4.528 3.084 4.234 4.141 4.754 3.786 4.209 1.588
## [133] 5.047 4.216 3.335 3.878 4.688 3.622 2.386 5.020 3.913 2.696 5.213 3.156
## [145] 3.055 5.992 4.663 6.342 4.072 3.904 2.921 3.363 4.684 5.105 5.131 5.619
## [157] 4.224 4.378 4.585 2.433 4.633 2.621 4.653 5.255 3.716 3.128 2.333 2.958
## [169] 2.782 4.328 4.413 3.634 4.854 5.255 3.953 4.217 4.867 5.730 2.900 5.608
## [181] 4.587 4.800 3.791 4.378 2.682 3.428 2.423 3.072 3.239 4.078 3.974 5.238
## [193] 4.753 5.635 4.556 3.249 3.362 3.598 2.856 4.371 5.548 4.400 4.350 2.781
## [205] 2.929 4.577 5.868 5.030 4.184 4.820 3.904 3.624 4.206 3.543 4.142 3.252
## [217] 5.401 5.828 3.965 1.754 2.075 5.170 6.108 3.240 4.630 4.550 4.891 4.513
## [229] 3.540 3.238 5.640 4.875 3.360 4.186 5.334 5.168 3.308 4.752 4.063 2.471
## [241] 3.248 3.718 2.832 3.269 5.298 4.343 5.378 5.232 5.243 4.756 3.470 2.787
## [253] 4.797 3.800 3.559 4.366 3.745 5.408 3.880 5.242 3.830 3.719 4.187 4.068
## [265] 3.110 4.075 3.149 5.914 3.401 5.849 6.322 4.330 4.329 4.853 3.553 2.800
## [277] 3.240 4.642 3.316 3.865 5.108 3.436 3.193 5.050 3.973 3.759 4.127 6.241
## [289] 5.105 4.641 4.324 3.181 3.097 4.117 4.492 5.170 3.019 4.809 3.367 3.474
## [301] 3.385 3.443 5.078 2.598 3.372 5.932 3.370 3.812 3.375 3.938 4.958 3.887
## [313] 5.573 3.890 3.679 3.534 3.498 4.898 3.792 5.408 2.826 2.821 3.556 3.788
## [325] 4.477 3.400 6.086 4.438 6.090 3.576 4.086 3.675 4.116 3.177 4.039 3.701
## [337] 4.320 5.548 4.261 4.688 5.574 4.485 4.493 1.290 2.494 2.973 3.912 4.265
## [349] 5.197 2.956 4.588 3.848 5.558 3.438 3.726 3.862 4.857 4.326 1.084 3.399
## [361] 4.416 3.706 3.124 3.639 5.469 4.232 4.397 1.843 2.904 3.930 5.864 5.376
## [373] 3.350 3.278 3.015 4.286 3.549 3.571 2.651 4.169 4.552 4.277 3.605 2.878
## [385] 3.773 2.908 2.923 2.698 2.449 5.857 3.115 4.572 3.583 5.524 3.936 0.937
## [397] 3.370 3.201 4.254 3.889 3.716 3.935 4.152 4.842 4.362 3.366 4.942 6.322
## [409] 2.941 3.547 4.944 5.111 3.112 4.633 3.087 4.591 3.521 5.863 3.378 5.240
## [421] 5.149 3.222 4.786 2.656 3.382 3.262 5.545 4.794 4.686 4.607 3.930 4.312
## [433] 4.551 3.818 5.143 4.131 5.036 3.783 3.961 4.147 4.006 3.216 4.311 4.338
## [445] 5.102 4.549 4.638 3.269 4.908 4.650 5.246 4.196 4.390 5.254 4.200 5.600
## [457] 3.869 5.581 4.185 2.659 4.344 4.480 3.765 3.036 5.254 5.896 5.623 4.915
## [469] 5.173 2.990 3.278 3.737 4.101 2.177 3.564 3.481 3.995 3.487 4.302 4.680
## [481] 2.744 4.626 2.657 3.334 3.445 4.674 5.465 3.000 5.162 4.168 3.044 5.752
## [493] 4.548 5.582 5.033 3.968 5.232 3.183 4.579 4.832 4.233 2.811 4.621 4.157
## [505] 2.883 3.345 3.549 3.782 4.964 5.557 4.173 4.725 4.488 5.090 4.041 4.439
## [517] 3.680 3.988 4.770 3.875 5.043 3.004 2.541 4.743 2.044 4.961 4.421 5.022
## [529] 4.059 3.512 5.980 5.149 4.895 4.966 3.314 4.732 6.176 5.061 5.210 4.336
## [541] 4.594 3.507 5.514 3.694 3.466 3.997 4.809 4.334 2.590 5.090 4.124 5.057
## [553] 2.058 5.192 3.886 3.409 3.648 3.165 2.539 6.322 5.002 2.778 4.064 6.127
## [565] 3.302 5.102 3.748 4.008 2.902 3.350 5.407 4.467 4.410 2.785 3.434 4.246
## [577] 3.732 3.068 3.542 4.117 4.005 3.970 3.282 4.817 4.876 4.959 5.401 2.901
## [589] 4.257 4.569 1.938 4.038 3.199 3.917 3.895 4.094 4.261 3.686 3.381 4.619
## [601] 3.678 4.722 3.227 5.344 3.171 1.898 3.801 3.112 4.903 3.601 3.935 1.102
## [613] 4.283 4.277 3.847 4.685 4.572 2.716 4.763 6.322 4.302 4.374 3.544 4.945
## [625] 4.272 4.002 3.669 3.815 4.748 5.130 6.387 4.589 3.975 4.926 3.950 5.923
## [637] 3.925 4.074 4.972 4.208 2.891 3.502 4.306 4.088 4.574 3.693#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans2=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 45.18 57.71 57.78 70.11 100.00round(sd(IH_trans2),2)## [1] 17.59#hist(IH_trans,col="azure2",xlab="IPRG",main = "Distribución de IPRG",freq = FALSE,ylim = c(0,0.035))ECM
#ERROR DE ESTIMACIÓN (EE)
n=round(646*0.80,0);n## [1] 517NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Palmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:16])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
} #; NfinalMedia
hist(NfinalMedia)
I_mediana2=mean(NfinalMedia);I_mediana## [1] 54.94283#I_ref=58.21
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana2)+(I_mediana2-I_mediana)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana)^2+(I_mediana2-I_mediana)^2,2);EE## [1] 7679.75grupos
#Datos=Palmira[,c(19:34)]
#falta activar incertidumbre
pesos=c(
#voluntariedad
#0.50,0.50,
#conocimiento
#0.171,0.220,0.171,0.195,0.244,
#incertidumbre
#0.032,0.024,0.024,
#confianza gubernamental
#0.241,0.276,0.241,0.241,
#confianza sector salud
#0.438,0.563,
#confianza medios
#0.467,0.533,
#probabilidad de contagio
0.164,0.145,0.164,0.127,0.109,0.145,0.145,
#severidad
0.241,0.276,0.276,0.207,
#susceptibilidad
0.222,0.222,0.194,0.167,0.194
#,
#cumplimiento
#0.190,0.190,0.190,0.143,0.143,0.143
)PROBABILIDAD DE CONTAGIO
#--------------------------------probabilidad de contagio-----------------------------------
#ProbabilidadContagio=Datos[,c(16:22)]*pesos[c(16:22)];ProbabilidadContagio
temp_K<-c()
lista_vacia <- vector("list", length = 646)
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};ProbabilidadContagio=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(ProbabilidadContagio[ ,1:7])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans62=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans62)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 50.98 64.30 63.98 76.94 100.00round(sd(IH_trans62),2)## [1] 19.47#hist(IH_trans6,col="azure2",xlab="IPPC",main = "Distribución de IPPC",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Palmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 1:7) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(1:7)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:7])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
}; NfinalMedia## [1] 64.01078 62.57698 64.66513 62.93303 62.58661 62.56736 64.64588 62.58661
## [9] 65.01155 62.93303 64.64588 64.66513 64.33795 62.56736 64.29946 64.65550
## [17] 64.66513 64.66513 64.64588 63.06774 64.12625 63.27945 63.97229 63.43341
## [25] 63.25058 62.58661 62.04773 64.66513 63.27945 65.35797 64.66513 63.58737
## [33] 64.66513 64.64588 64.66513 64.33795 63.78945 63.43341 64.66513 62.75019
## [41] 64.64588 63.43341 64.33795 63.27945 64.65550 63.93380 65.33872 62.57698
## [49] 65.35797 64.66513 64.31871 64.17436 63.95304 63.98191 64.33795 64.66513
## [57] 62.58661 64.29946 63.95304 64.66513 63.93380 64.29946 64.31871 65.02117
## [65] 64.66513 64.64588 65.36759 64.01078 62.40377 63.95304 63.93380 64.29946
## [73] 64.66513 64.66513 65.02117 64.66513 62.75982 64.64588 63.76059 64.31871
## [81] 63.08699 64.33795 64.33795 62.56736 64.49192 64.65550 62.91378 64.66513
## [89] 62.93303 64.66513 62.91378 65.02117 64.66513 63.76059 63.78945 65.33872
## [97] 63.93380 62.91378 63.07737 65.34834 64.01078 63.98191 65.18476 64.49192
## [105] 65.55042 62.93303 65.37721 64.65550 64.66513 65.01155 64.66513 64.29946
## [113] 64.66513 65.03079 63.95304 64.12625 64.66513 64.33795 64.64588 63.93380
## [121] 63.11586 64.66513 64.31871 63.79908 62.58661 63.08699 63.06774 63.94342
## [129] 64.66513 63.11586 65.03079 63.61624 64.31871 63.95304 63.95304 62.58661
## [137] 63.93380 62.94265 64.66513 63.27945 64.83834 63.58737 62.93303 65.03079
## [145] 64.65550 64.49192 63.58737 62.56736 64.66513 62.56736 64.49192 64.31871
## [153] 64.66513 63.98191 63.94342 64.66513 64.67475 64.66513 63.78945 65.90647
## [161] 63.61624 62.91378 62.92340 62.58661 65.19438 63.93380 64.66513 63.27945
## [169] 64.31871 62.56736 63.58737 63.58737 64.15512 63.98191 63.08699 64.01078
## [177] 63.93380 65.03079 64.01078 64.29946 63.11586 63.27945 64.66513 64.29946
## [185] 65.03079 63.78945 63.58737 63.94342 63.25058 63.98191 63.95304 64.66513
## [193] 62.93303 64.66513 62.04773 63.93380 62.91378 64.15512 63.61624 63.58737
## [201] 63.58737 64.66513 64.66513 63.27945 64.65550 65.01155 62.56736 62.56736
## [209] 64.49192 64.66513 63.08699 63.98191 63.27945 64.66513 63.64511 63.93380
## [217] 64.29946 65.34834 64.65550 63.27945 64.66513 63.27945 65.03079 64.66513
## [225] 64.64588 64.66513 64.64588 64.66513 64.64588 65.03079 63.78945 65.01155
## [233] 65.74288 64.66513 63.95304 63.61624 63.43341 63.27945 64.12625 63.98191
## [241] 64.29946 64.29946 64.66513 65.03079 63.25058 64.64588 63.95304 64.66513
## [249] 64.67475 64.15512 63.27945 63.64511 64.33795 63.94342 63.58737 63.58737
## [257] 63.97229 64.49192 63.58737 61.87452 63.78945 63.95304 64.67475 64.66513
## [265] 64.64588 64.66513 62.91378 62.56736 62.91378 64.15512 62.91378 64.68437
## [273] 64.29946 65.03079 65.03079 64.66513 62.92340 64.31871 63.58737 63.58737
## [281] 65.03079 63.93380 65.18476 63.93380 65.35797 64.29946 63.97229 64.66513
## [289] 64.83834 62.93303 64.33795 64.66513 64.50154 64.29946 63.93380 62.95227
## [297] 63.94342 64.66513 64.15512 63.43341 63.58737 63.94342 63.58737 63.10624
## [305] 64.29946 63.58737 62.58661 63.94342 64.66513 64.64588 62.91378 64.84796
## [313] 65.20400 63.25058 64.66513 64.65550 64.31871 64.66513 63.94342 63.95304
## [321] 64.66513 62.91378 64.64588 63.58737 63.25058 63.98191 62.95227 62.93303
## [329] 64.01078 63.94342 62.95227 62.56736 63.43341 63.27945 62.38453 64.47267
## [337] 64.66513 63.95304 64.66513 64.33795 64.29946 62.92340 65.34834 63.64511
## [345] 62.93303 62.58661 63.61624 62.94265 64.31871 64.66513 63.22171 63.98191
## [353] 62.93303 62.94265 64.66513 64.66513 63.94342 63.27945 63.22171 65.20400
## [361] 63.07737 64.66513 65.03079 64.66513 63.27945 63.43341 64.65550 64.68437
## [369] 63.27945 62.23056 63.94342 64.66513 64.66513 64.66513 63.94342 64.64588
## [377] 64.33795 63.27945 63.95304 63.61624 63.58737 62.95227 65.03079 64.33795
## [385] 64.29946 63.94342 63.93380 63.93380 64.29946 64.66513 64.29946 64.31871
## [393] 63.95304 62.56736 64.15512 62.57698 63.61624 64.66513 62.93303 65.39646
## [401] 63.95304 64.66513 64.49192 64.66513 64.66513 63.95304 64.31871 63.95304
## [409] 64.33795 63.58737 65.35797 63.58737 64.15512 63.58737 64.01078 63.58737
## [417] 63.46228 63.95304 64.66513 64.67475 64.66513 63.94342 64.29946 64.29946
## [425] 65.02117 64.33795 63.78945 63.27945 64.66513 64.66513 63.27945 63.58737
## [433] 62.58661 64.66513 62.93303 63.27945 64.66513 64.66513 64.65550 65.37721
## [441] 64.66513 64.64588 64.66513 63.94342 63.58737 63.61624 63.94342 64.31871
## [449] 64.31871 63.58737 62.93303 63.61624 63.08699 64.31871 63.27945 63.22171
## [457] 64.66513 62.92340 64.66513 63.61624 63.43341 64.66513 64.66513 63.27945
## [465] 63.25058 64.15512 63.93380 64.64588 63.58737 64.15512 63.27945 64.33795
## [473] 65.71401 64.15512 64.66513 65.55042 64.33795 63.27945 64.83834 63.27945
## [481] 65.03079 64.65550 63.95304 63.94342 64.66513 64.67475 64.33795 64.66513
## [489] 64.29946 64.66513 64.66513 63.08699 62.95227 63.07737 62.58661 63.64511
## [497] 63.64511 64.66513 62.94265 62.58661 64.14550 64.66513 64.29946 64.65550
## [505] 63.94342 63.93380 63.46228 63.58737 63.64511 64.66513 64.33795 64.66513
## [513] 63.93380 63.94342 64.66513 63.61624 62.95227 65.35797 63.78945 64.29946
## [521] 64.31871 63.43341 62.75019 63.78945 63.78945 63.94342 63.95304 64.33795
## [529] 64.66513 64.66513 62.92340 65.37721 63.98191 64.66513 62.94265 63.94342
## [537] 64.66513 62.75019 62.56736 63.95304 63.95304 64.66513 65.74288 64.64588
## [545] 64.31871 64.66513 64.64588 63.95304 65.37721 64.01078 64.66513 64.66513
## [553] 64.66513 62.22094 63.58737 64.66513 63.61624 63.95304 64.66513 63.97229
## [561] 64.66513 63.98191 62.95227 62.94265 63.58737 64.31871 64.66513 64.66513
## [569] 63.27945 62.92340 65.03079 64.64588 64.64588 62.58661 64.64588 62.56736
## [577] 63.61624 65.03079 63.58737 64.49192 64.66513 62.24018 65.03079 62.56736
## [585] 63.27945 62.93303 64.66513 63.94342 64.12625 65.03079 63.78945 63.95304
## [593] 64.49192 64.66513 63.27945 63.94342 64.84796 62.56736 64.15512 64.29946
## [601] 63.97229 64.66513 64.29946 62.56736 63.58737 62.56736 65.03079 64.64588
## [609] 64.66513 62.93303 63.78945 64.49192 65.02117 64.66513 64.66513 64.15512
## [617] 64.29946 64.66513 65.35797 63.64511 62.56736 63.61624 64.29946 63.95304
## [625] 63.95304 64.66513 64.65550 63.27945 64.66513 64.64588 64.29946 64.50154
## [633] 65.03079 65.03079 62.56736 64.66513 64.66513 64.66513 62.56736 63.10624
## [641] 64.66513 64.65550 64.66513 62.93303 63.64511 63.25058 64.65550 64.64588
## [649] 64.66513 62.95227 63.58737 64.66513 65.19438 64.64588 65.02117 64.66513
## [657] 64.66513 63.27945 62.56736 64.01078 64.66513 62.57698 64.65550 65.01155
## [665] 63.25058 64.66513 63.95304 63.43341 64.15512 64.66513 62.92340 64.01078
## [673] 63.27945 64.33795 63.58737 65.03079 60.85450 64.33795 64.66513 64.15512
## [681] 63.58737 64.31871 65.37721 63.27945 64.66513 65.02117 64.66513 63.78945
## [689] 64.66513 64.66513 64.66513 62.56736 64.31871 64.66513 63.94342 63.43341
## [697] 64.66513 64.66513 64.84796 62.54811 62.94265 64.48229 64.66513 63.43341
## [705] 65.02117 63.43341 64.49192 63.95304 64.66513 63.76059 64.66513 63.93380
## [713] 63.94342 64.66513 63.22171 64.66513 62.93303 63.95304 64.83834 63.94342
## [721] 64.66513 63.58737 64.67475 64.66513 65.03079 63.27945 63.58737 65.01155
## [729] 63.76059 63.64511 63.58737 64.29946 64.49192 63.76059 62.57698 64.66513
## [737] 65.03079 63.27945 63.78945 64.66513 64.12625 65.35797 63.61624 64.66513
## [745] 63.94342 63.95304 64.83834 64.66513 64.66513 63.95304 62.75019 63.95304
## [753] 64.01078 64.01078 63.98191 63.93380 64.66513 63.58737 64.66513 64.66513
## [761] 62.57698 62.93303 63.64511 62.93303 62.92340 64.66513 64.66513 64.33795
## [769] 64.66513 63.27945 63.27945 64.49192 64.15512 64.29946 63.79908 63.27945
## [777] 64.64588 64.66513 64.64588 63.27945 62.93303 62.57698 63.25058 63.79908
## [785] 64.65550 64.66513 63.94342 64.65550 63.11586 62.58661 64.65550 64.66513
## [793] 63.58737 65.18476 64.29946 64.66513 64.66513 62.56736 64.67475 64.66513
## [801] 63.27945 63.61624 64.66513 62.56736 63.58737 64.66513 62.56736 64.66513
## [809] 64.66513 62.58661 64.66513 63.93380 62.95227 64.66513 65.03079 63.93380
## [817] 62.57698 64.64588 64.15512 63.78945 64.66513 64.49192 62.57698 63.27945
## [825] 64.29946 64.66513 64.64588 64.66513 64.66513 64.64588 64.66513 63.95304
## [833] 63.94342 64.83834 63.58737 65.01155 64.49192 65.03079 62.57698 64.31871
## [841] 64.66513 64.66513 63.95304 65.34834 64.66513 62.57698 64.66513 63.27945
## [849] 64.66513 64.31871 63.95304 62.93303 64.31871 64.29946 64.33795 64.33795
## [857] 63.78945 62.93303 64.66513 64.66513 64.66513 62.93303 64.66513 62.58661
## [865] 64.67475 64.66513 64.67475 64.33795 63.58737 64.66513 63.61624 63.94342
## [873] 63.27945 63.94342 63.78945 63.58737 65.02117 64.84796 62.93303 64.66513
## [881] 64.84796 64.66513 64.66513 63.58737 64.33795 64.29946 64.64588 65.37721
## [889] 63.78945 64.66513 64.49192 64.67475 64.66513 63.58737 62.91378 64.29946
## [897] 64.49192 64.31871 64.31871 64.49192 63.11586 62.91378 62.57698 64.65550
## [905] 64.64588 63.43341 65.73326 63.58737 63.58737 64.29946 64.33795 63.27945
## [913] 64.66513 63.58737 64.66513 63.98191 63.58737 63.58737 64.66513 64.66513
## [921] 64.66513 64.66513 63.94342 64.33795 64.66513 64.66513 63.22171 63.58737
## [929] 64.33795 62.95227 64.31871 64.66513 63.61624 63.94342 63.93380 64.66513
## [937] 63.76059 65.18476 64.66513 64.31871 64.65550 63.94342 64.66513 62.93303
## [945] 63.78945 64.29946 64.31871 64.66513 64.65550 64.66513 64.67475 64.66513
## [953] 64.31871 62.56736 63.95304 62.92340 65.39646 62.58661 64.66513 64.66513
## [961] 63.58737 63.94342 63.93380 64.66513 63.61624 64.64588 64.66513 63.93380
## [969] 63.27945 63.95304 64.66513 64.31871 63.58737 64.66513 64.31871 64.66513
## [977] 63.58737 64.29946 63.08699 63.58737 64.66513 63.98191 64.64588 63.94342
## [985] 64.66513 63.08699 64.66513 62.93303 63.58737 63.27945 64.29946 64.66513
## [993] 63.58737 64.83834 65.18476 63.08699 63.93380 62.95227 63.78945 65.01155hist(NfinalMedia)
I_mediana62=median(NfinalMedia);
#I_ref=64.32
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana62)+(I_mediana62-I_mediana63)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana63)^2+(I_mediana62-I_mediana63)^2,2);EE## [1] 2206.93SEVERIDAD
#--------------------------------severidad-----------------------------------
#severidad=Datos[,c(23:26)]*pesos[c(23:26)];severidad
temp_K<-c()
lista_vacia <- vector("list", length = 646)
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};severidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(severidad[ ,1:4])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans72=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans72)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 44.39 58.05 57.79 71.85 100.00round(sd(IH_trans72),2)## [1] 21.81#hist(IH_trans7,col="azure2",xlab="IPSE",main = "Distribución de IPSE",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Palmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 8:11) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(8:11)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:4])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
}; NfinalMedia## [1] 58.05000 58.03333 58.61667 58.03333 58.60000 58.04167 58.05000 57.45833
## [9] 55.45833 57.46667 58.61667 58.61667 58.61667 58.32500 58.32500 54.60000
## [17] 58.61667 58.60000 55.75000 58.60000 58.03333 58.03333 58.03333 58.33333
## [25] 58.61667 57.46667 58.61667 57.75000 56.30000 57.46667 62.06667 58.61667
## [33] 58.03333 58.04167 58.61667 58.61667 58.61667 57.75000 58.03333 55.16667
## [41] 56.30000 57.46667 59.20000 58.61667 56.02500 57.46667 58.61667 58.61667
## [49] 55.45833 57.46667 54.60000 58.32500 57.46667 60.05833 55.75000 58.61667
## [57] 58.05000 55.16667 58.61667 58.03333 58.05000 58.61667 58.60000 58.61667
## [65] 57.46667 58.61667 61.20833 58.61667 57.75000 58.04167 58.61667 58.03333
## [73] 55.15000 58.61667 58.04167 54.58333 57.46667 58.61667 58.61667 58.61667
## [81] 55.75000 58.61667 57.46667 58.60833 58.03333 58.32500 57.46667 60.33333
## [89] 58.03333 57.46667 58.32500 58.05000 55.45833 58.61667 57.46667 54.87500
## [97] 58.61667 54.01667 58.03333 58.60000 56.30000 58.61667 54.01667 54.60000
## [105] 55.75000 58.61667 58.61667 58.61667 59.75833 55.75000 60.90000 56.60833
## [113] 58.03333 57.46667 60.90000 58.61667 58.61667 58.61667 58.05000 57.46667
## [121] 58.60000 58.61667 58.60000 58.61667 58.03333 55.16667 58.61667 58.32500
## [129] 58.03333 58.05000 58.61667 58.61667 58.03333 57.46667 58.61667 58.60833
## [137] 58.60000 58.61667 56.88333 57.46667 58.32500 58.61667 58.61667 58.60000
## [145] 57.46667 58.61667 58.61667 55.16667 58.61667 57.46667 57.46667 57.46667
## [153] 58.03333 54.60000 58.03333 56.02500 58.61667 60.91667 58.03333 58.61667
## [161] 58.61667 57.45833 58.05000 58.03333 58.05000 58.61667 58.61667 55.16667
## [169] 57.46667 57.75000 58.05000 54.58333 58.05000 58.61667 54.58333 58.03333
## [177] 55.75000 58.60833 58.61667 58.03333 60.62500 58.05000 58.61667 58.33333
## [185] 58.60000 54.60000 57.46667 58.03333 58.61667 59.47500 58.60000 57.46667
## [193] 58.32500 55.75000 58.61667 58.03333 58.32500 60.33333 57.46667 57.46667
## [201] 60.62500 58.32500 58.03333 58.03333 58.61667 58.60000 58.60000 58.61667
## [209] 57.46667 58.03333 55.15000 58.03333 58.61667 58.61667 57.46667 58.33333
## [217] 55.16667 58.04167 55.16667 58.32500 58.03333 58.61667 58.03333 58.61667
## [225] 59.75833 57.75000 58.61667 57.46667 60.33333 59.19167 58.61667 60.62500
## [233] 55.75000 58.04167 58.05000 58.03333 56.87500 57.75000 58.61667 55.15833
## [241] 60.90000 58.61667 58.03333 57.45833 57.75000 58.61667 58.61667 56.02500
## [249] 58.61667 54.87500 57.45000 58.61667 58.03333 58.05000 57.46667 55.45833
## [257] 58.03333 58.61667 58.61667 59.76667 57.46667 54.87500 60.90000 57.46667
## [265] 58.03333 57.45833 58.03333 58.03333 55.45000 58.61667 58.61667 57.46667
## [273] 57.46667 58.03333 58.61667 58.61667 58.03333 58.61667 56.88333 58.61667
## [281] 58.61667 58.61667 58.61667 58.61667 58.05000 58.61667 58.61667 57.46667
## [289] 58.61667 55.45833 57.75000 57.75000 58.32500 60.91667 56.30833 57.46667
## [297] 57.75000 58.05000 58.03333 58.61667 58.03333 58.61667 57.46667 58.61667
## [305] 58.05000 58.61667 58.61667 57.46667 58.61667 58.03333 55.45833 55.15833
## [313] 58.03333 58.61667 54.01667 58.61667 58.32500 58.32500 58.03333 58.61667
## [321] 58.32500 58.61667 55.45833 59.20000 57.46667 58.03333 57.75000 57.46667
## [329] 55.16667 58.61667 58.61667 57.75000 58.03333 58.60000 58.61667 60.34167
## [337] 58.32500 58.03333 58.61667 58.61667 58.03333 58.61667 57.46667 58.60833
## [345] 58.32500 58.05000 58.05000 54.60000 57.46667 58.32500 58.05000 57.45833
## [353] 58.61667 58.61667 58.61667 58.61667 58.61667 58.61667 58.61667 58.61667
## [361] 58.61667 58.61667 58.31667 58.04167 58.61667 58.61667 58.60000 58.61667
## [369] 59.75833 58.61667 58.03333 55.16667 58.61667 58.03333 55.16667 58.03333
## [377] 58.61667 58.61667 57.46667 58.05000 59.76667 58.61667 58.03333 58.03333
## [385] 58.61667 62.06667 58.32500 58.61667 58.03333 54.88333 58.61667 60.90000
## [393] 60.05000 54.01667 57.45833 57.75000 58.05000 58.61667 58.61667 58.03333
## [401] 54.87500 58.03333 58.61667 55.73333 58.03333 58.61667 58.61667 58.03333
## [409] 55.15833 55.16667 57.46667 55.15833 58.03333 58.61667 56.87500 54.01667
## [417] 58.03333 57.46667 58.61667 58.60000 60.62500 57.46667 54.58333 58.32500
## [425] 57.46667 58.04167 58.03333 58.61667 58.61667 60.05000 55.75000 58.03333
## [433] 58.04167 58.03333 55.45833 55.75000 58.61667 58.03333 58.61667 58.61667
## [441] 58.61667 58.03333 57.46667 54.60000 58.05000 55.16667 58.03333 58.61667
## [449] 58.03333 58.61667 58.33333 58.32500 58.05000 54.60000 58.61667 58.61667
## [457] 58.04167 58.61667 58.61667 57.45833 58.61667 58.61667 58.05000 58.61667
## [465] 58.61667 58.03333 58.61667 58.03333 58.03333 57.46667 59.47500 55.16667
## [473] 58.61667 58.61667 57.45833 58.61667 57.46667 58.05000 58.32500 58.61667
## [481] 58.05000 57.46667 58.32500 58.03333 59.47500 57.46667 58.03333 58.05000
## [489] 58.03333 58.60000 56.30000 54.60000 58.03333 58.03333 58.61667 58.03333
## [497] 58.04167 57.46667 58.03333 57.46667 55.16667 57.46667 56.02500 58.61667
## [505] 58.90000 58.32500 58.03333 57.45833 58.61667 58.61667 57.75000 57.46667
## [513] 58.61667 58.03333 58.05000 58.03333 60.90000 58.61667 58.05000 58.03333
## [521] 58.61667 58.61667 58.61667 58.03333 58.61667 58.61667 57.45833 58.04167
## [529] 58.05000 58.04167 58.61667 58.61667 58.03333 54.60000 58.05000 57.46667
## [537] 58.05000 58.61667 58.61667 58.04167 58.03333 58.61667 58.61667 58.61667
## [545] 58.61667 57.46667 58.61667 58.03333 54.60000 57.46667 58.61667 58.33333
## [553] 54.60000 58.03333 59.19167 57.46667 58.03333 58.90833 55.75000 58.32500
## [561] 58.05000 58.61667 57.46667 58.61667 55.16667 58.61667 58.04167 58.60833
## [569] 58.61667 55.16667 58.05000 58.03333 58.61667 57.46667 58.60833 58.05000
## [577] 58.32500 58.90000 58.05000 58.61667 58.03333 58.61667 58.90833 59.76667
## [585] 58.61667 58.05000 59.18333 60.33333 58.60000 54.59167 59.19167 57.46667
## [593] 54.59167 58.04167 58.60000 58.05000 58.04167 58.32500 58.03333 54.01667
## [601] 58.61667 58.03333 58.61667 58.61667 55.16667 57.46667 58.03333 58.60833
## [609] 57.75000 58.03333 56.88333 58.05000 58.05000 58.61667 59.75000 55.45833
## [617] 55.15000 58.61667 58.03333 58.61667 58.05000 55.16667 58.61667 58.61667
## [625] 58.04167 57.46667 57.46667 54.60000 58.60833 58.61667 58.03333 58.61667
## [633] 55.15833 58.61667 58.03333 58.61667 58.60000 58.03333 58.61667 57.46667
## [641] 57.46667 55.75000 58.03333 56.30000 60.33333 56.88333 58.05000 58.03333
## [649] 58.61667 58.61667 58.32500 58.04167 58.90833 58.61667 58.03333 59.76667
## [657] 54.60000 54.60000 58.61667 58.61667 58.61667 58.61667 58.61667 56.60000
## [665] 57.46667 58.61667 57.46667 57.46667 57.46667 57.46667 58.61667 58.61667
## [673] 54.59167 57.75000 58.61667 55.15000 58.61667 58.60000 58.61667 56.02500
## [681] 57.46667 58.61667 58.05000 58.61667 58.03333 58.03333 58.60833 55.75000
## [689] 58.61667 57.46667 58.61667 60.90000 59.47500 58.61667 59.75000 57.46667
## [697] 58.04167 57.46667 55.15833 57.46667 59.47500 58.61667 55.16667 58.61667
## [705] 59.20000 58.61667 58.05000 58.61667 58.61667 58.61667 57.46667 58.61667
## [713] 58.03333 56.30000 58.61667 54.01667 58.60833 59.18333 58.61667 58.03333
## [721] 58.61667 58.03333 58.04167 58.61667 58.05000 58.60000 58.03333 57.46667
## [729] 58.03333 58.60000 55.16667 58.61667 58.61667 55.15833 58.03333 57.46667
## [737] 57.75000 58.61667 58.61667 55.16667 58.61667 57.46667 60.33333 58.61667
## [745] 57.46667 57.46667 58.61667 57.75000 54.60000 58.05000 58.61667 58.61667
## [753] 60.90833 60.33333 58.03333 57.46667 58.61667 58.61667 58.03333 58.32500
## [761] 59.46667 57.46667 58.05000 58.61667 57.45833 57.46667 56.02500 57.46667
## [769] 58.05000 58.03333 58.60833 60.33333 58.03333 58.60000 58.61667 57.75000
## [777] 58.60000 58.32500 58.61667 58.90833 59.19167 58.03333 54.60000 58.61667
## [785] 58.03333 59.18333 58.61667 58.05000 60.90000 57.45000 58.03333 58.60000
## [793] 57.46667 58.03333 58.03333 54.58333 58.03333 58.90833 58.03333 60.05000
## [801] 58.05000 57.45000 58.61667 57.45000 59.20000 54.87500 58.03333 58.03333
## [809] 58.03333 58.61667 58.05000 54.01667 60.33333 58.32500 58.05000 58.61667
## [817] 58.61667 55.75000 55.15833 58.32500 57.75000 58.05000 58.05000 58.61667
## [825] 59.48333 58.03333 58.61667 57.46667 59.75000 54.58333 58.03333 58.05000
## [833] 58.61667 57.45000 58.03333 58.03333 55.75000 58.03333 58.60000 58.61667
## [841] 60.90833 56.87500 58.61667 56.30000 58.61667 58.05000 58.03333 58.61667
## [849] 58.61667 59.76667 59.75000 58.03333 58.61667 58.05000 58.61667 57.46667
## [857] 58.61667 58.03333 58.05000 58.61667 55.15000 57.46667 58.61667 58.61667
## [865] 58.03333 55.45833 54.87500 57.45000 54.59167 59.18333 58.60000 58.61667
## [873] 58.03333 58.61667 56.87500 58.03333 58.61667 58.03333 57.46667 58.05000
## [881] 58.61667 58.90000 57.75000 58.61667 54.60000 55.45833 58.61667 57.46667
## [889] 58.03333 58.61667 58.05000 58.61667 55.15833 59.75833 58.61667 57.46667
## [897] 58.61667 58.04167 57.46667 57.75000 58.61667 58.61667 58.03333 58.61667
## [905] 58.61667 58.05000 58.32500 58.03333 58.61667 55.45833 54.60000 58.61667
## [913] 58.61667 54.60000 58.61667 58.61667 58.61667 58.61667 58.03333 57.46667
## [921] 60.62500 58.03333 58.60000 58.61667 58.03333 59.19167 57.75000 55.16667
## [929] 58.60833 58.32500 58.05000 58.60000 57.45000 58.61667 58.61667 58.04167
## [937] 58.61667 55.15833 58.05000 58.61667 58.90000 58.61667 58.03333 54.60000
## [945] 57.46667 60.33333 58.61667 58.03333 57.46667 58.61667 55.15833 58.61667
## [953] 58.61667 58.61667 59.18333 54.60000 58.61667 57.75000 57.46667 56.87500
## [961] 58.04167 58.61667 58.61667 61.19167 54.01667 58.61667 58.61667 58.05000
## [969] 58.61667 58.03333 57.45833 59.19167 57.46667 58.05000 58.05000 59.46667
## [977] 57.46667 55.45833 57.46667 54.60000 58.03333 60.35000 58.05000 58.03333
## [985] 58.61667 58.03333 55.75000 58.61667 62.05000 58.61667 55.45833 58.03333
## [993] 58.03333 58.03333 57.45833 58.31667 58.03333 58.61667 59.47500 58.61667hist(NfinalMedia)
I_mediana72=median(NfinalMedia);
#I_ref=58.05
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana72)+(I_mediana72-I_mediana73)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana73)^2+(I_mediana71-I_mediana73)^2,2);EE## [1] 15369.05SUSCEPTIBILIDAD
#--------------------------------susceptibilidad-----------------------------------
#susceptibilidad=Datos[,c(27:31)]*pesos[c(27:31)];susceptibilidad
temp_K<-c()
lista_vacia <- vector("list", length = 646)
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};susceptibilidad=data.frame(celsius_to_kelvin(Datos))
IH=rowSums(susceptibilidad[ ,1:5])#;IH
#data.frame(IH)
IH_MIN=min(IH)
IH_MAX=max(IH)
IH_trans82=((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
summary(IH_trans82)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 27.41 43.75 43.88 57.69 100.00round(sd(IH_trans82),2)## [1] 21.04#hist(IH_trans8,col="azure2",xlab="IPSU",main = "Distribución de IPSU",freq = FALSE,ylim = c(0,0.035))#ERROR
NfinalMedia<-c() ##En este vector se estan almacenando las proporciones, osea, cada repeticion bootstrap
for (i in 1:1000) {
#Datos=CaliyPalmira[,-c(8:10)] #Este es cuando junté todo y solo quité la incertidumbre
Datos=Palmira[,c(19:34)]
#N<-Datos[sample(nrow(Datos),replace = TRUE)]
temp_K<-c()
lista_vacia <- vector("list", length = length(Datos))
celsius_to_kelvin <- function(temp_C) {
for (i in 12:16) {
# temp_K <- temp_C[,i]*vector[i]
lista_vacia[i]<-data.frame(#temp_C[,i]*vector[i])
Datos[,i]*pesos[i])
datosnew=lista_vacia[c(12:16)]
}
return(datosnew)
};IHPRG1=data.frame(celsius_to_kelvin(Datos))
IH<-rowSums(IHPRG1[ ,1:5])
#data.frame(IH)
IH_MIN<-min(IH)
IH_MAX<-max(IH)
IH_trans<-((IH-IH_MIN)/(IH_MAX-IH_MIN))*100
NfinalMedia[i]=median(sample(IH_trans,replace=T))
}; NfinalMedia## [1] 40.60984 44.23077 43.26057 43.51178 43.74567 43.02668 41.11227 42.54158
## [9] 44.23077 40.86105 44.21344 43.02668 43.51178 43.74567 40.38462 43.74567
## [17] 43.74567 40.86105 44.23077 40.85239 44.23077 44.21344 43.74567 44.21344
## [25] 42.05648 43.27789 40.38462 43.26923 43.74567 43.74567 43.74567 40.85239
## [33] 43.74567 44.23077 41.35482 43.74567 44.23077 43.74567 42.29037 43.74567
## [41] 43.74567 43.27789 43.27789 43.74567 40.61850 43.74567 44.22211 40.86972
## [49] 43.27789 40.83507 40.38462 40.38462 43.74567 40.85239 43.26923 43.74567
## [57] 41.58870 43.74567 43.74567 44.23077 43.26923 43.74567 43.74567 40.38462
## [65] 44.23077 40.84373 43.26057 43.74567 43.74567 40.84373 44.23077 43.51178
## [73] 41.35482 44.21344 43.74567 44.22211 42.54158 44.23077 41.82259 40.38462
## [81] 43.74567 40.38462 42.77547 44.21344 40.85239 40.61850 41.33749 40.85239
## [89] 43.26923 40.38462 43.27789 41.10360 43.74567 43.74567 40.38462 44.23077
## [97] 44.21344 43.74567 40.61850 45.18365 40.38462 42.54158 43.74567 41.82259
## [105] 44.23077 43.74567 41.82259 43.27789 44.23077 40.38462 43.74567 43.74567
## [113] 43.26923 43.74567 40.38462 43.26923 41.82259 43.74567 42.29037 43.50312
## [121] 40.85239 43.74567 42.05648 44.23077 43.74567 42.29037 40.85239 41.58870
## [129] 43.74567 40.38462 44.23077 42.29037 43.74567 43.74567 42.29037 43.26923
## [137] 43.74567 43.74567 44.23077 43.74567 43.74567 43.74567 43.74567 40.85239
## [145] 43.26923 41.35482 43.74567 41.58870 44.21344 43.26923 43.74567 43.74567
## [153] 43.74567 43.51178 40.38462 43.02668 44.23077 44.23077 44.22211 44.23077
## [161] 41.34615 42.78413 40.85239 44.21344 42.79279 44.23077 40.85239 41.11227
## [169] 44.46466 43.74567 44.21344 40.38462 43.74567 40.38462 44.23077 41.82259
## [177] 40.38462 44.22211 44.23077 43.74567 41.58870 40.38462 43.74567 42.77547
## [185] 43.74567 40.38462 43.74567 43.97956 42.05648 43.97956 43.74567 40.38462
## [193] 41.58870 43.74567 43.27789 43.74567 41.10360 40.85239 43.74567 44.23077
## [201] 43.27789 40.86105 43.74567 43.74567 40.38462 44.23077 43.74567 43.74567
## [209] 40.84373 43.74567 40.85239 40.85239 40.38462 43.74567 40.60984 43.27789
## [217] 41.58870 44.21344 42.05648 43.74567 43.74567 43.03534 43.74567 44.23077
## [225] 43.74567 43.74567 40.60984 43.74567 43.74567 44.21344 41.82259 43.74567
## [233] 40.85239 43.74567 42.05648 43.27789 40.85239 41.82259 41.35482 43.97956
## [241] 47.10672 45.66875 40.38462 43.74567 43.74567 42.54158 44.23077 43.27789
## [249] 43.74567 43.51178 43.74567 44.23077 43.74567 43.02668 40.38462 40.85239
## [257] 40.38462 41.33749 40.60984 40.38462 41.58870 43.74567 44.21344 40.84373
## [265] 40.85239 43.27789 43.74567 43.03534 44.23077 43.74567 42.54158 43.74567
## [273] 43.74567 43.26923 43.02668 41.58870 40.85239 43.74567 43.74567 43.74567
## [281] 44.23077 41.11227 43.02668 44.23077 40.61850 43.74567 41.82259 43.51178
## [289] 43.50312 44.23077 43.51178 44.23077 44.21344 40.38462 43.74567 44.21344
## [297] 41.82259 43.74567 43.74567 40.38462 43.74567 43.74567 43.74567 41.58870
## [305] 43.74567 42.78413 43.74567 44.21344 43.74567 43.51178 40.86105 43.74567
## [313] 43.74567 43.74567 44.23077 40.85239 44.23077 42.30769 41.34615 43.02668
## [321] 43.74567 43.27789 41.58870 43.74567 43.27789 43.74567 44.23077 43.74567
## [329] 43.26923 43.74567 40.38462 44.23077 41.10360 40.38462 43.74567 43.74567
## [337] 43.74567 43.74567 43.74567 44.23077 43.74567 40.38462 44.22211 43.74567
## [345] 44.23077 43.74567 40.38462 40.86972 43.74567 44.21344 40.86105 44.21344
## [353] 41.58870 41.58870 42.05648 43.74567 43.02668 40.85239 44.23077 40.38462
## [361] 44.21344 43.26923 44.21344 43.74567 40.85239 43.74567 40.85239 43.74567
## [369] 41.35482 40.61850 40.38462 43.74567 43.74567 42.77547 43.27789 44.21344
## [377] 43.97956 41.82259 43.74567 43.74567 40.38462 40.38462 44.23077 41.82259
## [385] 43.74567 43.74567 43.27789 44.23077 40.86105 43.74567 43.02668 44.23077
## [393] 42.05648 40.60984 44.23077 44.23077 43.74567 40.38462 44.22211 40.60984
## [401] 43.51178 44.23077 44.21344 43.74567 43.74567 43.97956 44.94109 43.27789
## [409] 43.74567 42.79279 40.84373 43.74567 40.85239 43.74567 43.74567 43.27789
## [417] 43.74567 40.60984 41.82259 40.83507 40.38462 40.85239 44.23077 41.35482
## [425] 44.23077 43.74567 43.27789 44.23077 40.38462 43.74567 43.74567 41.10360
## [433] 43.02668 42.05648 43.74567 40.61850 43.26923 43.74567 43.74567 43.74567
## [441] 43.74567 40.38462 40.86105 43.74567 40.38462 43.74567 43.74567 44.23077
## [449] 43.26057 42.29037 40.85239 43.27789 43.74567 40.86105 40.38462 43.26923
## [457] 42.54158 44.21344 40.38462 40.85239 44.22211 40.38462 43.51178 43.74567
## [465] 44.23077 44.21344 43.74567 41.82259 43.74567 43.74567 44.21344 43.74567
## [473] 40.38462 42.05648 43.74567 43.97956 44.23077 43.74567 41.58870 43.74567
## [481] 43.74567 43.74567 43.74567 42.79279 44.23077 44.22211 43.74567 41.82259
## [489] 43.26923 40.38462 43.74567 40.38462 43.74567 43.27789 40.38462 42.54158
## [497] 41.11227 44.21344 43.74567 44.21344 40.38462 43.74567 43.74567 40.38462
## [505] 40.38462 43.74567 41.58870 44.21344 43.74567 41.33749 44.21344 43.74567
## [513] 44.23077 43.74567 43.27789 43.74567 44.23077 40.38462 43.74567 44.21344
## [521] 40.86972 43.74567 41.35482 44.21344 43.74567 40.86972 43.27789 40.38462
## [529] 41.58870 43.74567 43.74567 43.74567 44.23077 43.74567 43.74567 42.29037
## [537] 44.23077 43.74567 44.23077 44.21344 43.74567 44.23077 44.23077 47.10672
## [545] 43.74567 44.23077 43.74567 44.23077 40.85239 44.22211 43.74567 42.79279
## [553] 43.74567 44.22211 43.51178 40.38462 43.74567 44.21344 43.74567 43.74567
## [561] 43.26923 40.61850 41.35482 43.74567 40.62717 43.74567 43.74567 41.11227
## [569] 43.74567 43.74567 43.74567 43.97956 40.38462 43.74567 43.74567 43.74567
## [577] 44.23077 41.34615 40.85239 43.74567 42.79279 43.74567 43.74567 42.05648
## [585] 44.23077 43.51178 43.74567 40.38462 44.23077 43.74567 44.23077 43.02668
## [593] 43.74567 40.85239 44.23077 40.85239 42.05648 43.74567 43.74567 41.82259
## [601] 44.23077 43.27789 43.74567 43.74567 44.22211 40.38462 40.38462 41.82259
## [609] 42.29037 40.38462 44.23077 42.79279 44.22211 43.74567 40.83507 43.51178
## [617] 43.74567 41.82259 43.74567 41.82259 44.23077 43.74567 43.74567 43.74567
## [625] 44.46466 40.38462 43.74567 44.23077 43.74567 41.58870 42.78413 43.74567
## [633] 43.26057 43.51178 43.74567 42.79279 44.21344 43.74567 40.38462 40.85239
## [641] 43.74567 43.74567 43.74567 43.74567 40.85239 43.74567 43.74567 43.74567
## [649] 41.82259 40.38462 43.74567 44.23077 44.21344 44.23077 42.77547 40.83507
## [657] 40.86972 43.97956 43.74567 40.38462 43.74567 40.85239 43.02668 43.74567
## [665] 40.38462 43.74567 43.74567 43.74567 43.74567 40.83507 43.74567 43.26923
## [673] 42.79279 43.74567 43.74567 44.23077 43.74567 44.23077 43.74567 43.74567
## [681] 43.74567 44.21344 43.74567 43.27789 43.74567 41.34615 43.74567 43.74567
## [689] 43.74567 43.74567 40.85239 42.78413 44.23077 44.21344 43.74567 40.38462
## [697] 42.29037 40.86972 43.74567 40.37595 47.10672 40.38462 43.74567 40.38462
## [705] 43.74567 43.51178 44.23077 42.79279 43.74567 43.74567 43.27789 43.74567
## [713] 43.74567 44.21344 40.85239 40.38462 40.85239 43.97956 43.74567 43.03534
## [721] 43.74567 40.38462 43.97956 43.74567 40.38462 43.51178 43.74567 43.74567
## [729] 43.74567 42.05648 40.85239 40.38462 44.23077 40.38462 43.51178 42.79279
## [737] 44.23077 44.23077 40.85239 43.74567 43.74567 43.74567 44.21344 43.74567
## [745] 44.21344 43.74567 43.27789 43.74567 43.74567 40.84373 43.74567 44.23077
## [753] 43.74567 43.74567 43.74567 47.10672 43.74567 43.74567 43.74567 40.38462
## [761] 40.38462 40.38462 42.54158 44.21344 44.23077 40.85239 40.38462 44.23077
## [769] 43.74567 43.74567 41.58870 44.23077 43.51178 40.38462 43.74567 43.74567
## [777] 43.27789 43.74567 40.60984 40.85239 43.74567 41.35482 43.74567 40.85239
## [785] 40.38462 43.74567 44.21344 43.74567 43.74567 43.74567 44.21344 40.85239
## [793] 44.21344 43.74567 40.38462 44.23077 40.38462 44.23077 41.82259 44.23077
## [801] 41.11227 44.21344 43.97956 42.29037 40.86972 43.74567 41.33749 40.38462
## [809] 40.38462 40.38462 44.21344 40.86972 43.26057 42.30769 41.82259 41.82259
## [817] 43.74567 43.74567 43.51178 43.74567 41.82259 43.74567 44.21344 41.33749
## [825] 41.11227 46.17117 43.50312 43.74567 43.74567 40.85239 43.74567 40.85239
## [833] 43.74567 43.02668 43.74567 43.02668 42.54158 43.27789 43.97956 40.38462
## [841] 42.30769 44.22211 40.85239 43.74567 41.82259 40.38462 43.74567 41.11227
## [849] 43.74567 43.51178 40.38462 40.38462 43.27789 42.29037 41.58870 40.85239
## [857] 41.82259 44.23077 42.79279 40.38462 40.38462 44.21344 43.74567 44.22211
## [865] 43.74567 44.22211 42.79279 43.74567 40.38462 43.27789 43.74567 44.23077
## [873] 40.38462 43.02668 43.74567 40.86972 43.74567 40.60984 40.83507 43.74567
## [881] 44.23077 43.74567 40.85239 43.74567 43.50312 40.38462 43.51178 44.23077
## [889] 43.74567 44.23077 44.21344 42.29037 43.74567 43.74567 42.05648 43.74567
## [897] 40.85239 44.23077 43.74567 43.26057 43.74567 43.74567 43.03534 44.23077
## [905] 43.97956 44.21344 44.23077 43.74567 40.60984 43.74567 44.23077 40.38462
## [913] 43.74567 43.74567 43.74567 43.74567 40.38462 44.21344 43.51178 43.27789
## [921] 43.26057 43.74567 40.84373 43.74567 43.74567 43.01802 43.27789 43.74567
## [929] 40.85239 40.38462 41.82259 43.51178 40.85239 43.27789 40.38462 42.54158
## [937] 44.23077 44.21344 43.26923 40.85239 43.51178 42.30769 42.05648 44.21344
## [945] 43.74567 43.74567 43.74567 43.74567 40.85239 42.77547 42.29037 40.38462
## [953] 42.05648 40.85239 42.29037 41.35482 40.38462 43.74567 42.79279 43.74567
## [961] 42.05648 40.38462 43.51178 40.86972 43.97956 43.74567 44.21344 41.82259
## [969] 43.74567 42.05648 40.38462 44.21344 43.74567 44.23077 43.74567 43.74567
## [977] 44.71587 42.54158 42.54158 41.82259 43.74567 43.26923 43.74567 40.38462
## [985] 40.85239 44.23077 41.83125 43.74567 40.38462 40.38462 44.21344 40.85239
## [993] 40.38462 40.85239 46.63895 43.51178 40.38462 44.22211 40.38462 43.74567hist(NfinalMedia)
I_mediana82=median(NfinalMedia);
#I_ref=43.73
#EE=I_ref-round(I_mediana,3);EE
#EE<-round((1/1000)*sum((NfinalMedia-I_mediana)^2+((1/1000)*sum(NfinalMedia)-I_mediana)^2),2);EE
#EE<-round(var(I_mediana82)+(I_mediana82-I_mediana83)^2,2);EE
EE<-round((1/(1000-1))*sum(NfinalMedia-I_mediana83)^2+(I_mediana82-I_mediana83)^2,2);EE## [1] 88808.07