Practica_5
Miguel Ibañez Ureña
2022-12-12
library(haven)
comaut <- read_sav("https://www.uv.es/mlejarza/datamine/comaut.sav")
View(comaut)
names(comaut)## [1] "Cautonoma" "proppobla"
## [3] "propSuperf" "ratioPobsup"
## [5] "densidad" "Ventasaotras"
## [7] "Comprasdeotras" "autoabas"
## [9] "maxventa" "maxcompra"
## [11] "ratioVentasCompras" "proporcionvtasT"
## [13] "proporcioncomprasT" "ratiomaximaventaTotal"
## [15] "ratiomaxcompraTotal" "Gastoporhogar"
## [17] "Gastoporpersona" "Gastoporunidadconsumo"
## [19] "indicegastopersona" "indicegastoporhoga"
## [21] "indicegastounidadconsumo" "pibpercap"
## [23] "templeoAgri" "tempIndus"
## [25] "tempConstr" "tempServ"
## [27] "tbuscandoempleo"data<-comautProcedemos a quitar las varibles que no queremos También añadimos a las filas el nombre de la comunidad autonóma
data <- (comaut[,c(-1,-5,-6,-7,-9, -10, -14, -15, -16, -17, -18)])
row.names(data)<-comaut$Cautonoma## Warning: Setting row names on a tibble is deprecated.Comprobamos que estan las variables con las que queremos trabajar
names(data)## [1] "proppobla" "propSuperf"
## [3] "ratioPobsup" "autoabas"
## [5] "ratioVentasCompras" "proporcionvtasT"
## [7] "proporcioncomprasT" "indicegastopersona"
## [9] "indicegastoporhoga" "indicegastounidadconsumo"
## [11] "pibpercap" "templeoAgri"
## [13] "tempIndus" "tempConstr"
## [15] "tempServ" "tbuscandoempleo"data## # A tibble: 17 × 16
## proppobla propSuperf ratioP…¹ autoa…² ratio…³ propo…⁴ propo…⁵ indic…⁶ indic…⁷
## * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.179 0.173 1.03 26401. 0.556 0.112 0.113 89.9 99.2
## 2 0.0281 0.0943 0.298 8463. 0.476 0.0550 0.0646 100. 98.7
## 3 0.0218 0.021 1.04 5573. 0.540 0.0249 0.0257 100. 98.5
## 4 0.0244 0.0099 2.46 3707. 0.0809 0.00287 0.0198 105. 105.
## 5 0.0458 0.0147 3.12 6409. 0.283 0.0159 0.0314 87.1 95.3
## 6 0.0124 0.0105 1.18 2393. 0.457 0.0156 0.0192 90.9 94.5
## 7 0.051 0.186 0.274 12813. 0.698 0.0982 0.0787 94.1 92.2
## 8 0.0432 0.157 0.275 6592. 0.740 0.0673 0.0509 84.1 90.2
## 9 0.163 0.0635 2.57 48856. 0.577 0.178 0.172 113. 114.
## 10 0.106 0.046 2.31 20607. 0.451 0.0818 0.101 101. 101.
## 11 0.0227 0.0823 0.276 3892. 0.406 0.0155 0.0213 80.6 86.2
## 12 0.0574 0.0584 0.983 15424. 0.635 0.0619 0.0545 94.8 99.8
## 13 0.142 0.0159 8.91 12013. 0.692 0.110 0.0893 114. 120.
## 14 0.0318 0.0224 1.42 4861. 0.709 0.0374 0.0295 88.3 99.2
## 15 0.0139 0.0205 0.678 4119. 0.743 0.0339 0.0255 109. 111.
## 16 0.0469 0.0143 3.28 16712. 0.466 0.0711 0.0853 112. 109.
## 17 0.0067 0.01 0.67 1837. 0.705 0.0181 0.0143 87.7 86.6
## # … with 7 more variables: indicegastounidadconsumo <dbl>, pibpercap <dbl>,
## # templeoAgri <dbl>, tempIndus <dbl>, tempConstr <dbl>, tempServ <dbl>,
## # tbuscandoempleo <dbl>, and abbreviated variable names ¹ratioPobsup,
## # ²autoabas, ³ratioVentasCompras, ⁴proporcionvtasT, ⁵proporcioncomprasT,
## # ⁶indicegastopersona, ⁷indicegastoporhogaPasamos a ver la matriz de correlación y el gráfico.
library(corrplot)## Warning: package 'corrplot' was built under R version 4.2.2## corrplot 0.92 loadedmatriz_correlaciones <- cor(data, use = "pairwise.complete.obs")
matriz_correlaciones## proppobla propSuperf ratioPobsup autoabas
## proppobla 1.0000000 0.32877511 0.471492812 0.828041864
## propSuperf 0.3287751 1.00000000 -0.404260746 0.278640612
## ratioPobsup 0.4714928 -0.40426075 1.000000000 0.181662407
## autoabas 0.8280419 0.27864061 0.181662407 1.000000000
## ratioVentasCompras 0.1153052 0.28962282 -0.075678305 0.080373621
## proporcionvtasT 0.8602717 0.44741947 0.311642629 0.895774548
## proporcioncomprasT 0.8763673 0.37282083 0.306156670 0.949471701
## indicegastopersona 0.3388830 -0.34747989 0.570742876 0.426532386
## indicegastoporhoga 0.5075668 -0.34063883 0.711988019 0.459046865
## indicegastounidadconsumo 0.4077782 -0.35181772 0.633777591 0.446815316
## pibpercap 0.1099287 -0.42379896 0.573723549 0.180175588
## templeoAgri -0.1298541 0.42214313 -0.536843672 -0.203083034
## tempIndus -0.2833297 -0.03462689 -0.382041628 0.029191957
## tempConstr -0.1956773 -0.06770467 -0.004306534 -0.247757222
## tempServ 0.3544701 -0.31814475 0.768044033 0.154412159
## tbuscandoempleo 0.1805242 0.29472546 -0.114740656 0.009297234
## ratioVentasCompras proporcionvtasT proporcioncomprasT
## proppobla 0.11530521 0.86027168 0.87636734
## propSuperf 0.28962282 0.44741947 0.37282083
## ratioPobsup -0.07567830 0.31164263 0.30615667
## autoabas 0.08037362 0.89577455 0.94947170
## ratioVentasCompras 1.00000000 0.36935527 0.15184148
## proporcionvtasT 0.36935527 1.00000000 0.96237911
## proporcioncomprasT 0.15184148 0.96237911 1.00000000
## indicegastopersona -0.03656886 0.44371342 0.48055286
## indicegastoporhoga 0.02902381 0.49948500 0.50895551
## indicegastounidadconsumo -0.02891074 0.47020401 0.50178361
## pibpercap 0.10967237 0.28109172 0.26350968
## templeoAgri 0.30980391 -0.16859877 -0.24363845
## tempIndus 0.54312032 0.08245129 0.03469018
## tempConstr -0.52092477 -0.30272440 -0.27573058
## tempServ -0.55679728 0.11810496 0.19276948
## tbuscandoempleo -0.19202712 -0.07234084 -0.02578355
## indicegastopersona indicegastoporhoga
## proppobla 0.33888300 0.50756675
## propSuperf -0.34747989 -0.34063883
## ratioPobsup 0.57074288 0.71198802
## autoabas 0.42653239 0.45904686
## ratioVentasCompras -0.03656886 0.02902381
## proporcionvtasT 0.44371342 0.49948500
## proporcioncomprasT 0.48055286 0.50895551
## indicegastopersona 1.00000000 0.91177333
## indicegastoporhoga 0.91177333 1.00000000
## indicegastounidadconsumo 0.98901798 0.95979309
## pibpercap 0.86724232 0.76099841
## templeoAgri -0.76181877 -0.59980893
## tempIndus 0.23414877 0.01343191
## tempConstr -0.06702298 -0.07837518
## tempServ 0.46191786 0.51619425
## tbuscandoempleo -0.60476865 -0.39738196
## indicegastounidadconsumo pibpercap templeoAgri
## proppobla 0.40777817 0.1099287 -0.12985409
## propSuperf -0.35181772 -0.4237990 0.42214313
## ratioPobsup 0.63377759 0.5737235 -0.53684367
## autoabas 0.44681532 0.1801756 -0.20308303
## ratioVentasCompras -0.02891074 0.1096724 0.30980391
## proporcionvtasT 0.47020401 0.2810917 -0.16859877
## proporcioncomprasT 0.50178361 0.2635097 -0.24363845
## indicegastopersona 0.98901798 0.8672423 -0.76181877
## indicegastoporhoga 0.95979309 0.7609984 -0.59980893
## indicegastounidadconsumo 1.00000000 0.8501106 -0.71965247
## pibpercap 0.85011058 1.0000000 -0.67376367
## templeoAgri -0.71965247 -0.6737637 1.00000000
## tempIndus 0.15344888 0.3838987 -0.07374602
## tempConstr -0.06340038 -0.0684760 -0.15900351
## tempServ 0.49534236 0.3096957 -0.66397265
## tbuscandoempleo -0.54377296 -0.7728311 0.42239864
## tempIndus tempConstr tempServ tbuscandoempleo
## proppobla -0.28332973 -0.195677263 0.35447014 0.180524162
## propSuperf -0.03462689 -0.067704673 -0.31814475 0.294725462
## ratioPobsup -0.38204163 -0.004306534 0.76804403 -0.114740656
## autoabas 0.02919196 -0.247757222 0.15441216 0.009297234
## ratioVentasCompras 0.54312032 -0.520924770 -0.55679728 -0.192027125
## proporcionvtasT 0.08245129 -0.302724399 0.11810496 -0.072340839
## proporcioncomprasT 0.03469018 -0.275730578 0.19276948 -0.025783553
## indicegastopersona 0.23414877 -0.067022983 0.46191786 -0.604768649
## indicegastoporhoga 0.01343191 -0.078375179 0.51619425 -0.397381957
## indicegastounidadconsumo 0.15344888 -0.063400384 0.49534236 -0.543772959
## pibpercap 0.38389868 -0.068476003 0.30969573 -0.772831090
## templeoAgri -0.07374602 -0.159003513 -0.66397265 0.422398643
## tempIndus 1.00000000 -0.376993821 -0.66821175 -0.622145476
## tempConstr -0.37699382 1.000000000 0.29838978 -0.196906474
## tempServ -0.66821175 0.298389784 1.00000000 0.083210939
## tbuscandoempleo -0.62214548 -0.196906474 0.08321094 1.000000000Gráfico de las correlaciones
corrplot(cor(data), order = "hclust", tl.col="black", tl.cex=1)
names(data)## [1] "proppobla" "propSuperf"
## [3] "ratioPobsup" "autoabas"
## [5] "ratioVentasCompras" "proporcionvtasT"
## [7] "proporcioncomprasT" "indicegastopersona"
## [9] "indicegastoporhoga" "indicegastounidadconsumo"
## [11] "pibpercap" "templeoAgri"
## [13] "tempIndus" "tempConstr"
## [15] "tempServ" "tbuscandoempleo"data <- (data[,c(-5, -6, -10,-11)])
names(data)## [1] "proppobla" "propSuperf" "ratioPobsup"
## [4] "autoabas" "proporcioncomprasT" "indicegastopersona"
## [7] "indicegastoporhoga" "templeoAgri" "tempIndus"
## [10] "tempConstr" "tempServ" "tbuscandoempleo"matriz_correlaciones <- cor(data, use = "pairwise.complete.obs")
matriz_correlaciones## proppobla propSuperf ratioPobsup autoabas
## proppobla 1.0000000 0.32877511 0.471492812 0.828041864
## propSuperf 0.3287751 1.00000000 -0.404260746 0.278640612
## ratioPobsup 0.4714928 -0.40426075 1.000000000 0.181662407
## autoabas 0.8280419 0.27864061 0.181662407 1.000000000
## proporcioncomprasT 0.8763673 0.37282083 0.306156670 0.949471701
## indicegastopersona 0.3388830 -0.34747989 0.570742876 0.426532386
## indicegastoporhoga 0.5075668 -0.34063883 0.711988019 0.459046865
## templeoAgri -0.1298541 0.42214313 -0.536843672 -0.203083034
## tempIndus -0.2833297 -0.03462689 -0.382041628 0.029191957
## tempConstr -0.1956773 -0.06770467 -0.004306534 -0.247757222
## tempServ 0.3544701 -0.31814475 0.768044033 0.154412159
## tbuscandoempleo 0.1805242 0.29472546 -0.114740656 0.009297234
## proporcioncomprasT indicegastopersona indicegastoporhoga
## proppobla 0.87636734 0.33888300 0.50756675
## propSuperf 0.37282083 -0.34747989 -0.34063883
## ratioPobsup 0.30615667 0.57074288 0.71198802
## autoabas 0.94947170 0.42653239 0.45904686
## proporcioncomprasT 1.00000000 0.48055286 0.50895551
## indicegastopersona 0.48055286 1.00000000 0.91177333
## indicegastoporhoga 0.50895551 0.91177333 1.00000000
## templeoAgri -0.24363845 -0.76181877 -0.59980893
## tempIndus 0.03469018 0.23414877 0.01343191
## tempConstr -0.27573058 -0.06702298 -0.07837518
## tempServ 0.19276948 0.46191786 0.51619425
## tbuscandoempleo -0.02578355 -0.60476865 -0.39738196
## templeoAgri tempIndus tempConstr tempServ
## proppobla -0.12985409 -0.28332973 -0.195677263 0.35447014
## propSuperf 0.42214313 -0.03462689 -0.067704673 -0.31814475
## ratioPobsup -0.53684367 -0.38204163 -0.004306534 0.76804403
## autoabas -0.20308303 0.02919196 -0.247757222 0.15441216
## proporcioncomprasT -0.24363845 0.03469018 -0.275730578 0.19276948
## indicegastopersona -0.76181877 0.23414877 -0.067022983 0.46191786
## indicegastoporhoga -0.59980893 0.01343191 -0.078375179 0.51619425
## templeoAgri 1.00000000 -0.07374602 -0.159003513 -0.66397265
## tempIndus -0.07374602 1.00000000 -0.376993821 -0.66821175
## tempConstr -0.15900351 -0.37699382 1.000000000 0.29838978
## tempServ -0.66397265 -0.66821175 0.298389784 1.00000000
## tbuscandoempleo 0.42239864 -0.62214548 -0.196906474 0.08321094
## tbuscandoempleo
## proppobla 0.180524162
## propSuperf 0.294725462
## ratioPobsup -0.114740656
## autoabas 0.009297234
## proporcioncomprasT -0.025783553
## indicegastopersona -0.604768649
## indicegastoporhoga -0.397381957
## templeoAgri 0.422398643
## tempIndus -0.622145476
## tempConstr -0.196906474
## tempServ 0.083210939
## tbuscandoempleo 1.000000000det(matriz_correlaciones)## [1] 1.170451e-11Determinar el numero de factores
library(parallel); library(nFactors)## Warning: package 'nFactors' was built under R version 4.2.2## Loading required package: lattice##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
##
## parallelev <- eigen(cor(data)) # Obtención de los autovalores
ap <- parallel(subject=nrow(data),var=ncol(data),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS,xlab = "Número de Componentes",ylab = "Autovalores",
main = "Solución por autovalores para determinar
el número de factores")
abline(h=1, lty=3, col=4)
procedimiento prcomp
mod1<-prcomp(data,scale. = TRUE) # scale=TRUE PARA A.FACTORIAL SOBRE CORRELACIONES
summary(mod1)[1] # desvtipica, prop.de varianza, y proporc acumulada de var explicada## $sdev
## [1] 2.16508909 1.65802537 1.51330883 1.01032009 0.68953115 0.60790303
## [7] 0.47604604 0.31997064 0.23702294 0.13527912 0.06314175 0.00482212library(psy)
library(factoextra)## Warning: package 'factoextra' was built under R version 4.2.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.2.2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBavpropios <- (mod1$sdev)^2 #valores propios (cuadrado de desvi tipica)
plot(mod1,type="lines",main = "Scree Plot")
abline(h=1, lty=3, col=4)
fviz_screeplot(mod1, addlabels = TRUE) #grafico de los 10 componentes principales con mayor varianza
mod2 <- prcomp(data, scale. = TRUE, rank. = 4)
summary(mod2)## Importance of first k=4 (out of 12) components:
## PC1 PC2 PC3 PC4
## Standard deviation 2.1651 1.6580 1.5133 1.01032
## Proportion of Variance 0.3906 0.2291 0.1908 0.08506
## Cumulative Proportion 0.3906 0.6197 0.8106 0.89563matriz_correlaciones2<- cor(data, mod2$x)
matriz_correlaciones2## PC1 PC2 PC3 PC4
## proppobla -0.67844964 0.6682333 -0.14521434 0.03024572
## propSuperf 0.23710485 0.7622721 0.02107029 0.39627029
## ratioPobsup -0.78090971 -0.2161576 -0.34635500 -0.25851763
## autoabas -0.63457253 0.6593559 0.18740211 0.12573727
## proporcioncomprasT -0.69211081 0.6670003 0.18499826 0.12455550
## indicegastopersona -0.87115358 -0.2620808 0.34081420 0.02550106
## indicegastoporhoga -0.90313793 -0.1417298 0.13569439 -0.09347242
## templeoAgri 0.72599913 0.4551806 -0.05691845 -0.11536147
## tempIndus 0.11546466 -0.1025685 0.97252519 -0.03689022
## tempConstr 0.04724864 -0.4060604 -0.40335914 0.78758300
## tempServ -0.68768304 -0.2561813 -0.63490685 0.02508686
## tbuscandoempleo 0.29910632 0.5080283 -0.65562887 -0.34593867fviz_contrib(mod1, choice = "var", axes = 1, top = 10)
fviz_contrib(mod1, choice = "var", axes = 2, top = 10)
fviz_contrib(mod1, choice = "var", axes = 3, top = 10)
fviz_contrib(mod1, choice = "var", axes = 4, top = 10)
podemos obtener las puntuaciones TIPIFICADAS tipificando las columnas de mod2$x
puntuaciones<-as.array(scale(mod2$x),dimnames=c(F1,F2,F3,F4))
sd(puntuaciones[,1])#lo comprobamos## [1] 1biplot(x = mod2, scale = 0, cex = 0.6, col = c("blue4", "brown3"))
ROTACIÓN VARIMAX
solucion rotada varimax
sol.rotada<-varimax(mod2$rotation)# aplicamos la función varimax a la solución obtenida
sol.rotada## $loadings
##
## Loadings:
## PC1 PC2 PC3 PC4
## proppobla 0.485 -0.174
## propSuperf 0.309 0.430 0.124 0.287
## ratioPobsup -0.278 -0.376 -0.215
## autoabas 0.516
## proporcioncomprasT 0.532
## indicegastopersona -0.475
## indicegastoporhoga -0.405 0.116 -0.123
## templeoAgri 0.422 -0.131
## tempIndus -0.192 0.608 -0.120
## tempConstr -0.148 0.845
## tempServ -0.200 -0.502
## tbuscandoempleo 0.409 -0.387 -0.312
##
## PC1 PC2 PC3 PC4
## SS loadings 1.000 1.000 1.000 1.000
## Proportion Var 0.083 0.083 0.083 0.083
## Cumulative Var 0.083 0.167 0.250 0.333
##
## $rotmat
## [,1] [,2] [,3] [,4]
## [1,] 0.80193980 -0.4813327 0.34165632 0.09209959
## [2,] 0.50577368 0.8349332 0.04625931 -0.21198978
## [3,] -0.31742052 0.1001700 0.92930605 -0.16000143
## [4,] -0.01815948 0.2473285 0.13236803 0.95967575sol.rotada$loadings # de nuevo son los coeficientes de T ( CP no tipificadas)##
## Loadings:
## PC1 PC2 PC3 PC4
## proppobla 0.485 -0.174
## propSuperf 0.309 0.430 0.124 0.287
## ratioPobsup -0.278 -0.376 -0.215
## autoabas 0.516
## proporcioncomprasT 0.532
## indicegastopersona -0.475
## indicegastoporhoga -0.405 0.116 -0.123
## templeoAgri 0.422 -0.131
## tempIndus -0.192 0.608 -0.120
## tempConstr -0.148 0.845
## tempServ -0.200 -0.502
## tbuscandoempleo 0.409 -0.387 -0.312
##
## PC1 PC2 PC3 PC4
## SS loadings 1.000 1.000 1.000 1.000
## Proportion Var 0.083 0.083 0.083 0.083
## Cumulative Var 0.083 0.167 0.250 0.333sol.rotada$rotmat # matriz de rotación de la solución## [,1] [,2] [,3] [,4]
## [1,] 0.80193980 -0.4813327 0.34165632 0.09209959
## [2,] 0.50577368 0.8349332 0.04625931 -0.21198978
## [3,] -0.31742052 0.1001700 0.92930605 -0.16000143
## [4,] -0.01815948 0.2473285 0.13236803 0.95967575para obtener las puntuaciones rotadas tendremos que multiplicar matricialmente la matriz de puntuaciones (sin rotar)por la matriza de rotación que antes hay que definir como matriz
mat.rotacion<-as.array(sol.rotada$rotmat)
punt.rotadas<-as.array(puntuaciones%*%mat.rotacion,dimnames=C(FR1,FR2,FR3,FR4))
punt.rotadas## [,1] [,2] [,3] [,4]
## [1,] 1.29311678 2.01113651 -0.52387924 0.1382764
## [2,] -0.12688179 0.02437965 1.15350042 0.2184912
## [3,] -0.10877475 -0.81407429 -0.25970503 -0.8698561
## [4,] -0.89983394 -0.83646350 -1.26836513 2.5707869
## [5,] 0.68628581 -0.74875045 -2.01947746 -1.0971457
## [6,] -0.13598655 -0.94759194 0.11670472 1.6618826
## [7,] 0.56371253 0.79879441 0.81397642 1.1079817
## [8,] 1.32703766 0.27054828 0.15687706 0.5575134
## [9,] -1.09866540 2.31890528 0.26369528 -0.2017761
## [10,] -0.22803274 0.66719894 -0.07846606 -0.5958250
## [11,] 1.73744030 -0.49489401 -0.37094454 -0.4359671
## [12,] 0.03683842 0.09470207 0.46981428 0.2916004
## [13,] -1.76365018 0.22532298 -1.80407499 -0.7229765
## [14,] 0.86967997 -0.62156430 0.04927999 -0.8188107
## [15,] -0.99175772 -0.79755167 1.53486360 -0.8655108
## [16,] -1.32826604 -0.02206066 0.47503043 -0.5396868
## [17,] 0.16773764 -1.12803730 1.29117024 -0.3989778para obtener la matriz de estructura e intentar explicar la solución rotada
matriz.correlaciones.rotada<-as.array(cor(data,punt.rotadas),dimnames=c(FR1,FR2,FR3,FR4))
matriz.correlaciones.rotada## [,1] [,2] [,3] [,4]
## proppobla -0.16055620 0.877424643 -0.331829599 -0.15188298
## propSuperf 0.56179678 0.622439513 0.188304822 0.23716308
## ratioPobsup -0.62093465 0.096767131 -0.632891304 -0.21877408
## autoabas -0.23717269 0.905829139 0.004493146 -0.10753819
## proporcioncomprasT -0.27866412 0.939373635 -0.017201892 -0.11520746
## indicegastopersona -0.93981078 0.240941278 0.010337424 -0.05473246
## indicegastoporhoga -0.83732020 0.306849068 -0.201390247 -0.16454789
## templeoAgri 0.83258793 -0.003635495 0.200933700 -0.13123197
## tempIndus -0.26731030 -0.052921000 0.933594938 -0.15863034
## tempConstr -0.05375172 -0.207388315 -0.273234560 0.91079457
## tempServ -0.47997323 0.059716229 -0.833504111 0.11663373
## tbuscandoempleo 0.71120473 0.128965220 -0.529378492 -0.30723664Análisis Factorial por máx. Verosimilitud
Conjunto de datos
data2 <- (comaut[,c(2, 3, 4, 8, 12, 13, 16, 18, 19, 22, 23, 26, 27)])
names(data2)## [1] "proppobla" "propSuperf" "ratioPobsup"
## [4] "autoabas" "proporcionvtasT" "proporcioncomprasT"
## [7] "Gastoporhogar" "Gastoporunidadconsumo" "indicegastopersona"
## [10] "pibpercap" "templeoAgri" "tempServ"
## [13] "tbuscandoempleo"row.names(data2)<-comaut$Cautonoma## Warning: Setting row names on a tibble is deprecated.corrplot(cor(data2), order = "hclust", tl.col="black", tl.cex=1)
mod3<-prcomp(data2,scale. = TRUE) # scale=TRUE PARA A.FACTORIAL SOBRE CORRELACIONES
summary(mod3)[1] # desvtipica, prop.de varianza, y proporc acumulada de var explicada## $sdev
## [1] 2.55804207 1.83096146 1.23733727 0.73314996 0.66765521 0.52935559
## [7] 0.37767826 0.28141533 0.20689250 0.17268763 0.10698462 0.05320285
## [13] 0.02775725ev <- eigen(cor(data2)) # Obtención de los autovalores
ap <- parallel(subject=nrow(data2),var=ncol(data2),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS,xlab = "Número de Componentes",ylab = "Autovalores",
main = "Solución por autovalores para determinar
el número de factores")
abline(h=1, lty=3, col=4)
fa <- factanal(~proppobla + autoabas + proporcionvtasT + proporcioncomprasT + ratioPobsup + tempServ + pibpercap +
Gastoporhogar + indicegastopersona + Gastoporunidadconsumo + propSuperf + templeoAgri + tbuscandoempleo,
factors = 3, rotation = "none", scores = "regression", data = data2)
fa #vemos los (componentes principales) resultados##
## Call:
## factanal(x = ~proppobla + autoabas + proporcionvtasT + proporcioncomprasT + ratioPobsup + tempServ + pibpercap + Gastoporhogar + indicegastopersona + Gastoporunidadconsumo + propSuperf + templeoAgri + tbuscandoempleo, factors = 3, data = data2, scores = "regression", rotation = "none")
##
## Uniquenesses:
## proppobla autoabas proporcionvtasT
## 0.102 0.097 0.068
## proporcioncomprasT ratioPobsup tempServ
## 0.005 0.434 0.706
## pibpercap Gastoporhogar indicegastopersona
## 0.204 0.005 0.005
## Gastoporunidadconsumo propSuperf templeoAgri
## 0.005 0.459 0.373
## tbuscandoempleo
## 0.418
##
## Loadings:
## Factor1 Factor2 Factor3
## proppobla 0.568 0.684 0.327
## autoabas 0.606 0.732
## proporcionvtasT 0.633 0.729
## proporcioncomprasT 0.663 0.744
## ratioPobsup 0.634 -0.141 0.380
## tempServ 0.477 -0.159 0.201
## pibpercap 0.787 -0.357 -0.219
## Gastoporhogar 0.957 -0.159 0.231
## indicegastopersona 0.959 -0.216 -0.173
## Gastoporunidadconsumo 0.978 -0.199
## propSuperf -0.221 0.697
## templeoAgri -0.666 0.282 0.322
## tbuscandoempleo -0.460 0.390 0.467
##
## Factor1 Factor2 Factor3
## SS loadings 6.276 3.090 0.755
## Proportion Var 0.483 0.238 0.058
## Cumulative Var 0.483 0.720 0.779
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 68.32 on 42 degrees of freedom.
## The p-value is 0.00629print(fa, digits = 2, cutoff = .3, sort = TRUE)##
## Call:
## factanal(x = ~proppobla + autoabas + proporcionvtasT + proporcioncomprasT + ratioPobsup + tempServ + pibpercap + Gastoporhogar + indicegastopersona + Gastoporunidadconsumo + propSuperf + templeoAgri + tbuscandoempleo, factors = 3, data = data2, scores = "regression", rotation = "none")
##
## Uniquenesses:
## proppobla autoabas proporcionvtasT
## 0.10 0.10 0.07
## proporcioncomprasT ratioPobsup tempServ
## 0.00 0.43 0.71
## pibpercap Gastoporhogar indicegastopersona
## 0.20 0.00 0.00
## Gastoporunidadconsumo propSuperf templeoAgri
## 0.00 0.46 0.37
## tbuscandoempleo
## 0.42
##
## Loadings:
## Factor1 Factor2 Factor3
## ratioPobsup 0.63 0.38
## pibpercap 0.79 -0.36
## Gastoporhogar 0.96
## indicegastopersona 0.96
## Gastoporunidadconsumo 0.98
## templeoAgri -0.67 0.32
## proppobla 0.57 0.68 0.33
## autoabas 0.61 0.73
## proporcionvtasT 0.63 0.73
## proporcioncomprasT 0.66 0.74
## propSuperf 0.70
## tempServ 0.48
## tbuscandoempleo -0.46 0.39 0.47
##
## Factor1 Factor2 Factor3
## SS loadings 6.28 3.09 0.76
## Proportion Var 0.48 0.24 0.06
## Cumulative Var 0.48 0.72 0.78
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 68.32 on 42 degrees of freedom.
## The p-value is 0.00629load <- fa$loadings[,1:2] #cargas de las variables en los dos primeros fractores
plot(load,type="n") # set up plot
text(load,labels=names(data),cex=.7) # añadimos nombres de las variables
print("puntuaciones factoriales Max.Veros.")## [1] "puntuaciones factoriales Max.Veros."fa$scores## Factor1 Factor2 Factor3
## Andalucía -0.08256729 1.82762151 1.25710457
## Aragón 0.12069006 -0.01607969 -1.14837291
## Principado De Asturias -0.11492990 -0.90555704 -0.74260415
## Illes Balears 0.41486115 -1.56913565 -0.33691505
## Canarias -0.80315366 -0.11334418 1.10768945
## Cantabria -0.73549537 -0.57561052 0.01537339
## Castilla y León -0.36938027 0.90780803 -1.39424315
## Castilla-La Mancha -1.05397625 0.71057983 0.24635621
## Cataluña 1.90758164 1.81137374 -0.37290145
## Comunidad Valenciana 0.44847020 0.85600314 -0.54668534
## Extremadura -1.54449788 0.22677094 0.22628243
## Galicia -0.16023429 0.06150513 0.41413452
## Comunidad de Madrid 1.80064341 -0.57891521 1.50583465
## Región de Murcia -0.59775047 -0.31100723 1.69346160
## Comunidad Foral De Navarra 0.81578117 -1.70351227 0.31254557
## País Vasco 1.16412804 -0.30489335 -0.98834048
## La Rioja -1.21017029 -0.32360715 -1.24871985library(psych)## Warning: package 'psych' was built under R version 4.2.2##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:psy':
##
## wkappapar(mfrow = c(1,1))
fa.diagram(fa$scores)
estas puntuaciones pueden ser guardadas en la base de datos como nuevas variables
ML<- fa(r = data2, nfactors = 3, rotate = "varimax", fm = "mle")
ml.none <- factanal(data2, factors = 3, rotation = "none", scores = "regression")
ml.none##
## Call:
## factanal(x = data2, factors = 3, scores = "regression", rotation = "none")
##
## Uniquenesses:
## proppobla propSuperf ratioPobsup
## 0.102 0.459 0.434
## autoabas proporcionvtasT proporcioncomprasT
## 0.097 0.068 0.005
## Gastoporhogar Gastoporunidadconsumo indicegastopersona
## 0.005 0.005 0.005
## pibpercap templeoAgri tempServ
## 0.204 0.373 0.706
## tbuscandoempleo
## 0.418
##
## Loadings:
## Factor1 Factor2 Factor3
## proppobla 0.568 0.684 0.327
## propSuperf -0.221 0.697
## ratioPobsup 0.634 -0.141 0.380
## autoabas 0.606 0.732
## proporcionvtasT 0.633 0.729
## proporcioncomprasT 0.663 0.744
## Gastoporhogar 0.957 -0.159 0.231
## Gastoporunidadconsumo 0.978 -0.199
## indicegastopersona 0.959 -0.216 -0.173
## pibpercap 0.787 -0.357 -0.219
## templeoAgri -0.666 0.282 0.322
## tempServ 0.477 -0.159 0.201
## tbuscandoempleo -0.460 0.390 0.467
##
## Factor1 Factor2 Factor3
## SS loadings 6.276 3.090 0.755
## Proportion Var 0.483 0.238 0.058
## Cumulative Var 0.483 0.720 0.779
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 68.32 on 42 degrees of freedom.
## The p-value is 0.00629ml.none$scores## Factor1 Factor2 Factor3
## Andalucía -0.08256729 1.82762151 1.25710457
## Aragón 0.12069006 -0.01607969 -1.14837291
## Principado De Asturias -0.11492990 -0.90555704 -0.74260415
## Illes Balears 0.41486115 -1.56913565 -0.33691505
## Canarias -0.80315366 -0.11334418 1.10768945
## Cantabria -0.73549537 -0.57561052 0.01537339
## Castilla y León -0.36938027 0.90780803 -1.39424315
## Castilla-La Mancha -1.05397625 0.71057983 0.24635621
## Cataluña 1.90758164 1.81137374 -0.37290145
## Comunidad Valenciana 0.44847020 0.85600314 -0.54668534
## Extremadura -1.54449788 0.22677094 0.22628243
## Galicia -0.16023429 0.06150513 0.41413452
## Comunidad de Madrid 1.80064341 -0.57891521 1.50583465
## Región de Murcia -0.59775047 -0.31100723 1.69346160
## Comunidad Foral De Navarra 0.81578117 -1.70351227 0.31254557
## País Vasco 1.16412804 -0.30489335 -0.98834048
## La Rioja -1.21017029 -0.32360715 -1.24871985ml.varimax <- factanal(data2, factors = 3, rotation = "varimax", scores = "regression")
ml.varimax##
## Call:
## factanal(x = data2, factors = 3, scores = "regression", rotation = "varimax")
##
## Uniquenesses:
## proppobla propSuperf ratioPobsup
## 0.102 0.459 0.434
## autoabas proporcionvtasT proporcioncomprasT
## 0.097 0.068 0.005
## Gastoporhogar Gastoporunidadconsumo indicegastopersona
## 0.005 0.005 0.005
## pibpercap templeoAgri tempServ
## 0.204 0.373 0.706
## tbuscandoempleo
## 0.418
##
## Loadings:
## Factor1 Factor2 Factor3
## proppobla 0.867 -0.112 0.366
## propSuperf 0.508 -0.330 -0.417
## ratioPobsup 0.172 0.186 0.709
## autoabas 0.932 0.138 0.121
## proporcionvtasT 0.942 0.120 0.177
## proporcioncomprasT 0.971 0.176 0.146
## Gastoporhogar 0.313 0.500 0.805
## Gastoporunidadconsumo 0.292 0.713 0.636
## indicegastopersona 0.270 0.800 0.532
## pibpercap 0.774 0.439
## templeoAgri -0.742 -0.266
## tempServ 0.218 0.489
## tbuscandoempleo 0.116 -0.751
##
## Factor1 Factor2 Factor3
## SS loadings 4.023 3.378 2.720
## Proportion Var 0.309 0.260 0.209
## Cumulative Var 0.309 0.569 0.779
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 68.32 on 42 degrees of freedom.
## The p-value is 0.00629ml.varimax$scores## Factor1 Factor2 Factor3
## Andalucía 1.54582412 -1.5692436 0.27428473
## Aragón 0.06257658 0.8755010 -0.75044458
## Principado De Asturias -0.83824540 0.7621868 -0.31799271
## Illes Balears -1.17555748 1.0506706 0.51182439
## Canarias -0.50106850 -1.2311222 0.34372520
## Cantabria -0.85683688 -0.2694880 -0.25636700
## Castilla y León 0.64493040 0.4092130 -1.52350933
## Castilla-La Mancha 0.11778560 -1.0858903 -0.69530558
## Cataluña 2.50772922 0.8161854 0.32269744
## Comunidad Valenciana 0.97525118 0.3565992 -0.39300618
## Extremadura -0.54096755 -1.2092129 -0.85630643
## Galicia -0.02926307 -0.4089656 0.18126263
## Comunidad de Madrid 0.32473883 0.2980530 2.37712150
## Región de Murcia -0.58660718 -1.4366107 0.95597334
## Comunidad Foral De Navarra -1.11330950 0.9017983 1.26981865
## País Vasco 0.30364682 1.5242327 0.09738732
## La Rioja -0.84062719 0.2160933 -1.54116340par(mfrow = c(1,2))
plot(ml.none$loadings[,1],
ml.none$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "No rotation")
text(ml.none$loadings[,1]-0.08,
ml.none$loadings[,2]+0.08,
colnames(data2),
col="blue")
abline(h = 0, v = 0)
plot(ml.varimax$loadings[,1],
ml.varimax$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "Varimax rotation")
text(ml.varimax$loadings[,1]-0.08,
ml.varimax$loadings[,2]+0.08,
colnames(data2),
col="blue")
abline(h = 0, v = 0)
analisis factorial por el método de Ejes Principales Principal Axis Factor Analysis
creamos un nuevo conjunto de datos con la variables a utilizar
data2 <- (comaut[,c(2, 3, 4, 8, 12, 13, 16, 18, 19, 22, 23, 26, 27)]) # mismo conjunto de datos que ML
names(data2) # ver las variables añadidas## [1] "proppobla" "propSuperf" "ratioPobsup"
## [4] "autoabas" "proporcionvtasT" "proporcioncomprasT"
## [7] "Gastoporhogar" "Gastoporunidadconsumo" "indicegastopersona"
## [10] "pibpercap" "templeoAgri" "tempServ"
## [13] "tbuscandoempleo"row.names(data2)<-comaut$Cautonoma # nombrar filas ## Warning: Setting row names on a tibble is deprecated.extraemos los factores por el método de ejes princpales
mod4<-prcomp(data2,scale. = TRUE) # scale=TRUE PARA A.FACTORIAL SOBRE CORRELACIONES
summary(mod4)[1] # desvtipica, prop.de varianza, y proporc acumulada de var explicada## $sdev
## [1] 2.55804207 1.83096146 1.23733727 0.73314996 0.66765521 0.52935559
## [7] 0.37767826 0.28141533 0.20689250 0.17268763 0.10698462 0.05320285
## [13] 0.02775725install.packages("nFactors")## Warning: package 'nFactors' is in use and will not be installedlibrary(nFactors)
ev <- eigen(cor(data2)) # Obtención de los autovalores
ap <- parallel(subject=nrow(data2),var=ncol(data2),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS,xlab = "Número de Componentes",ylab = "Autovalores",
main = "Solución por autovalores para determinar
el número de factores")
abline(h=1, lty=3, col=4)
library(psych) cargamos el paquete necesario para ello la función fa de esta librería permite diverso métodos de análisis factorial por defecto es el metodo minres, con el argumento fm=‘pa’ aplicaremos ejes principales (rotate, por defecto es oblimin, si no queremos ninguna o queremos variamax hay que especificarlo)
library(psych)
ep<-fa(r = data2,nfactors=3,rotate = 'none', fm = 'pa', max.iter = 50)## 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.ep## Factor Analysis using method = pa
## Call: fa(r = data2, nfactors = 3, rotate = "none", max.iter = 50, fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 h2 u2 com
## proppobla 0.62 0.69 0.23 0.92 0.084 2.2
## propSuperf -0.21 0.70 -0.16 0.56 0.436 1.3
## ratioPobsup 0.70 -0.19 0.46 0.75 0.252 1.9
## autoabas 0.62 0.66 -0.10 0.83 0.168 2.0
## proporcionvtasT 0.67 0.71 -0.17 0.98 0.022 2.1
## proporcioncomprasT 0.70 0.70 -0.10 0.99 0.012 2.0
## Gastoporhogar 0.91 -0.14 0.03 0.86 0.144 1.0
## Gastoporunidadconsumo 0.95 -0.23 -0.11 0.96 0.035 1.1
## indicegastopersona 0.93 -0.27 -0.18 0.97 0.033 1.2
## pibpercap 0.78 -0.45 -0.30 0.91 0.089 1.9
## templeoAgri -0.69 0.38 -0.11 0.63 0.373 1.6
## tempServ 0.57 -0.20 0.70 0.85 0.147 2.1
## tbuscandoempleo -0.42 0.46 0.60 0.75 0.247 2.7
##
## PA1 PA2 PA3
## SS loadings 6.42 3.19 1.34
## Proportion Var 0.49 0.25 0.10
## Cumulative Var 0.49 0.74 0.84
## Proportion Explained 0.59 0.29 0.12
## Cumulative Proportion 0.59 0.88 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 27.84 with Chi Square of 301.6
## The degrees of freedom for the model are 42 and the objective function was 9.98
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 17 with the empirical chi square 3.46 with prob < 1
## The total number of observations was 17 with Likelihood Chi Square = 88.11 with prob < 4.1e-05
##
## Tucker Lewis Index of factoring reliability = 0.49
## RMSEA index = 0.247 and the 90 % confidence intervals are 0.185 0.339
## BIC = -30.88
## Fit based upon off diagonal values = 1ep$scores## PA1 PA2 PA3
## Andalucía -0.04816621 2.32260765 0.68939050
## Aragón 0.33272628 1.11344816 -1.14361581
## Principado De Asturias 0.05930880 -0.09737213 0.22411416
## Illes Balears 0.49939948 -1.91582529 1.02309084
## Canarias -0.51231245 0.30097246 2.27104783
## Cantabria -0.86425896 -0.09353955 0.16240154
## Castilla y León -0.20328468 0.79738925 -0.91912771
## Castilla-La Mancha -1.06898883 0.59319806 -0.03490738
## Cataluña 2.12214606 1.81338475 -0.70100040
## Comunidad Valenciana 0.47595380 0.47843217 0.25445306
## Extremadura -1.79058494 -1.17621866 0.42448064
## Galicia -0.88725827 -0.79239870 -0.17242308
## Comunidad de Madrid 2.07887945 -0.59067808 1.20201229
## Región de Murcia -0.49005520 0.99502553 -0.20670283
## Comunidad Foral De Navarra 0.24425747 -1.74480023 -1.38041205
## País Vasco 0.75688615 -1.46883530 -0.65519267
## La Rioja -0.70464796 -0.53479011 -1.03760891ep2<-fa(r = data2,nfactors=3,rotate = 'varimax', fm = 'pa', max.iter = 50)## 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.ep2## Factor Analysis using method = pa
## Call: fa(r = data2, nfactors = 3, rotate = "varimax", max.iter = 50,
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA2 PA1 PA3 h2 u2 com
## proppobla 0.90 -0.06 0.33 0.92 0.084 1.3
## propSuperf 0.49 -0.39 -0.42 0.56 0.436 2.9
## ratioPobsup 0.18 0.29 0.79 0.75 0.252 1.4
## autoabas 0.90 0.14 0.07 0.83 0.168 1.1
## proporcionvtasT 0.97 0.19 0.03 0.98 0.022 1.1
## proporcioncomprasT 0.97 0.18 0.10 0.99 0.012 1.1
## Gastoporhogar 0.37 0.66 0.53 0.86 0.144 2.6
## Gastoporunidadconsumo 0.33 0.80 0.47 0.96 0.035 2.0
## indicegastopersona 0.30 0.85 0.41 0.97 0.033 1.7
## pibpercap 0.07 0.91 0.28 0.91 0.089 1.2
## templeoAgri -0.05 -0.57 -0.55 0.63 0.373 2.0
## tempServ 0.08 0.07 0.92 0.85 0.147 1.0
## tbuscandoempleo 0.11 -0.85 0.16 0.75 0.247 1.1
##
## PA2 PA1 PA3
## SS loadings 4.13 3.99 2.84
## Proportion Var 0.32 0.31 0.22
## Cumulative Var 0.32 0.62 0.84
## Proportion Explained 0.38 0.36 0.26
## Cumulative Proportion 0.38 0.74 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 27.84 with Chi Square of 301.6
## The degrees of freedom for the model are 42 and the objective function was 9.98
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 17 with the empirical chi square 3.46 with prob < 1
## The total number of observations was 17 with Likelihood Chi Square = 88.11 with prob < 4.1e-05
##
## Tucker Lewis Index of factoring reliability = 0.49
## RMSEA index = 0.247 and the 90 % confidence intervals are 0.185 0.339
## BIC = -30.88
## Fit based upon off diagonal values = 1ep2$scores## PA2 PA1 PA3
## Andalucía 1.85695010 -1.556005362 -0.05166419
## Aragón 1.21000772 0.333552313 -1.04065118
## Principado De Asturias -0.06866104 -0.041963879 0.23822409
## Illes Balears -1.42017893 0.674583345 1.57940823
## Canarias -0.22154826 -1.785756150 1.50754703
## Cantabria -0.56013077 -0.617770792 -0.29441505
## Castilla y León 0.63612471 0.004939246 -1.05701040
## Castilla-La Mancha -0.07959823 -0.973635025 -0.73589303
## Cataluña 2.72596406 0.920164176 0.07541590
## Comunidad Valenciana 0.63585367 -0.064410200 0.33424117
## Extremadura -1.98972144 -0.852726897 -0.28936826
## Galicia -1.12818444 -0.101310119 -0.40222975
## Comunidad de Madrid 0.52775839 0.969848529 2.21276723
## Región de Murcia 0.58470857 -0.688597977 -0.67594724
## Comunidad Foral De Navarra -1.20793534 1.800460667 -0.55566228
## País Vasco -0.76216897 1.588901372 0.23264158
## La Rioja -0.73923978 0.389726755 -1.07740386Diagrama de arbol
fa.diagram(ep)
fa.diagram(ep2)
par(mfrow = c(1,2))
plot(ep$loadings[,1],
ep$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "No rotation")
text(ep$loadings[,1]-0.08,
ep$loadings[,2]+0.08,
colnames(data2),
col="blue")
abline(h = 0, v = 0)
plot(ep2$loadings[,1],
ep2$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "Varimax rotation")
text(ep2$loadings[,1]-0.08,
ep2$loadings[,2]+0.08,
colnames(data2),
col="blue")
abline(h = 0, v = 0)
COMPARACION CP, ML Y EP
ML vs EP
EP vs CP
fa.diagram(ep2)
fa.diagram(matriz.correlaciones.rotada)
ep2$scores## PA2 PA1 PA3
## Andalucía 1.85695010 -1.556005362 -0.05166419
## Aragón 1.21000772 0.333552313 -1.04065118
## Principado De Asturias -0.06866104 -0.041963879 0.23822409
## Illes Balears -1.42017893 0.674583345 1.57940823
## Canarias -0.22154826 -1.785756150 1.50754703
## Cantabria -0.56013077 -0.617770792 -0.29441505
## Castilla y León 0.63612471 0.004939246 -1.05701040
## Castilla-La Mancha -0.07959823 -0.973635025 -0.73589303
## Cataluña 2.72596406 0.920164176 0.07541590
## Comunidad Valenciana 0.63585367 -0.064410200 0.33424117
## Extremadura -1.98972144 -0.852726897 -0.28936826
## Galicia -1.12818444 -0.101310119 -0.40222975
## Comunidad de Madrid 0.52775839 0.969848529 2.21276723
## Región de Murcia 0.58470857 -0.688597977 -0.67594724
## Comunidad Foral De Navarra -1.20793534 1.800460667 -0.55566228
## País Vasco -0.76216897 1.588901372 0.23264158
## La Rioja -0.73923978 0.389726755 -1.07740386punt.rotadas## [,1] [,2] [,3] [,4]
## [1,] 1.29311678 2.01113651 -0.52387924 0.1382764
## [2,] -0.12688179 0.02437965 1.15350042 0.2184912
## [3,] -0.10877475 -0.81407429 -0.25970503 -0.8698561
## [4,] -0.89983394 -0.83646350 -1.26836513 2.5707869
## [5,] 0.68628581 -0.74875045 -2.01947746 -1.0971457
## [6,] -0.13598655 -0.94759194 0.11670472 1.6618826
## [7,] 0.56371253 0.79879441 0.81397642 1.1079817
## [8,] 1.32703766 0.27054828 0.15687706 0.5575134
## [9,] -1.09866540 2.31890528 0.26369528 -0.2017761
## [10,] -0.22803274 0.66719894 -0.07846606 -0.5958250
## [11,] 1.73744030 -0.49489401 -0.37094454 -0.4359671
## [12,] 0.03683842 0.09470207 0.46981428 0.2916004
## [13,] -1.76365018 0.22532298 -1.80407499 -0.7229765
## [14,] 0.86967997 -0.62156430 0.04927999 -0.8188107
## [15,] -0.99175772 -0.79755167 1.53486360 -0.8655108
## [16,] -1.32826604 -0.02206066 0.47503043 -0.5396868
## [17,] 0.16773764 -1.12803730 1.29117024 -0.3989778Análisis cluster
df <- scale(ep2$scores)
d <- dist(df, method = "euclidean")
res.hc <- hclust(d, method = "ward.D" )
fviz_dend(res.hc, cex = 0.5, k = 5, rect = TRUE)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <]8;;https://github.com/kassambara/factoextra/issueshttps://github.com/kassambara/factoextra/issues]8;;>.
ward_factor <- cutree(res.hc,5)añadimos resultadoa a la base de datos
data4<-cbind(ward_factor,comaut)analisis ANOVA
summary(aov(proppobla~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 0.00938 0.009380 3.692 0.0739 .
## Residuals 15 0.03811 0.002541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(aov(propSuperf~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 0.01179 0.011793 3.826 0.0693 .
## Residuals 15 0.04624 0.003082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(aov(ratioPobsup~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 0.00 0.000 0 0.998
## Residuals 15 69.93 4.662summary(aov(autoabas~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 2.000e+08 200027215 1.477 0.243
## Residuals 15 2.031e+09 135416915summary(aov(proporcionvtasT~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 0.003425 0.003425 1.649 0.219
## Residuals 15 0.031164 0.002078summary(aov(proporcioncomprasT~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 0.003514 0.003514 1.986 0.179
## Residuals 15 0.026539 0.001769summary(aov(Gastoporhogar~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 1716254 1716254 0.234 0.636
## Residuals 15 109986503 7332434summary(aov(templeoAgri~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 5.99 5.992 0.398 0.538
## Residuals 15 225.87 15.058summary(aov(tempServ~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 32.1 32.08 0.901 0.358
## Residuals 15 534.3 35.62summary(aov(tbuscandoempleo~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 21.34 21.335 8.261 0.0116 *
## Residuals 15 38.74 2.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(aov(pibpercap~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 89083644 89083644 4.26 0.0568 .
## Residuals 15 313686050 20912403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(aov(Gastoporunidadconsumo~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 1555409 1555409 0.579 0.458
## Residuals 15 40260869 2684058summary(aov(indicegastopersona~ward_factor, data = data4))## Df Sum Sq Mean Sq F value Pr(>F)
## ward_factor 1 97.2 97.21 0.863 0.368
## Residuals 15 1689.2 112.61ward_factor1<-as.factor(data4$ward_factor)
RatiopobSupWard<-TukeyHSD(aov(data4$ratioPobsup~ward_factor1)); RatiopobSupWard## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$ratioPobsup ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 -0.90487592 -5.1116323 3.3018805 0.9558668
## 3-1 3.61372162 -1.6330255 8.8604687 0.2451875
## 4-1 -1.29716414 -5.8409804 3.2466521 0.8876976
## 5-1 -0.09569192 -5.3424390 5.1510552 0.9999970
## 3-2 4.51859754 0.3118411 8.7253540 0.0334004
## 4-2 -0.39228822 -3.6808610 2.8962846 0.9949448
## 5-2 0.80918400 -3.3975724 5.0159404 0.9702041
## 4-3 -4.91088576 -9.4547020 -0.3670695 0.0322072
## 5-3 -3.70941355 -8.9561606 1.5373335 0.2252484
## 5-4 1.20147221 -3.3423440 5.7452885 0.9118976tempServ <- TukeyHSD(aov(data4$tempServ~ward_factor1)); tempServ## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$tempServ ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 -5.5857143 -15.537609 4.366180 0.4221862
## 3-1 7.6500000 -4.762193 20.062193 0.3377914
## 4-1 -7.5500000 -18.299275 3.199275 0.2301732
## 5-1 -6.5500000 -18.962193 5.862193 0.4787707
## 3-2 13.2357143 3.283820 23.187609 0.0082615
## 4-2 -1.9642857 -9.744040 5.815468 0.9241834
## 5-2 -0.9642857 -10.916180 8.987609 0.9977325
## 4-3 -15.2000000 -25.949275 -4.450725 0.0052567
## 5-3 -14.2000000 -26.612193 -1.787807 0.0227803
## 5-4 1.0000000 -9.749275 11.749275 0.9980644tbuscandoempleo <- TukeyHSD(aov(data4$tbuscandoempleo~ward_factor1)); tbuscandoempleo## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$tbuscandoempleo ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 -2.9928571 -6.796167 0.81045283 0.1528648
## 3-1 -4.7000000 -9.443561 0.04356095 0.0525468
## 4-1 -3.6000000 -7.708044 0.50804429 0.0967086
## 5-1 -5.1500000 -9.893561 -0.40643905 0.0313561
## 3-2 -1.7071429 -5.510453 2.09616711 0.6212725
## 4-2 -0.6071429 -3.580327 2.36604131 0.9631840
## 5-2 -2.1571429 -5.960453 1.64616711 0.4126049
## 4-3 1.1000000 -3.008044 5.20804429 0.9082631
## 5-3 -0.4500000 -5.193561 4.29356095 0.9979115
## 5-4 -1.5500000 -5.658044 2.55804429 0.7501312pibpercap <- TukeyHSD(aov(data4$pibpercap~ward_factor1)); pibpercap## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$pibpercap ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 4208.3571 -4807.9910 13224.705 0.5883125
## 3-1 11362.0000 116.6381 22607.362 0.0472671
## 4-1 3234.0000 -6504.7691 12972.769 0.8236133
## 5-1 12311.5000 1066.1381 23556.862 0.0298295
## 3-2 7153.6429 -1862.7053 16169.991 0.1479728
## 4-2 -974.3571 -8022.7607 6074.046 0.9911513
## 5-2 8103.1429 -913.2053 17119.491 0.0859797
## 4-3 -8128.0000 -17866.7691 1610.769 0.1200561
## 5-3 949.5000 -10295.8619 12194.862 0.9986740
## 5-4 9077.5000 -661.2691 18816.269 0.0720286Gastoporunidadconsumo <- TukeyHSD(aov(data4$Gastoporunidadconsumo~ward_factor1)); Gastoporunidadconsumo## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$Gastoporunidadconsumo ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 1000.501 -1902.8770 3903.8798 0.8042178
## 3-1 3147.715 -473.4341 6768.8641 0.1003464
## 4-1 -373.465 -3509.4721 2762.5421 0.9949769
## 5-1 2947.595 -673.5541 6568.7441 0.1333735
## 3-2 2147.214 -756.1648 5050.5920 0.1929032
## 4-2 -1373.966 -3643.6417 895.7089 0.3536853
## 5-2 1947.094 -956.2848 4850.4720 0.2663381
## 4-3 -3521.180 -6657.1871 -385.1729 0.0255877
## 5-3 -200.120 -3821.2691 3421.0291 0.9997504
## 5-4 3321.060 185.0529 6457.0671 0.0362752indicegastopersona <- TukeyHSD(aov(data4$indicegastopersona~ward_factor1)); indicegastopersona## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = data4$indicegastopersona ~ ward_factor1)
##
## $ward_factor1
## diff lwr upr p adj
## 2-1 8.808571 -10.5834085 28.2005514 0.6113520
## 3-1 21.200000 -2.9860483 45.3860483 0.0966029
## 4-1 0.032500 -20.9132323 20.9782323 1.0000000
## 5-1 21.565000 -2.6210483 45.7510483 0.0892501
## 3-2 12.391429 -7.0005514 31.7834085 0.3064903
## 4-2 -8.776071 -23.9354795 6.3833366 0.3939078
## 5-2 12.756429 -6.6355514 32.1484085 0.2821013
## 4-3 -21.167500 -42.1132323 -0.2217677 0.0472118
## 5-3 0.365000 -23.8210483 24.5510483 0.9999986
## 5-4 21.532500 0.5867677 42.4782323 0.0429485