PL_S14
Micaela Loza 20211300
2024-06-19
setwd("C:/Users/USUARIO/Documents/2024-1/Estadística 2 teoría/Ejercicios extra magallanes/PL Final")library(rio)## Warning: package 'rio' was built under R version 4.3.3data=import("dataOK_all.xlsx")## New names:
## • `` -> `...1`str(data)## 'data.frame': 196 obs. of 50 variables:
## $ ...1 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ key : chr "AMAZONAS+BAGUA" "AMAZONAS+BONGARA" "AMAZONAS+CHACHAPOYAS" "AMAZONAS+CONDORCANQUI" ...
## $ Código : num 102 103 101 104 105 106 107 202 203 204 ...
## $ pared1_Ladrillo : num 4633 1602 3782 291 430 ...
## $ pared2_Piedra : num 46 9 22 7 7 7 35 1 0 3 ...
## $ pared3_Adobe : num 6639 2729 5881 672 5217 ...
## $ pared4_Tapia : num 222 240 2476 8 6052 ...
## $ pared5_Quincha : num 2518 157 309 386 346 ...
## $ pared6_Piedra : num 127 36 168 7 54 28 518 65 7 6 ...
## $ pared7_Madera : num 4484 2505 1270 8145 606 ...
## $ pared8_Triplay : num 851 30 91 200 45 24 210 18 0 1 ...
## $ pared9_Otro : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pared10_Total : num 19520 7308 13999 9716 12757 ...
## $ techo1_Concreto : num 2187 692 2262 56 187 ...
## $ techo2_Madera : num 294 75 160 188 43 48 340 57 12 8 ...
## $ techo3_Tejas : num 179 382 3393 177 3071 ...
## $ techo4_Planchas : num 13186 6084 8005 2036 9343 ...
## $ techo5_Caña : num 160 38 50 15 26 15 196 10 8 5 ...
## $ techo6_Triplay : num 106 5 14 10 12 5 62 17 4 3 ...
## $ techo7_Paja : num 3408 32 115 7234 75 ...
## $ techo8_Otro : num 0 0 0 0 0 0 0 0 0 0 ...
## $ techo9_Total : num 19520 7308 13999 9716 12757 ...
## $ piso1_Parquet : num 6 5 23 2 4 3 20 0 0 5 ...
## $ piso2_Láminas : num 19 2 36 0 0 4 32 0 0 1 ...
## $ piso3_Losetas : num 647 165 1077 20 46 ...
## $ piso4_Madera : num 157 132 240 1523 295 ...
## $ piso5_Cemento : num 7121 2917 6189 943 1911 ...
## $ piso6_Tierra : num 11569 4087 6434 7228 10501 ...
## $ piso7_Otro : num 1 0 0 0 0 0 0 0 0 0 ...
## $ piso8_Total : num 19520 7308 13999 9716 12757 ...
## $ agua1_Red : num 9429 4569 10647 1307 7172 ...
## $ agua2_Red_fueraVivienda: num 4392 1497 1619 867 3097 ...
## $ agua3_Pilón : num 793 215 184 1003 1112 ...
## $ agua4_Camión : num 59 0 49 2 0 0 117 0 0 0 ...
## $ agua5_Pozo : num 1792 474 876 2564 819 ...
## $ agua6_Manantial : num 270 67 92 431 132 211 471 121 61 27 ...
## $ agua7_Río : num 2648 388 488 3428 369 ...
## $ agua8_Otro : num 56 61 24 80 9 29 104 2 1 6 ...
## $ agua9_Vecino : num 81 37 20 34 47 8 177 9 4 6 ...
## $ agua10_Total : num 19520 7308 13999 9716 12757 ...
## $ elec1_Sí : num 13204 6025 12248 1792 10886 ...
## $ elec2_No : num 6316 1283 1751 7924 1871 ...
## $ elec3_Total : num 19520 7308 13999 9716 12757 ...
## $ departamento : chr "AMAZONAS" "AMAZONAS" "AMAZONAS" "AMAZONAS" ...
## $ provincia : chr "BAGUA" "BONGARA" "CHACHAPOYAS" "CONDORCANQUI" ...
## $ Castillo : num 25629 8374 15671 13154 12606 ...
## $ Keiko : num 10770 5209 10473 1446 7840 ...
## $ ganaCastillo : num 1 1 1 1 1 1 1 1 1 1 ...
## $ countPositivos : num 8126 389 2174 3481 456 ...
## $ countFallecidos : num 462 72 281 111 88 60 336 26 31 21 ...total_na <- sum(is.na(data))
print(paste("Total de valores NA:", total_na))## [1] "Total de valores NA: 0"names(data)## [1] "...1" "key"
## [3] "Código" "pared1_Ladrillo"
## [5] "pared2_Piedra" "pared3_Adobe"
## [7] "pared4_Tapia" "pared5_Quincha"
## [9] "pared6_Piedra" "pared7_Madera"
## [11] "pared8_Triplay" "pared9_Otro"
## [13] "pared10_Total" "techo1_Concreto"
## [15] "techo2_Madera" "techo3_Tejas"
## [17] "techo4_Planchas" "techo5_Caña"
## [19] "techo6_Triplay" "techo7_Paja"
## [21] "techo8_Otro" "techo9_Total"
## [23] "piso1_Parquet" "piso2_Láminas"
## [25] "piso3_Losetas" "piso4_Madera"
## [27] "piso5_Cemento" "piso6_Tierra"
## [29] "piso7_Otro" "piso8_Total"
## [31] "agua1_Red" "agua2_Red_fueraVivienda"
## [33] "agua3_Pilón" "agua4_Camión"
## [35] "agua5_Pozo" "agua6_Manantial"
## [37] "agua7_Río" "agua8_Otro"
## [39] "agua9_Vecino" "agua10_Total"
## [41] "elec1_Sí" "elec2_No"
## [43] "elec3_Total" "departamento"
## [45] "provincia" "Castillo"
## [47] "Keiko" "ganaCastillo"
## [49] "countPositivos" "countFallecidos"head(data)## ...1 key Código pared1_Ladrillo pared2_Piedra
## 1 1 AMAZONAS+BAGUA 102 4633 46
## 2 2 AMAZONAS+BONGARA 103 1602 9
## 3 3 AMAZONAS+CHACHAPOYAS 101 3782 22
## 4 4 AMAZONAS+CONDORCANQUI 104 291 7
## 5 5 AMAZONAS+LUYA 105 430 7
## 6 6 AMAZONAS+RODRIGUEZ DE MENDOZA 106 1546 7
## pared3_Adobe pared4_Tapia pared5_Quincha pared6_Piedra pared7_Madera
## 1 6639 222 2518 127 4484
## 2 2729 240 157 36 2505
## 3 5881 2476 309 168 1270
## 4 672 8 386 7 8145
## 5 5217 6052 346 54 606
## 6 2778 155 720 28 3646
## pared8_Triplay pared9_Otro pared10_Total techo1_Concreto techo2_Madera
## 1 851 0 19520 2187 294
## 2 30 0 7308 692 75
## 3 91 0 13999 2262 160
## 4 200 0 9716 56 188
## 5 45 0 12757 187 43
## 6 24 0 8904 480 48
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay techo7_Paja
## 1 179 13186 160 106 3408
## 2 382 6084 38 5 32
## 3 3393 8005 50 14 115
## 4 177 2036 15 10 7234
## 5 3071 9343 26 12 75
## 6 2810 5495 15 5 51
## techo8_Otro techo9_Total piso1_Parquet piso2_Láminas piso3_Losetas
## 1 0 19520 6 19 647
## 2 0 7308 5 2 165
## 3 0 13999 23 36 1077
## 4 0 9716 2 0 20
## 5 0 12757 4 0 46
## 6 0 8904 3 4 264
## piso4_Madera piso5_Cemento piso6_Tierra piso7_Otro piso8_Total agua1_Red
## 1 157 7121 11569 1 19520 9429
## 2 132 2917 4087 0 7308 4569
## 3 240 6189 6434 0 13999 10647
## 4 1523 943 7228 0 9716 1307
## 5 295 1911 10501 0 12757 7172
## 6 176 2974 5483 0 8904 5256
## agua2_Red_fueraVivienda agua3_Pilón agua4_Camión agua5_Pozo agua6_Manantial
## 1 4392 793 59 1792 270
## 2 1497 215 0 474 67
## 3 1619 184 49 876 92
## 4 867 1003 2 2564 431
## 5 3097 1112 0 819 132
## 6 1278 154 0 1020 211
## agua7_Río agua8_Otro agua9_Vecino agua10_Total elec1_Sí elec2_No elec3_Total
## 1 2648 56 81 19520 13204 6316 19520
## 2 388 61 37 7308 6025 1283 7308
## 3 488 24 20 13999 12248 1751 13999
## 4 3428 80 34 9716 1792 7924 9716
## 5 369 9 47 12757 10886 1871 12757
## 6 948 29 8 8904 6895 2009 8904
## departamento provincia Castillo Keiko ganaCastillo countPositivos
## 1 AMAZONAS BAGUA 25629 10770 1 8126
## 2 AMAZONAS BONGARA 8374 5209 1 389
## 3 AMAZONAS CHACHAPOYAS 15671 10473 1 2174
## 4 AMAZONAS CONDORCANQUI 13154 1446 1 3481
## 5 AMAZONAS LUYA 12606 7840 1 456
## 6 AMAZONAS RODRÍGUEZ DE MENDOZA 7967 5491 1 110
## countFallecidos
## 1 462
## 2 72
## 3 281
## 4 111
## 5 88
## 6 60select=c("pared10_Total", "techo9_Total", "piso8_Total", "agua10_Total")
data=data[, select]
library(magrittr)## Warning: package 'magrittr' was built under R version 4.3.3head(data,10)%>%
rmarkdown::paged_table()data$pared10_Total= as.numeric(data$pared10_Total)
data$techo9_Total= as.numeric(data$techo9_Total)
data$piso8_Total= as.numeric(data$piso8_Total)
data$agua10_Total= as.numeric(data$agua10_Total)str(data)## 'data.frame': 196 obs. of 4 variables:
## $ pared10_Total: num 19520 7308 13999 9716 12757 ...
## $ techo9_Total : num 19520 7308 13999 9716 12757 ...
## $ piso8_Total : num 19520 7308 13999 9716 12757 ...
## $ agua10_Total : num 19520 7308 13999 9716 12757 ...library(polycor)## Warning: package 'polycor' was built under R version 4.3.3corMatrix=polycor::hetcor(data)$correlations## Warning in hetcor.data.frame(data): the correlation matrix has been adjusted to
## make it positive-definiteround(corMatrix,2)## pared10_Total techo9_Total piso8_Total agua10_Total
## pared10_Total 1 1 1 1
## techo9_Total 1 1 1 1
## piso8_Total 1 1 1 1
## agua10_Total 1 1 1 1library(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.3.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.3ggcorrplot(corMatrix)
library(psych)## Warning: package 'psych' was built under R version 4.3.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:polycor':
##
## polyserialCORTEST
library(psych)psych::KMO(corMatrix) ## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.9
## MSA for each item =
## pared10_Total techo9_Total piso8_Total agua10_Total
## 0.9 0.9 0.9 0.9cortest.bartlett(corMatrix,n=nrow(data))$p.value>0.05## [1] FALSElibrary(matrixcalc)
is.singular.matrix(corMatrix)## [1] TRUElibrary(matrixcalc)
is.singular.matrix(corMatrix)## [1] TRUEfa.parallel(data, fa = 'fa',correct = T,plot = F)## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used## Parallel analysis suggests that the number of factors = 1 and the number of components = NAlibrary(GPArotation)## Warning: package 'GPArotation' was built under R version 4.3.3##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, variminresfa <- fa(data,
nfactors = 1,
cor = 'mixed',
rotate = "varimax", #oblimin?
fm="minres")## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is usedprint(resfa$loadings)##
## Loadings:
## MR1
## pared10_Total 0.999
## techo9_Total 0.999
## piso8_Total 0.999
## agua10_Total 0.999
##
## MR1
## SS loadings 3.995
## Proportion Var 0.999sort(resfa$communality)## pared10_Total techo9_Total piso8_Total agua10_Total
## 0.99875 0.99875 0.99875 0.99875regresFactors=as.data.frame(resfa$scores)%>%head()
data$confianza_instituciones <- resfa$scores
head(data)## pared10_Total techo9_Total piso8_Total agua10_Total MR1
## 1 19520 19520 19520 19520 -0.1245042
## 2 7308 7308 7308 7308 -0.2014495
## 3 13999 13999 13999 13999 -0.1592909
## 4 9716 9716 9716 9716 -0.1862772
## 5 12757 12757 12757 12757 -0.1671165
## 6 8904 8904 8904 8904 -0.1913934