library(rio)
datalimpia=import("dataOK_all.xlsx")
## New names:
## • `` -> `...1`
str(datalimpia)
## '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 ...
names(datalimpia)
## [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"
df_seleccionado <- datalimpia[, 3:40]
df_seleccionado <- df_seleccionado[, -c(1,10,11,19,20,27,28,36,38)]
names(df_seleccionado )
## [1] "pared1_Ladrillo" "pared2_Piedra"
## [3] "pared3_Adobe" "pared4_Tapia"
## [5] "pared5_Quincha" "pared6_Piedra"
## [7] "pared7_Madera" "pared8_Triplay"
## [9] "techo1_Concreto" "techo2_Madera"
## [11] "techo3_Tejas" "techo4_Planchas"
## [13] "techo5_Caña" "techo6_Triplay"
## [15] "techo7_Paja" "piso1_Parquet"
## [17] "piso2_Láminas" "piso3_Losetas"
## [19] "piso4_Madera" "piso5_Cemento"
## [21] "piso6_Tierra" "agua1_Red"
## [23] "agua2_Red_fueraVivienda" "agua3_Pilón"
## [25] "agua4_Camión" "agua5_Pozo"
## [27] "agua6_Manantial" "agua7_Río"
## [29] "agua9_Vecino"
library(polycor)
corMatrix=polycor::hetcor(df_seleccionado)$correlations
round(corMatrix,2)
## pared1_Ladrillo pared2_Piedra pared3_Adobe pared4_Tapia
## pared1_Ladrillo 1.00 0.65 0.35 -0.03
## pared2_Piedra 0.65 1.00 0.20 -0.05
## pared3_Adobe 0.35 0.20 1.00 0.00
## pared4_Tapia -0.03 -0.05 0.00 1.00
## pared5_Quincha 0.22 0.14 0.15 -0.11
## pared6_Piedra 0.07 0.10 0.18 0.06
## pared7_Madera 0.89 0.50 0.16 -0.09
## pared8_Triplay 0.85 0.50 0.27 -0.09
## techo1_Concreto 1.00 0.65 0.35 -0.02
## techo2_Madera 0.96 0.55 0.26 -0.04
## techo3_Tejas 0.13 0.04 0.45 0.41
## techo4_Planchas 0.92 0.62 0.46 -0.02
## techo5_Caña 0.44 0.24 0.38 -0.13
## techo6_Triplay 0.84 0.49 0.29 -0.10
## techo7_Paja 0.04 0.02 0.08 -0.12
## piso1_Parquet 0.99 0.59 0.31 -0.02
## piso2_Láminas 0.99 0.69 0.30 -0.03
## piso3_Losetas 1.00 0.62 0.36 -0.03
## piso4_Madera 0.57 0.32 0.12 -0.02
## piso5_Cemento 1.00 0.67 0.38 -0.03
## piso6_Tierra 0.69 0.45 0.72 0.14
## agua1_Red 1.00 0.64 0.39 -0.02
## agua2_Red_fueraVivienda 0.99 0.59 0.40 0.01
## agua3_Pilón 0.91 0.84 0.38 -0.02
## agua4_Camión 0.99 0.61 0.33 -0.06
## agua5_Pozo 0.37 0.25 0.45 0.02
## agua6_Manantial -0.05 -0.03 0.27 0.24
## agua7_Río 0.01 0.00 0.06 -0.06
## agua9_Vecino 0.94 0.55 0.41 -0.03
## pared5_Quincha pared6_Piedra pared7_Madera
## pared1_Ladrillo 0.22 0.07 0.89
## pared2_Piedra 0.14 0.10 0.50
## pared3_Adobe 0.15 0.18 0.16
## pared4_Tapia -0.11 0.06 -0.09
## pared5_Quincha 1.00 -0.03 0.17
## pared6_Piedra -0.03 1.00 0.04
## pared7_Madera 0.17 0.04 1.00
## pared8_Triplay 0.45 0.04 0.74
## techo1_Concreto 0.20 0.07 0.88
## techo2_Madera 0.18 0.05 0.94
## techo3_Tejas -0.05 0.00 0.03
## techo4_Planchas 0.43 0.12 0.87
## techo5_Caña 0.16 -0.05 0.31
## techo6_Triplay 0.25 0.03 0.73
## techo7_Paja -0.06 0.26 0.24
## piso1_Parquet 0.17 0.07 0.89
## piso2_Láminas 0.18 0.07 0.88
## piso3_Losetas 0.22 0.06 0.89
## piso4_Madera 0.04 0.02 0.76
## piso5_Cemento 0.24 0.07 0.89
## piso6_Tierra 0.50 0.23 0.58
## agua1_Red 0.24 0.07 0.89
## agua2_Red_fueraVivienda 0.21 0.10 0.90
## agua3_Pilón 0.28 0.11 0.79
## agua4_Camión 0.27 0.06 0.88
## agua5_Pozo 0.07 0.24 0.45
## agua6_Manantial -0.04 0.39 0.00
## agua7_Río 0.47 0.10 0.19
## agua9_Vecino 0.45 0.06 0.86
## pared8_Triplay techo1_Concreto techo2_Madera
## pared1_Ladrillo 0.85 1.00 0.96
## pared2_Piedra 0.50 0.65 0.55
## pared3_Adobe 0.27 0.35 0.26
## pared4_Tapia -0.09 -0.02 -0.04
## pared5_Quincha 0.45 0.20 0.18
## pared6_Piedra 0.04 0.07 0.05
## pared7_Madera 0.74 0.88 0.94
## pared8_Triplay 1.00 0.84 0.79
## techo1_Concreto 0.84 1.00 0.96
## techo2_Madera 0.79 0.96 1.00
## techo3_Tejas 0.04 0.13 0.10
## techo4_Planchas 0.85 0.91 0.88
## techo5_Caña 0.51 0.44 0.39
## techo6_Triplay 0.93 0.83 0.80
## techo7_Paja 0.01 0.04 0.08
## piso1_Parquet 0.81 0.99 0.96
## piso2_Láminas 0.81 0.99 0.96
## piso3_Losetas 0.85 1.00 0.97
## piso4_Madera 0.47 0.56 0.61
## piso5_Cemento 0.85 0.99 0.96
## piso6_Tierra 0.72 0.68 0.61
## agua1_Red 0.85 1.00 0.96
## agua2_Red_fueraVivienda 0.82 0.99 0.96
## agua3_Pilón 0.79 0.91 0.86
## agua4_Camión 0.89 0.99 0.95
## agua5_Pozo 0.29 0.37 0.36
## agua6_Manantial -0.08 -0.05 -0.05
## agua7_Río 0.22 -0.02 0.00
## agua9_Vecino 0.88 0.93 0.90
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay
## pared1_Ladrillo 0.13 0.92 0.44 0.84
## pared2_Piedra 0.04 0.62 0.24 0.49
## pared3_Adobe 0.45 0.46 0.38 0.29
## pared4_Tapia 0.41 -0.02 -0.13 -0.10
## pared5_Quincha -0.05 0.43 0.16 0.25
## pared6_Piedra 0.00 0.12 -0.05 0.03
## pared7_Madera 0.03 0.87 0.31 0.73
## pared8_Triplay 0.04 0.85 0.51 0.93
## techo1_Concreto 0.13 0.91 0.44 0.83
## techo2_Madera 0.10 0.88 0.39 0.80
## techo3_Tejas 1.00 0.09 -0.05 0.03
## techo4_Planchas 0.09 1.00 0.37 0.76
## techo5_Caña -0.05 0.37 1.00 0.66
## techo6_Triplay 0.03 0.76 0.66 1.00
## techo7_Paja -0.13 0.13 -0.11 -0.02
## piso1_Parquet 0.13 0.89 0.38 0.80
## piso2_Láminas 0.12 0.89 0.38 0.80
## piso3_Losetas 0.14 0.92 0.44 0.84
## piso4_Madera 0.16 0.63 0.14 0.43
## piso5_Cemento 0.13 0.93 0.47 0.85
## piso6_Tierra 0.32 0.84 0.36 0.62
## agua1_Red 0.16 0.93 0.46 0.84
## agua2_Red_fueraVivienda 0.19 0.92 0.40 0.81
## agua3_Pilón 0.11 0.90 0.38 0.76
## agua4_Camión 0.10 0.91 0.46 0.88
## agua5_Pozo 0.04 0.51 0.15 0.26
## agua6_Manantial 0.19 0.06 -0.13 -0.10
## agua7_Río -0.10 0.27 -0.03 0.04
## agua9_Vecino 0.13 0.96 0.45 0.81
## techo7_Paja piso1_Parquet piso2_Láminas piso3_Losetas
## pared1_Ladrillo 0.04 0.99 0.99 1.00
## pared2_Piedra 0.02 0.59 0.69 0.62
## pared3_Adobe 0.08 0.31 0.30 0.36
## pared4_Tapia -0.12 -0.02 -0.03 -0.03
## pared5_Quincha -0.06 0.17 0.18 0.22
## pared6_Piedra 0.26 0.07 0.07 0.06
## pared7_Madera 0.24 0.89 0.88 0.89
## pared8_Triplay 0.01 0.81 0.81 0.85
## techo1_Concreto 0.04 0.99 0.99 1.00
## techo2_Madera 0.08 0.96 0.96 0.97
## techo3_Tejas -0.13 0.13 0.12 0.14
## techo4_Planchas 0.13 0.89 0.89 0.92
## techo5_Caña -0.11 0.38 0.38 0.44
## techo6_Triplay -0.02 0.80 0.80 0.84
## techo7_Paja 1.00 0.05 0.05 0.04
## piso1_Parquet 0.05 1.00 0.99 0.99
## piso2_Láminas 0.05 0.99 1.00 0.99
## piso3_Losetas 0.04 0.99 0.99 1.00
## piso4_Madera 0.34 0.57 0.56 0.57
## piso5_Cemento 0.04 0.98 0.98 0.99
## piso6_Tierra 0.20 0.63 0.64 0.68
## agua1_Red 0.04 0.98 0.99 1.00
## agua2_Red_fueraVivienda 0.06 0.99 0.98 0.99
## agua3_Pilón 0.08 0.87 0.92 0.90
## agua4_Camión 0.03 0.98 0.98 0.99
## agua5_Pozo 0.51 0.35 0.35 0.35
## agua6_Manantial 0.33 -0.04 -0.05 -0.06
## agua7_Río 0.43 -0.03 -0.03 0.01
## agua9_Vecino 0.06 0.91 0.91 0.94
## piso4_Madera piso5_Cemento piso6_Tierra agua1_Red
## pared1_Ladrillo 0.57 1.00 0.69 1.00
## pared2_Piedra 0.32 0.67 0.45 0.64
## pared3_Adobe 0.12 0.38 0.72 0.39
## pared4_Tapia -0.02 -0.03 0.14 -0.02
## pared5_Quincha 0.04 0.24 0.50 0.24
## pared6_Piedra 0.02 0.07 0.23 0.07
## pared7_Madera 0.76 0.89 0.58 0.89
## pared8_Triplay 0.47 0.85 0.72 0.85
## techo1_Concreto 0.56 0.99 0.68 1.00
## techo2_Madera 0.61 0.96 0.61 0.96
## techo3_Tejas 0.16 0.13 0.32 0.16
## techo4_Planchas 0.63 0.93 0.84 0.93
## techo5_Caña 0.14 0.47 0.36 0.46
## techo6_Triplay 0.43 0.85 0.62 0.84
## techo7_Paja 0.34 0.04 0.20 0.04
## piso1_Parquet 0.57 0.98 0.63 0.98
## piso2_Láminas 0.56 0.98 0.64 0.99
## piso3_Losetas 0.57 0.99 0.68 1.00
## piso4_Madera 1.00 0.58 0.38 0.58
## piso5_Cemento 0.58 1.00 0.71 1.00
## piso6_Tierra 0.38 0.71 1.00 0.72
## agua1_Red 0.58 1.00 0.72 1.00
## agua2_Red_fueraVivienda 0.59 0.98 0.72 0.99
## agua3_Pilón 0.51 0.92 0.73 0.91
## agua4_Camión 0.56 0.98 0.69 0.99
## agua5_Pozo 0.43 0.40 0.59 0.38
## agua6_Manantial -0.01 -0.05 0.30 -0.05
## agua7_Río 0.35 0.02 0.37 0.02
## agua9_Vecino 0.59 0.94 0.79 0.95
## agua2_Red_fueraVivienda agua3_Pilón agua4_Camión
## pared1_Ladrillo 0.99 0.91 0.99
## pared2_Piedra 0.59 0.84 0.61
## pared3_Adobe 0.40 0.38 0.33
## pared4_Tapia 0.01 -0.02 -0.06
## pared5_Quincha 0.21 0.28 0.27
## pared6_Piedra 0.10 0.11 0.06
## pared7_Madera 0.90 0.79 0.88
## pared8_Triplay 0.82 0.79 0.89
## techo1_Concreto 0.99 0.91 0.99
## techo2_Madera 0.96 0.86 0.95
## techo3_Tejas 0.19 0.11 0.10
## techo4_Planchas 0.92 0.90 0.91
## techo5_Caña 0.40 0.38 0.46
## techo6_Triplay 0.81 0.76 0.88
## techo7_Paja 0.06 0.08 0.03
## piso1_Parquet 0.99 0.87 0.98
## piso2_Láminas 0.98 0.92 0.98
## piso3_Losetas 0.99 0.90 0.99
## piso4_Madera 0.59 0.51 0.56
## piso5_Cemento 0.98 0.92 0.98
## piso6_Tierra 0.72 0.73 0.69
## agua1_Red 0.99 0.91 0.99
## agua2_Red_fueraVivienda 1.00 0.88 0.97
## agua3_Pilón 0.88 1.00 0.89
## agua4_Camión 0.97 0.89 1.00
## agua5_Pozo 0.39 0.40 0.34
## agua6_Manantial 0.03 0.01 -0.07
## agua7_Río 0.01 0.10 0.03
## agua9_Vecino 0.93 0.86 0.94
## agua5_Pozo agua6_Manantial agua7_Río agua9_Vecino
## pared1_Ladrillo 0.37 -0.05 0.01 0.94
## pared2_Piedra 0.25 -0.03 0.00 0.55
## pared3_Adobe 0.45 0.27 0.06 0.41
## pared4_Tapia 0.02 0.24 -0.06 -0.03
## pared5_Quincha 0.07 -0.04 0.47 0.45
## pared6_Piedra 0.24 0.39 0.10 0.06
## pared7_Madera 0.45 0.00 0.19 0.86
## pared8_Triplay 0.29 -0.08 0.22 0.88
## techo1_Concreto 0.37 -0.05 -0.02 0.93
## techo2_Madera 0.36 -0.05 0.00 0.90
## techo3_Tejas 0.04 0.19 -0.10 0.13
## techo4_Planchas 0.51 0.06 0.27 0.96
## techo5_Caña 0.15 -0.13 -0.03 0.45
## techo6_Triplay 0.26 -0.10 0.04 0.81
## techo7_Paja 0.51 0.33 0.43 0.06
## piso1_Parquet 0.35 -0.04 -0.03 0.91
## piso2_Láminas 0.35 -0.05 -0.03 0.91
## piso3_Losetas 0.35 -0.06 0.01 0.94
## piso4_Madera 0.43 -0.01 0.35 0.59
## piso5_Cemento 0.40 -0.05 0.02 0.94
## piso6_Tierra 0.59 0.30 0.37 0.79
## agua1_Red 0.38 -0.05 0.02 0.95
## agua2_Red_fueraVivienda 0.39 0.03 0.01 0.93
## agua3_Pilón 0.40 0.01 0.10 0.86
## agua4_Camión 0.34 -0.07 0.03 0.94
## agua5_Pozo 1.00 0.29 0.27 0.42
## agua6_Manantial 0.29 1.00 0.31 -0.06
## agua7_Río 0.27 0.31 1.00 0.16
## agua9_Vecino 0.42 -0.06 0.16 1.00
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:polycor':
##
## polyserial
psych::KMO(corMatrix)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.43
## MSA for each item =
## pared1_Ladrillo pared2_Piedra pared3_Adobe
## 0.49 0.29 0.16
## pared4_Tapia pared5_Quincha pared6_Piedra
## 0.02 0.10 0.03
## pared7_Madera pared8_Triplay techo1_Concreto
## 0.44 0.43 0.49
## techo2_Madera techo3_Tejas techo4_Planchas
## 0.47 0.05 0.47
## techo5_Caña techo6_Triplay techo7_Paja
## 0.18 0.42 0.06
## piso1_Parquet piso2_Láminas piso3_Losetas
## 0.74 0.74 0.75
## piso4_Madera piso5_Cemento piso6_Tierra
## 0.54 0.75 0.65
## agua1_Red agua2_Red_fueraVivienda agua3_Pilón
## 0.59 0.59 0.54
## agua4_Camión agua5_Pozo agua6_Manantial
## 0.59 0.25 0.07
## agua7_Río agua9_Vecino
## 0.10 0.60
cortest.bartlett(corMatrix,n=nrow(df_seleccionado))$p.value>0.05
## [1] FALSE
library(matrixcalc)
is.singular.matrix(corMatrix)
## [1] TRUE
fa.parallel(df_seleccionado, fa = 'fa',correct = T,plot = F)
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Parallel analysis suggests that the number of factors = 4 and the number of components = NA
library(GPArotation)
##
## Attaching package: 'GPArotation'
## The following objects are masked from 'package:psych':
##
## equamax, varimin
resfa <- fa(df_seleccionado,
nfactors = 4,
cor = 'mixed',
rotate = "varimax", #?
fm="minres")
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
print(resfa$loadings)
##
## Loadings:
## MR1 MR2 MR4 MR3
## pared1_Ladrillo 0.991
## pared2_Piedra 0.631
## pared3_Adobe 0.274 0.151 0.299 0.639
## pared4_Tapia -0.116 0.379
## pared5_Quincha 0.165 0.735
## pared6_Piedra 0.321 0.209
## pared7_Madera 0.905 0.317 -0.162
## pared8_Triplay 0.827 0.448
## techo1_Concreto 0.991
## techo2_Madera 0.970
## techo3_Tejas 0.591
## techo4_Planchas 0.882 0.256 0.335
## techo5_Caña 0.429 -0.179 0.280
## techo6_Triplay 0.833 0.286
## techo7_Paja 0.767 -0.115
## piso1_Parquet 0.985
## piso2_Láminas 0.990
## piso3_Losetas 0.989
## piso4_Madera 0.584 0.426 -0.125
## piso5_Cemento 0.987 0.112
## piso6_Tierra 0.592 0.346 0.560 0.502
## agua1_Red 0.985 0.110 0.119
## agua2_Red_fueraVivienda 0.970 0.165
## agua3_Pilón 0.891 0.107 0.176 0.119
## agua4_Camión 0.980 0.149
## agua5_Pozo 0.331 0.588 0.207
## agua6_Manantial -0.115 0.499 0.395
## agua7_Río 0.608 0.515 -0.196
## agua9_Vecino 0.908 0.339
##
## MR1 MR2 MR4 MR3
## SS loadings 15.787 2.237 1.929 1.621
## Proportion Var 0.544 0.077 0.067 0.056
## Cumulative Var 0.544 0.621 0.688 0.744
library(GPArotation)
resfa <- fa(df_seleccionado,
nfactors = 4,
cor = 'mixed',
rotate = "oblimin", #?
fm="minres")
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
print(resfa$loadings)
##
## Loadings:
## MR1 MR3 MR2 MR4
## pared1_Ladrillo 0.998
## pared2_Piedra 0.634
## pared3_Adobe 0.732
## pared4_Tapia 0.366 -0.250
## pared5_Quincha 0.738
## pared6_Piedra 0.267 0.268
## pared7_Madera 0.956 -0.191 0.305
## pared8_Triplay 0.777 0.393
## techo1_Concreto 1.003
## techo2_Madera 1.009
## techo3_Tejas 0.580 -0.133 -0.283
## techo4_Planchas 0.814 0.151 0.142 0.215
## techo5_Caña 0.388 -0.245 0.224
## techo6_Triplay 0.815 -0.166 0.218
## techo7_Paja 0.779
## piso1_Parquet 1.014 -0.110
## piso2_Láminas 1.019
## piso3_Losetas 0.996
## piso4_Madera 0.615 -0.114 0.423
## piso5_Cemento 0.985
## piso6_Tierra 0.383 0.664 0.124 0.303
## agua1_Red 0.980
## agua2_Red_fueraVivienda 0.963 0.116
## agua3_Pilón 0.863 0.108
## agua4_Camión 0.984
## agua5_Pozo 0.250 0.311 0.500
## agua6_Manantial -0.229 0.509 0.402 -0.122
## agua7_Río -0.161 0.564 0.563
## agua9_Vecino 0.856 0.232
##
## MR1 MR3 MR2 MR4
## SS loadings 15.464 2.009 1.852 1.523
## Proportion Var 0.533 0.069 0.064 0.053
## Cumulative Var 0.533 0.603 0.666 0.719
df_seleccionado2 <- datalimpia[, 41:50]
names(df_seleccionado2)
## [1] "elec1_Sí" "elec2_No" "elec3_Total" "departamento"
## [5] "provincia" "Castillo" "Keiko" "ganaCastillo"
## [9] "countPositivos" "countFallecidos"
df_seleccionado2 <- df_seleccionado2[df_seleccionado2$provincia != "LIMA", ]
porcentaje_electricidad <- (df_seleccionado2$elec1_Sí / df_seleccionado2$elec3_Total) * 100
df_seleccionado2$porcentaje_elc <- porcentaje_electricidad
tasa_fallecidos_por_1000 <- (df_seleccionado2$countFallecidos / df_seleccionado2$countPositivos) * 1000
df_seleccionado2$tasa_fallecidos_por_1000 <- tasa_fallecidos_por_1000
razon_votacion_castillo_keiko <- df_seleccionado2$Castillo / df_seleccionado2$Keiko
df_seleccionado2$razon_votacion_castillo_keiko <- razon_votacion_castillo_keiko
cor(df_seleccionado2[,c(11:13)])
## porcentaje_elc tasa_fallecidos_por_1000
## porcentaje_elc 1.00000000 0.09436612
## tasa_fallecidos_por_1000 0.09436612 1.00000000
## razon_votacion_castillo_keiko -0.31187963 0.15322775
## razon_votacion_castillo_keiko
## porcentaje_elc -0.3118796
## tasa_fallecidos_por_1000 0.1532277
## razon_votacion_castillo_keiko 1.0000000
dataClus=df_seleccionado2[,c(11:13)]
row.names(dataClus)=df_seleccionado2$provincia
library(cluster)
g.dist = daisy(dataClus, metric="gower")
library(factoextra)
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
fviz_nbclust(dataClus, hcut,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F,hc_func = "agnes")
