Agrupando las comunidades autónomas
2022-11-08
Debemos de tener en cuenta que en los datos que vamos a trabajar no están incluidas las ciudades (Ceuta y Melilla), ya que son 2 ciudades, cuyos datos varian mucho respesto al resto de comunidades, por lo que los resultados estarían alterados. Un gran ejemplo, es su superficies es muy pequeña en comparación al resto de comunidades, el raito de población, etc.
cor(datos$Gastoporpersona, datos$indicegastopersona)## [1] 1cor(datos$Gastoporhogar, datos$indicegastoporhoga)## [1] 1cor(datos$Comprasdeotras, datos$proporcioncomprasT)## [1] 1cor(datos$Ventasaotras, datos$proporcionvtasT)## [1] 1cor(datos$Gastoporunidadconsumo, datos$indicegastounidadconsumo)## [1] 1También he eliminado otras variables que presentan una correlación positiva perfecta, siendo (Gastoporpersona, Gastoporhogar, Gastoporunidadconsumo, Comprasdeotras y Ventasaotras).
Los datos que voy a presentar estan trabajados con las siguientes variables:
names(datos)## [1] "Cautonoma" "proppobla"
## [3] "propSuperf" "ratioPobsup"
## [5] "densidad" "autoabas"
## [7] "maxventa" "maxcompra"
## [9] "ratioVentasCompras" "proporcionvtasT"
## [11] "proporcioncomprasT" "ratiomaximaventaTotal"
## [13] "ratiomaxcompraTotal" "indicegastopersona"
## [15] "indicegastoporhoga" "indicegastounidadconsumo"
## [17] "pibpercap" "templeoAgri"
## [19] "tempIndus" "tempConstr"
## [21] "tempServ" "tbuscandoempleo"Convertimos los datos en una Matriz y tipificamos las variables para obtener el mismo peso.
z = model.matrix(~ -1 + proppobla + propSuperf + ratioPobsup + densidad + maxcompra + ratioVentasCompras + proporcionvtasT +
proporcioncomprasT + ratiomaximaventaTotal + ratiomaxcompraTotal + indicegastopersona + indicegastoporhoga +
tempIndus + tempConstr + tempServ + tbuscandoempleo, datos)
row.names(z)=datos$Cautonoma
z <- scale(z)Resultado Análisis Simple
clust_simple <- hclust(dist(z), method = "single")
plot(clust_simple, labels = row.names(z), hang = -1, main = "Dendograma agrupación Comunidades Autonomas", xlab = "Comunidades Autonomas", sub = "Encadenamiento simple Distancia euclidea normalizada")
rect.hclust(clust_simple, k = 4)
Para el metodo Simple la mejor agrupación son 4 grupos, en los que obtenemos las siguientes agrupaciones.
## Andalucía Aragón
## 1 2
## Principado De Asturias Illes Balears
## 1 1
## Canarias Cantabria
## 1 1
## Castilla y León Castilla-La Mancha
## 1 1
## Cataluña Comunidad Valenciana
## 1 3
## Extremadura Galicia
## 1 1
## Comunidad de Madrid Región de Murcia
## 4 1
## Comunidad Foral De Navarra País Vasco
## 1 1
## La Rioja
## 1Resultado Análisis Completo
clust_comple <- hclust(dist(z), method = "complete")
plot(clust_comple, labels = row.names(z), hang = -1, main = "Dendograma agrupación Comunidades Autonomas", xlab = "Comunidades Autonomas", sub = "Encadenamiento complete Distancia euclidea normalizada")
rect.hclust(clust_comple, k = 5)
Para el metodo Complete la mejor agrupación son 5 grupos, el agrupar a 4 grupos el salto de agrupación del grupo que contiene las ccaa (Andalucia, Castilla-Leon y Castilla la Mancha) darian un gran salto, por lo cual obtenemos las siguientes agrupaciones.
## Andalucía Aragón
## 1 2
## Principado De Asturias Illes Balears
## 2 3
## Canarias Cantabria
## 3 2
## Castilla y León Castilla-La Mancha
## 1 1
## Cataluña Comunidad Valenciana
## 4 4
## Extremadura Galicia
## 3 2
## Comunidad de Madrid Región de Murcia
## 5 2
## Comunidad Foral De Navarra País Vasco
## 2 4
## La Rioja
## 2Resultado Análisis Ward
clust_ward <- hclust(dist(z), method = "ward.D")
plot(clust_ward, labels = row.names(z), hang = -1, main = "Dendograma agrupación Comunidades Autonomas", xlab = "Comunidades Autonomas", sub = "Encadenamiento ward Distancia euclidea normalizada")
rect.hclust(clust_ward, k = 5)
Para el metodo ward la mejor agrupación son 5 grupos. Estando agrupados de la siguiente forma
## Andalucía Aragón
## 1 2
## Principado De Asturias Illes Balears
## 3 4
## Canarias Cantabria
## 4 3
## Castilla y León Castilla-La Mancha
## 1 1
## Cataluña Comunidad Valenciana
## 5 5
## Extremadura Galicia
## 4 3
## Comunidad de Madrid Región de Murcia
## 5 3
## Comunidad Foral De Navarra País Vasco
## 3 5
## La Rioja
## 3Unimos los datos obtenidos a la base de datos, para el análisis de los métodos directos.
datos.cluster<-as.data.frame(cbind(z,clus1,clus2,clus3))
write.csv(datos.cluster, "datos_cluster.csv")Sobre el análisis obtenido en weka he obtenido unos resultados muy parecidos a las agrupaciones por los metodos jerarquicos.
Mapa autorganizado (modelo SOM)
## Warning: package 'kohonen' was built under R version 4.2.2## SOM of size 3x3 with a hexagonal topology and a bubble neighbourhood function.
## The number of data layers is 1.
## Distance measure(s) used: sumofsquares.
## Training data included: 17 objects.
## Mean distance to the closest unit in the map: 3.557.asignacion## X.. ClusMapa
## 1 Andalucía 2
## 2 Aragón 7
## 3 'Principado De Asturias' 8
## 4 'Illes Balears' 9
## 5 Canarias 6
## 6 Cantabria 9
## 7 'Castilla y León' 3
## 8 'Castilla-La Mancha' 3
## 9 Cataluña 1
## 10 'Comunidad Valenciana' 2
## 11 Extremadura 6
## 12 Galicia 8
## 13 'Comunidad de Madrid' 1
## 14 'Región de Murcia' 8
## 15 'Comunidad Foral De Navarra' 5
## 16 'País Vasco' 4
## 17 'La Rioja' 8“changes” muestra la distancia media al perfil (codebook vector) del nodo más prómimo durante el entrenamiento
plot(som_model, type="changes")
“count” muestra el número de instancias asignada a cada nodo los nodos vacíos aparecen en gris
plot(som_model, type="count", main="instancias por nodo")
“dist.neighbours” muestra la suma de las distancias( entre perfiles) a todos los vecinos inmediatos. Esta visualización también se conoce como grafico de la matriz U
plot(som_model, type="dist.neighbours", main = " distancias a los perfiles vecinos")
“codes” permite visulizar ( si hay pocas variables, que en nuestro caso hay 17)los valores que toma para cada variable cada perfil de cada nodo
plot(som_model, type="codes")## Warning in par(opar): argument 1 does not name a graphical parameter
som_model$codes## [[1]]
## proppobla propSuperf ratioPobsup densidad maxcompra
## V1 1.7177472 -0.3271857 1.917024813 1.917121972 0.53473367
## V2 1.5755465 0.8944411 -0.080829216 -0.081145460 0.59615913
## V3 -0.2037379 1.8979109 -0.734811200 -0.734205929 -0.08283246
## V4 -0.2145801 -0.7390912 0.702662943 0.702197393 0.07772345
## V5 -0.8001576 -0.6202565 -0.538015320 -0.538164503 -0.52769602
## V6 -0.4721357 -0.1050534 -0.135284843 -0.136837202 -0.32346035
## V7 -0.5601761 0.5878101 -0.723279900 -0.722982274 3.29187838
## V8 -0.5425856 -0.5223660 -0.369740441 -0.369272917 -0.75783227
## V9 -0.7415825 -0.8072306 -0.004172417 -0.003619632 -0.65054258
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## V1 0.5213189 1.8148611 1.6408348 -0.50513662
## V2 -0.1993839 0.8398795 1.1277796 1.56466356
## V3 0.9752480 0.5497744 0.1749433 -0.26600973
## V4 -0.4220894 0.2648052 0.6156336 -0.47683128
## V5 1.1060659 -0.5200514 -0.7469037 -0.22657807
## V6 -1.0621434 -0.9283820 -0.7579701 0.07385556
## V7 -0.3681749 -0.0831336 0.1373859 -0.03615130
## V8 0.5859796 -0.5094637 -0.6461063 -0.23427378
## V9 -1.4883273 -1.0615941 -0.9034887 -0.18142258
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## V1 -0.7271241 1.53765869 1.81483854 -0.3504064
## V2 -0.6370566 -0.19463513 0.01646825 -0.4069511
## V3 -0.4942280 -0.71202583 -0.93011974 0.1890180
## V4 -0.7349719 1.36006640 0.94141001 0.8760495
## V5 0.2741748 1.04794574 1.13597702 1.8047994
## V6 0.8622558 -1.29658014 -1.04844485 -1.2316178
## V7 2.9772273 0.31804604 -0.13825980 1.0102860
## V8 -0.4049474 -0.41284964 -0.41807491 0.2904705
## V9 0.5417169 0.07604578 -0.04610141 -0.6377014
## tempConstr tempServ tbuscandoempleo
## V1 -0.3321687 1.28036770 -0.4994789
## V2 -0.3278350 -0.04832533 0.8890827
## V3 0.2073137 -0.55181247 0.1944186
## V4 -0.5789573 0.22632214 -0.9348981
## V5 -0.8972183 -1.08331122 -1.0236638
## V6 -0.3546606 0.28408988 1.7346821
## V7 -0.3023219 -0.71504547 -0.7803354
## V8 -0.3817349 -0.52402154 -0.1448279
## V9 2.4574556 0.75639654 -0.6968842“property” evalúa la variable considerada para el perfil obtenido en cada nodo mostrándolo en un mapa de color “property es el atributo del objeto som del que considera los valores numéricos de los perfiles
plot(som_model, type = "property", property = getCodes(som_model)[,1], main=colnames(getCodes(som_model))[1], palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,2], main=colnames(getCodes(som_model))[2], palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,3], main=colnames(getCodes(som_model))[3],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,4], main=colnames(getCodes(som_model))[4],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,5], main=colnames(getCodes(som_model))[5],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,6], main=colnames(getCodes(som_model))[6],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,7], main=colnames(getCodes(som_model))[7],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,8], main=colnames(getCodes(som_model))[8],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,9], main=colnames(getCodes(som_model))[9],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,10], main=colnames(getCodes(som_model))[10],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,11], main=colnames(getCodes(som_model))[11],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,12], main=colnames(getCodes(som_model))[12],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,13], main=colnames(getCodes(som_model))[13],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,14], main=colnames(getCodes(som_model))[14],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,15], main=colnames(getCodes(som_model))[15],palette.name=coolBlueHotRed)
plot(som_model, type = "property", property = getCodes(som_model)[,16], main=colnames(getCodes(som_model))[16],palette.name=coolBlueHotRed)
perfiles_de_propSuperf= getCodes(som_model)[,2]*sd(datos2$propSuperf)+ mean(datos2$propSuperf)
# deshacemos la tipificación
perfiles_de_propSuperf## V1 V2 V3 V4 V5 V6 V7
## -0.3271856 0.8944411 1.8979109 -0.7390912 -0.6202565 -0.1050534 0.5878102
## V8 V9
## -0.5223660 -0.8072305“quality” muestra para cada nodo la distancia media de las instancia asignadas al nodo a cada perfil
plot(som_model, type="quality")
“mapping” muestra dónde han sido asignados los disntintos individuos o instancia añadiendo intify podemos identificarlos ( en realidad sólo a uno por neurona)
plot(som_model, type="mapping")
identify(som_model)
## integer(0)agrupando perfiles
análisis cluster jerárquico para agrupar los prefiles según su semejanza utilizamos el método de ward
clusperfil<-hclust(object.distances(som_model, "codes"),method="ward.D")
plot(clusperfil)#visulizamos el dendograma
rect.hclust(clusperfil, k = 5)
generamos 5 grupos clusters
som.hc <- cutree(clusperfil, 5)
plot(som_model, type = "mapping", main = "Instancias por nodo en cada cluster", bgcol = c("steelblue1", "sienna1", "yellowgreen", "red", "yellow")[som.hc])
add.cluster.boundaries(som_model, clustering = som.hc)
visualizamos las fronteras
plot(som_model, type = "property", property = getCodes(som_model)[,2], main=colnames(getCodes(som_model))[2],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,1], main=colnames(getCodes(som_model))[1], palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,2], main=colnames(getCodes(som_model))[2], palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,3], main=colnames(getCodes(som_model))[3],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,4], main=colnames(getCodes(som_model))[4],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,5], main=colnames(getCodes(som_model))[5],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,6], main=colnames(getCodes(som_model))[6],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,7], main=colnames(getCodes(som_model))[7],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,8], main=colnames(getCodes(som_model))[8],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,9], main=colnames(getCodes(som_model))[9],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,10], main=colnames(getCodes(som_model))[10],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,11], main=colnames(getCodes(som_model))[11],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,12], main=colnames(getCodes(som_model))[12],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,13], main=colnames(getCodes(som_model))[13],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,14], main=colnames(getCodes(som_model))[14],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,15], main=colnames(getCodes(som_model))[15],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
plot(som_model, type = "property", property = getCodes(som_model)[,16], main=colnames(getCodes(som_model))[16],palette.name=coolBlueHotRed)
add.cluster.boundaries(som_model, som.hc)
Añadimos e método SOM a nuestra base de datos
datos2$ClusMapa<- som_model$unit.classifAnalizar las coincidencias y diferencias de agrupación de todos estos métodos.
MEDIAS
media_cluscompleto<- aggregate(z~datos2$complete,datos2,mean,na.rm=T)
media_cluscompleto## datos2$complete proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 0.5952239 1.8809533 -0.61376485 -0.61343294 0.1329400
## 2 2 -0.6244591 -0.4143291 -0.43780961 -0.43796798 -0.1383115
## 3 3 -0.5073332 -0.3850727 0.06762995 0.06791874 -0.4049229
## 4 4 0.8607676 -0.2915313 0.43542589 0.43481490 0.3333647
## 5 5 1.5252388 -0.7127443 3.39679433 3.39787377 0.7840354
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 0.6843970 0.7274920 0.5127143 -0.313127040
## 2 0.3746956 -0.5068956 -0.5836773 -0.194702283
## 3 -1.5916133 -1.0197017 -0.7945754 0.009685088
## 4 -0.2450365 1.1050853 1.4081670 0.914000386
## 5 0.8338894 1.1096424 0.7068234 -0.468759323
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 -0.5462522 -0.7365369 -0.6556281 -0.2302528
## 2 0.2886145 -0.1195321 -0.1801469 0.6188512
## 3 0.7885681 -0.5788043 -0.4844450 -1.3756299
## 4 -0.8318039 1.0647691 0.8435327 0.5092382
## 5 -0.2518375 1.5884412 2.1506493 -1.0420250
## tempConstr tempServ tbuscandoempleo
## 1 0.09106472 -0.4346901 0.5960033
## 2 -0.09378307 -0.5923612 -0.4336672
## 3 0.71492604 0.8763010 0.8884395
## 4 -0.53279659 0.2432155 -0.2641033
## 5 -0.16310100 2.0920491 -0.6253481media_clusfarthest<- aggregate(z~datos2$FF,datos2,mean,na.rm=T)
media_clusfarthest## datos2$FF proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 -0.1930082 -0.4278934 -0.1040698 -0.1046997 -0.3534051
## 2 2 -0.5599524 0.5890860 -0.7235600 -0.7232621 3.2958072
## 3 3 -0.6278680 -0.8123741 0.3128283 0.3173842 -0.5912069
## 4 4 1.5252388 -0.7127443 3.3967943 3.3978738 0.7840354
## 5 5 0.5952239 1.8809533 -0.6137648 -0.6134329 0.1329400
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 0.004729345 -0.18236400 -0.1352564 0.15030258
## 2 -0.369532249 -0.08269003 0.1382376 -0.03593137
## 3 -2.569570755 -1.20342436 -0.8953839 -0.20925655
## 4 0.833889351 1.10964242 0.7068234 -0.46875932
## 5 0.684396952 0.72749200 0.5127143 -0.31312704
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 -0.1630004 -0.04366071 -0.05206763 0.2074773
## 2 2.9801038 0.31743216 -0.13933731 1.0096456
## 3 0.7034951 0.78400510 0.52831637 -1.5591127
## 4 -0.2518375 1.58844120 2.15064933 -1.0420250
## 5 -0.5462522 -0.73653687 -0.65562815 -0.2302528
## tempConstr tempServ tbuscandoempleo
## 1 -0.24502218 -0.1708621 0.04553506
## 2 -0.30173684 -0.7148164 -0.78016729
## 3 2.88688765 1.8063203 -0.88338008
## 4 -0.16310100 2.0920491 -0.62534810
## 5 0.09106472 -0.4346901 0.59600328media_clusterWard<- aggregate(z~datos2$ward,datos2,mean,na.rm=T)
media_clusterWard## datos2$ward proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 0.5952239 1.8809533 -0.61376485 -0.61343294 0.1329400
## 2 2 -0.5599524 0.5890860 -0.72356001 -0.72326209 3.2958072
## 3 3 -0.6352102 -0.5815649 -0.39018455 -0.39041897 -0.7106647
## 4 4 -0.5073332 -0.3850727 0.06762995 0.06791874 -0.4049229
## 5 5 1.0268854 -0.3968346 1.17576800 1.17557962 0.4460324
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 0.68439695 0.72749200 0.5127143 -0.313127040
## 2 -0.36953225 -0.08269003 0.1382376 -0.035931368
## 3 0.49873359 -0.57759654 -0.7039964 -0.221164102
## 4 -1.59161329 -1.01970172 -0.7945754 0.009685088
## 5 0.02469493 1.10622461 1.2328311 0.568310459
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 -0.5462522 -0.7365369 -0.6556281 -0.2302528
## 2 2.9801038 0.3174322 -0.1393373 1.0096456
## 3 -0.1599671 -0.1923595 -0.1869485 0.5537188
## 4 0.7885681 -0.5788043 -0.4844450 -1.3756299
## 5 -0.6868123 1.1956871 1.1703119 0.1214224
## tempConstr tempServ tbuscandoempleo
## 1 0.09106472 -0.4346901 0.5960033
## 2 -0.30173684 -0.7148164 -0.7801673
## 3 -0.05912411 -0.5719520 -0.3759172
## 4 0.71492604 0.8763010 0.8884395
## 5 -0.44037269 0.7054239 -0.3544145media_clustersimple<- aggregate(z~datos2$simple,datos2,mean,na.rm=T)
media_clustersimple## datos2$simple proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 -0.09343892 0.08838206 -0.2481826 -0.2486057 -0.3183322
## 2 2 -0.55995244 0.58908602 -0.7235600 -0.7232621 3.2958072
## 3 3 -0.62786800 -0.81237413 0.3128283 0.3173842 -0.5912069
## 4 4 0.87728762 -0.21293442 0.2403114 0.2398788 0.6496834
## 5 5 1.52523876 -0.71274426 3.3967943 3.3978738 0.7840354
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 0.2008303 -0.02451825 -0.07206381 -0.23448720
## 2 -0.3695322 -0.08269003 0.13823756 -0.03593137
## 3 -2.5695708 -1.20342436 -0.89538386 -0.20925655
## 4 -0.5055806 0.49520927 0.98715255 3.76228078
## 5 0.8338894 1.10964242 0.70682335 -0.46875932
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 -0.2229301 -0.2338068 -0.2051169 0.1012136
## 2 2.9801038 0.3174322 -0.1393373 1.0096456
## 3 0.7034951 0.7840051 0.5283164 -1.5591127
## 4 -0.5336704 0.3496096 0.1268915 0.2757147
## 5 -0.2518375 1.5884412 2.1506493 -1.0420250
## tempConstr tempServ tbuscandoempleo
## 1 -0.1471046 -0.25066867 0.1368387
## 2 -0.3017368 -0.71481640 -0.7801673
## 3 2.8868877 1.80632029 -0.8833801
## 4 -0.5096906 0.07513976 0.5099926
## 5 -0.1631010 2.09204912 -0.6253481media_clusKM<- aggregate(z~datos2$Kmeans,datos2,mean,na.rm=T)
media_clusKM## datos2$Kmeans proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 -0.6278680 -0.81237413 0.31282832 0.31738416 -0.5912069
## 2 2 0.5952239 1.88095331 -0.61376485 -0.61343294 0.1329400
## 3 3 -0.5732602 -0.45495198 -0.29524516 -0.29544200 -0.1113420
## 4 4 1.2012632 -0.46283934 1.81855287 1.81887631 0.7168594
## 5 5 0.3419172 -0.08839709 0.08444058 0.08289934 -0.1168654
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 -2.5695708 -1.2034244 -0.8953839 -0.2092566
## 2 0.6843970 0.7274920 0.5127143 -0.3131270
## 3 0.2750276 -0.4104461 -0.4337432 -0.2299826
## 4 0.1641544 0.8024258 0.8469879 1.6467607
## 5 -0.6707167 0.2332217 0.3777369 -0.1016742
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 0.7034951 0.78400510 0.52831637 -1.5591127
## 2 -0.5462522 -0.73653687 -0.65562815 -0.2302528
## 3 0.1605922 0.06545438 -0.03994477 0.6510203
## 4 -0.3927540 0.96902540 1.13877043 -0.3831551
## 5 0.1453438 -0.34536010 -0.17313820 -0.7306603
## tempConstr tempServ tbuscandoempleo
## 1 2.88688765 1.8063203 -0.88338008
## 2 0.09106472 -0.4346901 0.59600328
## 3 -0.15443626 -0.4900150 -0.49633211
## 4 -0.33639581 1.0835944 -0.05767774
## 5 -0.41726672 0.4168938 1.06046085media_Mapa<- aggregate(z~datos2$ClusMapa,datos2,mean,na.rm=T)
media_Mapa## datos2$ClusMapa proppobla propSuperf ratioPobsup densidad maxcompra
## 1 1 1.7225610 -0.3175458 1.880027258 1.880099857 0.52850052
## 2 2 1.5426765 0.8423119 -0.065711734 -0.066033453 0.59867889
## 3 3 -0.2111969 1.8726508 -0.734779829 -0.734176528 -0.07442723
## 4 4 -0.2148680 -0.7393122 0.702706049 0.702239915 0.07744503
## 5 5 -0.8206013 -0.6363614 -0.541762559 -0.541902738 -0.52483580
## 6 6 -0.4470658 -0.1714220 -0.054969229 -0.056813968 -0.31178085
## 7 7 -0.5599524 0.5890860 -0.723560012 -0.723262095 3.29580723
## 8 8 -0.5356313 -0.5126543 -0.374534548 -0.374051597 -0.75825281
## 9 9 -0.7380013 -0.8073926 0.005810891 0.006489745 -0.64867394
## ratioVentasCompras proporcionvtasT proporcioncomprasT ratiomaximaventaTotal
## 1 0.5135042 1.83249419 1.6641881 -0.50604694
## 2 -0.2137982 0.82365432 1.1211598 1.66811581
## 3 0.9876033 0.51518831 0.1414879 -0.25666598
## 4 -0.4226482 0.26470078 0.6157955 -0.47694506
## 5 1.1215334 -0.53547239 -0.7640525 -0.22766802
## 6 -1.1026346 -0.92784039 -0.7441711 0.11915591
## 7 -0.3695322 -0.08269003 0.1382376 -0.03593137
## 8 0.5865139 -0.50035238 -0.6372107 -0.23599370
## 9 -1.5223791 -1.06606084 -0.9032335 -0.18229918
## ratiomaxcompraTotal indicegastopersona indicegastoporhoga tempIndus
## 1 -0.7390076 1.53638945 1.80644303 -0.3331144
## 2 -0.6321897 -0.16901509 0.02166635 -0.3748150
## 3 -0.4540238 -0.76098542 -0.94166280 0.1672931
## 4 -0.7355635 1.36035991 0.94146989 0.8762037
## 5 0.3072205 1.08117326 1.16641934 1.8436581
## 6 0.8311046 -1.26020900 -0.99082567 -1.2838886
## 7 2.9801038 0.31743216 -0.13933731 1.0096456
## 8 -0.4142879 -0.41200211 -0.42594282 0.3132453
## 9 0.5468119 0.09834162 -0.02801123 -0.6667194
## tempConstr tempServ tbuscandoempleo
## 1 -0.3363958 1.26007401 -0.4963321
## 2 -0.3363958 -0.04251329 0.8712374
## 3 0.2181476 -0.57195199 0.2777638
## 4 -0.5790085 0.22640796 -0.9349865
## 5 -0.9255982 -1.10139070 -1.0381993
## 6 -0.3710548 0.41129132 1.7743493
## 7 -0.3017368 -0.71481640 -0.7801673
## 8 -0.3710548 -0.52573115 -0.1737921
## 9 2.4709801 0.78946182 -0.7027577varianza
anovaCompleto <- aov(z~datos2$complete)
summary(anovaCompleto)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 1.861 1.8610 1.9743 0.1804
## Residuals 15 14.139 0.9426
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 5.1351 5.1351 7.0894 0.01774 *
## Residuals 15 10.8649 0.7243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 9.9138 9.9138 24.434 0.0001769 ***
## Residuals 15 6.0862 0.4057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 9.9131 9.9131 24.429 0.0001771 ***
## Residuals 15 6.0869 0.4058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 0.3373 0.33732 0.3231 0.5782
## Residuals 15 15.6627 1.04418
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 1.5111 1.51113 1.5644 0.2302
## Residuals 15 14.4889 0.96592
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 1.001 1.00103 1.0011 0.3329
## Residuals 15 14.999 0.99993
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 1.9874 1.98741 2.1275 0.1653
## Residuals 15 14.0126 0.93417
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 1.1452 1.14519 1.1564 0.2992
## Residuals 15 14.8548 0.99032
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 0.1365 0.13646 0.129 0.7244
## Residuals 15 15.8635 1.05757
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 6.08 6.0800 9.1935 0.008405 **
## Residuals 15 9.92 0.6613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 6.505 6.505 10.277 0.005895 **
## Residuals 15 9.495 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 0.5531 0.55306 0.5371 0.475
## Residuals 15 15.4469 1.02980
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 0.1481 0.14807 0.1401 0.7134
## Residuals 15 15.8519 1.05680
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 6.1232 6.1232 9.2994 0.008112 **
## Residuals 15 9.8768 0.6585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$complete 1 0.3001 0.30014 0.2868 0.6002
## Residuals 15 15.6999 1.04666anovafarther<- aov(z~datos2$FF)
summary(anovafarther)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 4.5068 1.12669 1.1764 0.3696
## Residuals 12 11.4932 0.95777
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 14.143 3.5357 22.848 1.541e-05 ***
## Residuals 12 1.857 0.1548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 13.4089 3.3522 15.525 0.0001087 ***
## Residuals 12 2.5911 0.2159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 13.4189 3.3547 15.597 0.0001063 ***
## Residuals 12 2.5811 0.2151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 13.2534 3.3134 14.476 0.0001528 ***
## Residuals 12 2.7466 0.2289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 8.8401 2.21002 3.704 0.03465 *
## Residuals 12 7.1599 0.59666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 4.6399 1.15998 1.2253 0.351
## Residuals 12 11.3601 0.94667
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 2.3103 0.57757 0.5063 0.7322
## Residuals 12 13.6897 1.14081
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 0.8075 0.20186 0.1594 0.9549
## Residuals 12 15.1925 1.26605
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 10.6268 2.65670 5.9332 0.007151 **
## Residuals 12 5.3732 0.44777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 4.887 1.22175 1.3193 0.318
## Residuals 12 11.113 0.92608
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 6.2432 1.56080 1.9196 0.1718
## Residuals 12 9.7568 0.81307
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 5.1686 1.29215 1.4316 0.2828
## Residuals 12 10.8314 0.90262
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 9.137 2.28426 3.9941 0.02757 *
## Residuals 12 6.863 0.57191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 9.0384 2.25961 3.895 0.02978 *
## Residuals 12 6.9616 0.58013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$FF 4 2.8685 0.71714 0.6553 0.6343
## Residuals 12 13.1315 1.09429anovasimple <-aov(z~datos2$simple)
summary(anovasimple)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 1.9839 1.98389 2.1231 0.1657
## Residuals 15 14.0161 0.93441
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.8492 0.84916 0.8407 0.3737
## Residuals 15 15.1508 1.01006
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 8.3727 8.3727 16.466 0.001031 **
## Residuals 15 7.6273 0.5085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 8.3874 8.3874 16.526 0.001015 **
## Residuals 15 7.6126 0.5075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 2.1486 2.14862 2.3268 0.148
## Residuals 15 13.8514 0.92343
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.5645 0.56453 0.5486 0.4703
## Residuals 15 15.4355 1.02903
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.4891 0.48914 0.473 0.5021
## Residuals 15 15.5109 1.03406
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.7094 0.70937 0.6959 0.4173
## Residuals 15 15.2906 1.01938
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 3.3268 3.3268 3.9376 0.06582 .
## Residuals 15 12.6732 0.8449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.1312 0.13119 0.124 0.7296
## Residuals 15 15.8688 1.05792
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 3.5769 3.5769 4.3189 0.05527 .
## Residuals 15 12.4231 0.8282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 4.0643 4.0643 5.1077 0.03913 *
## Residuals 15 11.9357 0.7957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 1.2314 1.23136 1.2506 0.281
## Residuals 15 14.7686 0.98458
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.449 0.44896 0.433 0.5205
## Residuals 15 15.551 1.03674
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 5.4754 5.4754 7.8037 0.01364 *
## Residuals 15 10.5246 0.7016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$simple 1 0.5133 0.51326 0.4971 0.4916
## Residuals 15 15.4867 1.03245anovaward <- aov(z~datos2$ward)
summary(anovaward)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.4364 0.43643 0.4206 0.5264
## Residuals 15 15.5636 1.03757
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 8.4547 8.4547 16.808 0.0009468 ***
## Residuals 15 7.5453 0.5030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 6.3243 6.3243 9.8044 0.006865 **
## Residuals 15 9.6757 0.6450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 6.3217 6.3217 9.7976 0.00688 **
## Residuals 15 9.6783 0.6452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.0975 0.09747 0.0919 0.7659
## Residuals 15 15.9025 1.06017
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 2.2256 2.22561 2.4236 0.1404
## Residuals 15 13.7744 0.91829
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.0733 0.07326 0.069 0.7964
## Residuals 15 15.9267 1.06178
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.5855 0.58551 0.5698 0.462
## Residuals 15 15.4145 1.02763
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 1.3562 1.35624 1.3892 0.2569
## Residuals 15 14.6438 0.97625
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.2581 0.25811 0.246 0.6271
## Residuals 15 15.7419 1.04946
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 4.5831 4.5831 6.0215 0.02684 *
## Residuals 15 11.4169 0.7611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 4.6227 4.6227 6.0946 0.02606 *
## Residuals 15 11.3773 0.7585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.2495 0.24948 0.2376 0.633
## Residuals 15 15.7505 1.05003
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.0848 0.0848 0.0799 0.7813
## Residuals 15 15.9152 1.0610
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 4.3289 4.3289 5.5636 0.03233 *
## Residuals 15 11.6711 0.7781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ward 1 0.2832 0.28321 0.2703 0.6107
## Residuals 15 15.7168 1.04779anovaKM <- aov(z~datos2$Kmeans)
summary(anovaKM)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 7.3229 1.83072 2.5318 0.0953 .
## Residuals 12 8.6771 0.72309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 13.3816 3.3454 15.332 0.0001156 ***
## Residuals 12 2.6184 0.2182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 8.561 2.14025 3.4525 0.04253 *
## Residuals 12 7.439 0.61992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 8.5652 2.14129 3.4561 0.0424 *
## Residuals 12 7.4348 0.61957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 1.5705 0.39262 0.3265 0.8549
## Residuals 12 14.4295 1.20246
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 10.0165 2.50412 5.022 0.01301 *
## Residuals 12 5.9835 0.49863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 5.8346 1.45866 1.7219 0.2097
## Residuals 12 10.1654 0.84711
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 4.9582 1.23956 1.3471 0.3089
## Residuals 12 11.0418 0.92015
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 6.2157 1.55393 1.9058 0.1742
## Residuals 12 9.7843 0.81536
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 1.9683 0.49207 0.4208 0.7907
## Residuals 12 14.0317 1.16931
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 4.5122 1.12806 1.1784 0.3688
## Residuals 12 11.4878 0.95731
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 4.265 1.06624 1.0903 0.4046
## Residuals 12 11.735 0.97792
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 7.8757 1.96893 2.9082 0.06776 .
## Residuals 12 8.1243 0.67702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 9.2985 2.32462 4.1625 0.02423 *
## Residuals 12 6.7015 0.55846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 8.6203 2.15508 3.5044 0.04075 *
## Residuals 12 7.3797 0.61497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Kmeans 4 7.1972 1.79929 2.4528 0.1026
## Residuals 12 8.8028 0.73357anovaMapa <-aov(z~datos2$ClusMapa)
summary(anovaMapa)## Response proppobla :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 10.0306 10.031 25.205 0.0001522 ***
## Residuals 15 5.9694 0.398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response propSuperf :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 3.3658 3.3658 3.9961 0.06406 .
## Residuals 15 12.6342 0.8423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratioPobsup :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 2.5781 2.57809 2.8812 0.1103
## Residuals 15 13.4219 0.89479
##
## Response densidad :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 2.576 2.57598 2.8784 0.1104
## Residuals 15 13.424 0.89493
##
## Response maxcompra :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 1.927 1.9270 2.0539 0.1723
## Residuals 15 14.073 0.9382
##
## Response ratioVentasCompras :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 1.5849 1.58491 1.6492 0.2185
## Residuals 15 14.4151 0.96101
##
## Response proporcionvtasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 11.2583 11.2583 35.615 2.579e-05 ***
## Residuals 15 4.7417 0.3161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response proporcioncomprasT :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 10.5815 10.5815 29.292 7.2e-05 ***
## Residuals 15 5.4185 0.3612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response ratiomaximaventaTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 0.5598 0.55977 0.5438 0.4722
## Residuals 15 15.4402 1.02935
##
## Response ratiomaxcompraTotal :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 2.8268 2.82680 3.2188 0.09297 .
## Residuals 15 13.1732 0.87821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response indicegastopersona :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 1.8589 1.85885 1.9717 0.1806
## Residuals 15 14.1411 0.94274
##
## Response indicegastoporhoga :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 2.8473 2.84730 3.2472 0.09167 .
## Residuals 15 13.1527 0.87685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response tempIndus :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 0.0118 0.01179 0.0111 0.9176
## Residuals 15 15.9882 1.06588
##
## Response tempConstr :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 2.57 2.56999 2.8704 0.1109
## Residuals 15 13.43 0.89533
##
## Response tempServ :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 0.5319 0.5319 0.5158 0.4837
## Residuals 15 15.4681 1.0312
##
## Response tbuscandoempleo :
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$ClusMapa 1 0.3889 0.3889 0.3737 0.5502
## Residuals 15 15.6111 1.0407TUKEY Ward
#Proporcion población
propsuperf_ward <- TukeyHSD(aov(propSuperf~ward_factor, data = datos2))
propsuperf_ward## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ ward_factor, data = datos2)
##
## $ward_factor
## diff lwr upr p adj
## 2-1 -1.29186733 -2.7698039 0.1860692 0.0978412
## 3-1 -2.46251833 -3.3675659 -1.5574707 0.0000133
## 4-1 -2.26602600 -3.3110850 -1.2209670 0.0001297
## 5-1 -2.27778758 -3.2553507 -1.3002244 0.0000642
## 3-2 -1.17065100 -2.5531341 0.2118321 0.1127648
## 4-2 -0.97415867 -2.4520952 0.5037779 0.2804527
## 5-2 -0.98592025 -2.4169262 0.4450857 0.2449442
## 4-3 0.19649233 -0.7085553 1.1015399 0.9544267
## 5-3 0.18473075 -0.6414609 1.0109224 0.9495720
## 5-4 -0.01176158 -0.9893247 0.9658016 0.9999994plot(propsuperf_ward)
# Densidad
densi_ward <- TukeyHSD(aov(densidad~ward_factor, data = datos2))
densi_ward## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ ward_factor, data = datos2)
##
## $ward_factor
## diff lwr upr p adj
## 2-1 -0.1098290 -3.0945479 2.874890 0.9999504
## 3-1 0.2230140 -1.6047456 2.050774 0.9944888
## 4-1 0.6813517 -1.4291633 2.791867 0.8373758
## 5-1 1.7890128 -0.1851932 3.763219 0.0826508
## 3-2 0.3328430 -2.4591058 3.124792 0.9949566
## 4-2 0.7911807 -2.1935382 3.775900 0.9111899
## 5-2 1.8988418 -0.9910999 4.788783 0.2830677
## 4-3 0.4583377 -1.3694219 2.286097 0.9258698
## 5-3 1.5659988 -0.1025098 3.234507 0.0695871
## 5-4 1.1076611 -0.8665449 3.081867 0.4225207plot(densi_ward)
# Tasa empleo servicios
tempServ_ward <- TukeyHSD(aov(tempServ~ward_factor, data = datos2))
tempServ_ward## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ ward_factor, data = datos2)
##
## $ward_factor
## diff lwr upr p adj
## 2-1 -0.2801260 -3.4077091 2.847457 0.9983304
## 3-1 -0.1372622 -2.0525078 1.777983 0.9993031
## 4-1 1.3109907 -0.9005445 3.522526 0.3724635
## 5-1 1.1401140 -0.9285877 3.208816 0.4389248
## 3-2 0.1428638 -2.7827222 3.068450 0.9998472
## 4-2 1.5911167 -1.5364664 4.718700 0.5121217
## 5-2 1.4202400 -1.6080293 4.448509 0.5841831
## 4-3 1.4482528 -0.4669928 3.363498 0.1779457
## 5-3 1.2773762 -0.4709959 3.025748 0.2013848
## 5-4 -0.1708767 -2.2395784 1.897825 0.9987832plot(tempServ_ward)
Simple
#Proporcion población
propsuperf_simple <- TukeyHSD(aov(propSuperf~simple_factor, data = datos2))
propsuperf_simple## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ simple_factor, data = datos2)
##
## $simple_factor
## diff lwr upr p adj
## 2-1 0.5007039 -3.114968 4.116375 0.9910925
## 3-1 -0.9007561 -4.516428 2.714915 0.9274547
## 4-1 -0.3013161 -3.916988 3.314355 0.9987402
## 5-1 -0.8011261 -4.416798 2.814545 0.9511187
## 3-2 -1.4014600 -6.328790 3.525870 0.8889823
## 4-2 -0.8020200 -5.729350 4.125310 0.9837356
## 5-2 -1.3018300 -6.229160 3.625500 0.9121263
## 4-3 0.5994400 -4.327890 5.526770 0.9945504
## 5-3 0.0996300 -4.827700 5.026960 0.9999955
## 5-4 -0.4998100 -5.427140 4.427520 0.9972917plot(propsuperf_simple)
# Densidad
densi_simple <- TukeyHSD(aov(densidad~simple_factor, data = datos2))
densi_simple## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ simple_factor, data = datos2)
##
## $simple_factor
## diff lwr upr p adj
## 2-1 -0.4746562 -2.1201354 1.170823 0.8840183
## 3-1 0.5659898 -1.0794894 2.211469 0.8052166
## 4-1 0.4884848 -1.1569944 2.133964 0.8733223
## 5-1 3.6464798 2.0010006 5.291959 0.0001051
## 3-2 1.0406460 -1.2017642 3.283056 0.5932292
## 4-2 0.9631410 -1.2792692 3.205551 0.6568653
## 5-2 4.1211360 1.8787258 6.363546 0.0006024
## 4-3 -0.0775050 -2.3199152 2.164905 0.9999614
## 5-3 3.0804900 0.8380798 5.322900 0.0065243
## 5-4 3.1579950 0.9155848 5.400405 0.0054206plot(densi_simple)
# Tasa empleo servicios
tempServ_simple <- TukeyHSD(aov(tempServ~simple_factor, data = datos2))
tempServ_simple## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ simple_factor, data = datos2)
##
## $simple_factor
## diff lwr upr p adj
## 2-1 -0.4641472 -2.9953680 2.067074 0.9748963
## 3-1 2.0569888 -0.4742320 4.588210 0.1342885
## 4-1 0.3258088 -2.2054120 2.857030 0.9932447
## 5-1 2.3427178 -0.1885030 4.873939 0.0745938
## 3-2 2.5211360 -0.9283365 5.970608 0.2011274
## 4-2 0.7899560 -2.6595165 4.239428 0.9452838
## 5-2 2.8068650 -0.6426075 6.256337 0.1335672
## 4-3 -1.7311800 -5.1806525 1.718292 0.5243952
## 5-3 0.2857290 -3.1637435 3.735201 0.9987698
## 5-4 2.0169090 -1.4325635 5.466381 0.3848865plot(tempServ_simple)
Comlete
#Proporcion población
propsuperf_complete <- TukeyHSD(aov(propSuperf~complete_factor, data = datos2))
propsuperf_complete## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ complete_factor, data = datos2)
##
## $complete_factor
## diff lwr upr p adj
## 2-1 -2.29528248 -3.3907434 -1.1998216 0.0001800
## 3-1 -2.26602600 -3.5621928 -0.9698592 0.0009355
## 4-1 -2.17248433 -3.4686511 -0.8763175 0.0013433
## 5-1 -2.59369733 -4.4267540 -0.7606407 0.0052313
## 3-2 0.02925648 -1.0662044 1.1247174 0.9999862
## 4-2 0.12279814 -0.9726627 1.2182590 0.9960172
## 5-2 -0.29841486 -1.9954956 1.3986658 0.9784276
## 4-3 0.09354167 -1.2026251 1.3897085 0.9992838
## 5-3 -0.32767133 -2.1607280 1.5053853 0.9771019
## 5-4 -0.42121300 -2.2542697 1.4118437 0.9446488plot(propsuperf_complete)
# Densidad
densi_complete <- TukeyHSD(aov(densidad~complete_factor, data = datos2))
densi_complete## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ complete_factor, data = datos2)
##
## $complete_factor
## diff lwr upr p adj
## 2-1 0.1754650 -0.5763061 0.9272361 0.9416451
## 3-1 0.6813517 -0.2081559 1.5708592 0.1695779
## 4-1 1.0482480 0.1587404 1.9377556 0.0188652
## 5-1 4.0113070 2.7533533 5.2692607 0.0000025
## 3-2 0.5058867 -0.2458844 1.2576578 0.2635842
## 4-2 0.8727830 0.1210119 1.6245541 0.0207620
## 5-2 3.8358420 2.6712032 5.0004808 0.0000018
## 4-3 0.3668963 -0.5226112 1.2564039 0.6879255
## 5-3 3.3299553 2.0720017 4.5879090 0.0000177
## 5-4 2.9630590 1.7051053 4.2210127 0.0000577plot(densi_complete)
# Tasa empleo servicios
tempServ_complete <- TukeyHSD(aov(tempServ~complete_factor, data = datos2))
tempServ_complete## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ complete_factor, data = datos2)
##
## $complete_factor
## diff lwr upr p adj
## 2-1 -0.1576713 -1.7283331 1.412991 0.9973988
## 3-1 1.3109907 -0.5474415 3.169423 0.2269068
## 4-1 0.6779057 -1.1805265 2.536338 0.7713844
## 5-1 2.5267390 -0.1014809 5.154959 0.0615781
## 3-2 1.4686620 -0.1019999 3.039324 0.0709004
## 4-2 0.8355770 -0.7350849 2.406239 0.4713650
## 5-2 2.6844103 0.2511515 5.117669 0.0284878
## 4-3 -0.6330850 -2.4915171 1.225347 0.8104125
## 5-3 1.2157483 -1.4124716 3.843968 0.5959903
## 5-4 1.8488333 -0.7793866 4.477053 0.2290126plot(tempServ_complete)
K-means
#Proporcion población
propsuperf_kmeans <- TukeyHSD(aov(propSuperf~Kmeans, data = datos2))
propsuperf_kmeans## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ Kmeans, data = datos2)
##
## $Kmeans
## diff lwr upr p adj
## 2-1 2.693327 0.9740962 4.4125585 0.0023539
## 3-1 0.357422 -1.2217927 1.9366367 0.9474322
## 4-1 0.349535 -1.4739850 2.1730550 0.9705735
## 5-1 0.723977 -0.9952542 2.4432082 0.6722377
## 3-2 -2.335905 -3.3438940 -1.3279167 0.0000678
## 4-2 -2.343792 -3.7029639 -0.9846208 0.0010534
## 5-2 -1.969350 -3.1850304 -0.7536703 0.0017876
## 4-3 -0.007887 -1.1849641 1.1691901 0.9999999
## 5-3 0.366555 -0.6414336 1.3745436 0.7732587
## 5-4 0.374442 -0.9847296 1.7336136 0.8995673plot(propsuperf_kmeans)
# Densidad
densi_kmeans <- TukeyHSD(aov(densidad~Kmeans, data = datos2))
densi_kmeans## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ Kmeans, data = datos2)
##
## $Kmeans
## diff lwr upr p adj
## 2-1 -0.9308170 -3.8278643 1.9662303 0.8396199
## 3-1 -0.6128260 -3.2739339 2.0482819 0.9442369
## 4-1 1.5014925 -1.5712902 4.5742752 0.5482699
## 5-1 -0.2344847 -3.1315320 2.6625626 0.9988763
## 3-2 0.3179910 -1.3805535 2.0165355 0.9729531
## 4-2 2.4323095 0.1419925 4.7226265 0.0356884
## 5-2 0.6963323 -1.3521895 2.7448541 0.8115600
## 4-3 2.1143185 0.1308458 4.0977912 0.0349230
## 5-3 0.3783413 -1.3202032 2.0768859 0.9502246
## 5-4 -1.7359772 -4.0262942 0.5543398 0.1763918plot(densi_kmeans)
# Tasa empleo servicios
tempServ_kmeans <- TukeyHSD(aov(tempServ~Kmeans, data = datos2))
tempServ_kmeans## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ Kmeans, data = datos2)
##
## $Kmeans
## diff lwr upr p adj
## 2-1 -2.24101000 -5.1272875 0.6452675 0.1610437
## 3-1 -2.29633513 -4.9475503 0.3548800 0.1019882
## 4-1 -0.72272550 -3.7840851 2.3386341 0.9393578
## 5-1 -1.38942633 -4.2757038 1.4968511 0.5614745
## 3-2 -0.05532513 -1.7475553 1.6369050 0.9999690
## 4-2 1.51828450 -0.7635182 3.8000872 0.2727068
## 5-2 0.85158367 -1.1893227 2.8924900 0.6792430
## 4-3 1.57360963 -0.4024895 3.5497087 0.1458249
## 5-3 0.90690879 -0.7853214 2.5991390 0.4646065
## 5-4 -0.66670083 -2.9485035 1.6151019 0.8793416plot(tempServ_kmeans)
FF
#Proporcion población
propsuperf_FF <- TukeyHSD(aov(propSuperf~FF, data = datos2))
propsuperf_FF## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ FF, data = datos2)
##
## $FF
## diff lwr upr p adj
## 2-1 1.0169794 -0.2926724 2.3266311 0.1609715
## 3-1 -0.3844806 -1.6941324 0.9251711 0.8775665
## 4-1 -0.2848506 -1.5945024 1.0248011 0.9541406
## 5-1 2.3088467 1.4921354 3.1255580 0.0000089
## 3-2 -1.4014600 -3.1747368 0.3718168 0.1502781
## 4-2 -1.3018300 -3.0751068 0.4714468 0.1980034
## 5-2 1.2918673 -0.1560071 2.7397418 0.0889589
## 4-3 0.0996300 -1.6736468 1.8729068 0.9997335
## 5-3 2.6933273 1.2454529 4.1412018 0.0005406
## 5-4 2.5936973 1.1458229 4.0415718 0.0007559plot(propsuperf_FF)
# Densidad
densi_FF <- TukeyHSD(aov(densidad~FF, data = datos2))
densi_FF## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ FF, data = datos2)
##
## $FF
## diff lwr upr p adj
## 2-1 -0.6185623 -2.1625710 0.9254465 0.7092372
## 3-1 0.4220837 -1.1219250 1.9660925 0.9020009
## 4-1 3.5025737 1.9585650 5.0465825 0.0000836
## 5-1 -0.5087333 -1.4715919 0.4541253 0.4776432
## 3-2 1.0406460 -1.0499517 3.1312437 0.5317532
## 4-2 4.1211360 2.0305383 6.2117337 0.0003187
## 5-2 0.1098290 -1.5971369 1.8167949 0.9995444
## 4-3 3.0804900 0.9898923 5.1710877 0.0038327
## 5-3 -0.9308170 -2.6377829 0.7761489 0.4486760
## 5-4 -4.0113070 -5.7182729 -2.3043411 0.0000591plot(densi_FF)
# Tasa empleo servicios
tempServ_FF <- TukeyHSD(aov(tempServ~FF, data = datos2))
tempServ_FF## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ FF, data = datos2)
##
## $FF
## diff lwr upr p adj
## 2-1 -0.5439538 -3.0796571 1.9917494 0.9562796
## 3-1 1.9771822 -0.5585211 4.5128854 0.1584256
## 4-1 2.2629112 -0.2727921 4.7986144 0.0888800
## 5-1 -0.2638278 -1.8451166 1.3174610 0.9821895
## 3-2 2.5211360 -0.9122224 5.9544944 0.1978436
## 4-2 2.8068650 -0.6264934 6.2402234 0.1310005
## 5-2 0.2801260 -2.5231994 3.0834514 0.9974446
## 4-3 0.2857290 -3.1476294 3.7190874 0.9987470
## 5-3 -2.2410100 -5.0443354 0.5623154 0.1435765
## 5-4 -2.5267390 -5.3300644 0.2765864 0.0847994plot(tempServ_FF)
Mapa
#Proporcion población
propsuperf_mapa <- TukeyHSD(aov(propSuperf~Mapa_factor, data = datos2))
propsuperf_mapa## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = propSuperf ~ Mapa_factor, data = datos2)
##
## $Mapa_factor
## diff lwr upr p adj
## 2-1 1.1598575 -1.5959935 3.915708476 0.7265985
## 3-1 2.1901965 -0.5656545 4.946047476 0.1430905
## 4-1 -0.4217665 -3.7969808 2.953447849 0.9996935
## 5-1 -0.3188155 -3.6940298 3.056398849 0.9999613
## 6-1 0.1461235 -2.6097275 2.901974476 0.9999996
## 7-1 0.9066315 -2.4685828 4.281845849 0.9592508
## 8-1 -0.1951090 -2.5817460 2.191527954 0.9999871
## 9-1 -0.4898470 -3.2456980 2.266003976 0.9964925
## 3-2 1.0303390 -1.7255120 3.786189976 0.8187560
## 4-2 -1.5816240 -4.9568383 1.793590349 0.6255940
## 5-2 -1.4786730 -4.8538873 1.896541349 0.6905358
## 6-2 -1.0137340 -3.7695850 1.742116976 0.8295960
## 7-2 -0.2532260 -3.6284403 3.121988349 0.9999933
## 8-2 -1.3549665 -3.7416035 1.031670454 0.4257903
## 9-2 -1.6497045 -4.4055555 1.106146476 0.3718148
## 4-3 -2.6119630 -5.9871773 0.763251349 0.1591258
## 5-3 -2.5090120 -5.8842263 0.866202349 0.1856229
## 6-3 -2.0440730 -4.7999240 0.711777976 0.1871601
## 7-3 -1.2835650 -4.6587793 2.091649349 0.8069214
## 8-3 -2.3853055 -4.7719425 0.001331454 0.0501418
## 9-3 -2.6800435 -5.4358945 0.075807476 0.0575137
## 5-4 0.1029510 -3.7944108 4.000312826 1.0000000
## 6-4 0.5678900 -2.8073243 3.943104349 0.9975607
## 7-4 1.3283980 -2.5689638 5.225759826 0.8745279
## 8-4 0.2266575 -2.8544776 3.307792559 0.9999943
## 9-4 -0.0680805 -3.4432948 3.307133849 1.0000000
## 6-5 0.4649390 -2.9102753 3.840153349 0.9993848
## 7-5 1.2254470 -2.6719148 5.122808826 0.9118613
## 8-5 0.1237065 -2.9574286 3.204841559 1.0000000
## 9-5 -0.1710315 -3.5462458 3.204182849 0.9999997
## 7-6 0.7605080 -2.6147063 4.135722349 0.9846480
## 8-6 -0.3412325 -2.7278695 2.045404454 0.9992004
## 9-6 -0.6359705 -3.3918215 2.119880476 0.9823548
## 8-7 -1.1017405 -4.1828756 1.979394559 0.8474234
## 9-7 -1.3964785 -4.7716928 1.978735849 0.7412562
## 9-8 -0.2947380 -2.6813750 2.091898954 0.9997186plot(propsuperf_mapa)
# Densidad
densi_mapa <- TukeyHSD(aov(densidad~Mapa_factor, data = datos2))
densi_mapa## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = densidad ~ Mapa_factor, data = datos2)
##
## $Mapa_factor
## diff lwr upr p adj
## 2-1 -1.9461335 -5.468298 1.5760310 0.4540735
## 3-1 -2.6142765 -6.136441 0.9078880 0.1866773
## 4-1 -1.1778600 -5.491613 3.1358929 0.9556363
## 5-1 -2.4220030 -6.735756 1.8917499 0.4373310
## 6-1 -1.9369140 -5.459079 1.5852505 0.4590549
## 7-1 -2.6033620 -6.917115 1.7103909 0.3637363
## 8-1 -2.2541515 -5.304435 0.7961325 0.1897350
## 9-1 -1.8736105 -5.395775 1.6485540 0.4940540
## 3-2 -0.6681430 -4.190308 2.8540215 0.9946575
## 4-2 0.7682735 -3.545479 5.0820264 0.9964470
## 5-2 -0.4758695 -4.789622 3.8378834 0.9998767
## 6-2 0.0092195 -3.512945 3.5313840 1.0000000
## 7-2 -0.6572285 -4.970981 3.6565244 0.9987563
## 8-2 -0.3080180 -3.358302 2.7422660 0.9999361
## 9-2 0.0725230 -3.449642 3.5946875 1.0000000
## 4-3 1.4364165 -2.877336 5.7501694 0.8863650
## 5-3 0.1922735 -4.121479 4.5060264 0.9999999
## 6-3 0.6773625 -2.844802 4.1995270 0.9941707
## 7-3 0.0109145 -4.302838 4.3246674 1.0000000
## 8-3 0.3601250 -2.690159 3.4104090 0.9997971
## 9-3 0.7406660 -2.781499 4.2628305 0.9898398
## 5-4 -1.2441430 -6.225236 3.7369498 0.9724160
## 6-4 -0.7590540 -5.072807 3.5546989 0.9967175
## 7-4 -1.4255020 -6.406595 3.5555908 0.9437210
## 8-4 -1.0762915 -5.014191 2.8616082 0.9554108
## 9-4 -0.6957505 -5.009503 3.6180024 0.9981647
## 6-5 0.4850890 -3.828664 4.7988419 0.9998580
## 7-5 -0.1813590 -5.162452 4.7997338 1.0000000
## 8-5 0.1678515 -3.770048 4.1057512 0.9999999
## 9-5 0.5483925 -3.765360 4.8621454 0.9996531
## 7-6 -0.6664480 -4.980201 3.6473049 0.9986313
## 8-6 -0.3172375 -3.367521 2.7330465 0.9999204
## 9-6 0.0633035 -3.458861 3.5854680 1.0000000
## 8-7 0.3492105 -3.588689 4.2871102 0.9999760
## 9-7 0.7297515 -3.584001 5.0435044 0.9974708
## 9-8 0.3805410 -2.669743 3.4308250 0.9996971plot(densi_mapa)
# Tasa empleo servicios
tempServ_mapa <- TukeyHSD(aov(tempServ~Mapa_factor, data = datos2))
tempServ_mapa## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tempServ ~ Mapa_factor, data = datos2)
##
## $Mapa_factor
## diff lwr upr p adj
## 2-1 -1.30258700 -5.303693 2.698519 0.8969938
## 3-1 -1.83202600 -5.833132 2.169080 0.6484728
## 4-1 -1.03366600 -5.934000 3.866668 0.9896471
## 5-1 -2.36146500 -7.261799 2.538869 0.5972955
## 6-1 -0.84878300 -4.849889 3.152323 0.9892848
## 7-1 -1.97489000 -6.875224 2.925444 0.7629530
## 8-1 -1.78580525 -5.250865 1.679254 0.5274381
## 9-1 -0.47061250 -4.471718 3.530493 0.9998026
## 3-2 -0.52943900 -4.530545 3.471667 0.9995376
## 4-2 0.26892100 -4.631413 5.169255 0.9999994
## 5-2 -1.05887800 -5.959212 3.841456 0.9880252
## 6-2 0.45380400 -3.547302 4.454910 0.9998488
## 7-2 -0.67230300 -5.572637 4.228031 0.9994021
## 8-2 -0.48321825 -3.948278 2.981841 0.9993290
## 9-2 0.83197450 -3.169131 4.833080 0.9905137
## 4-3 0.79836000 -4.101974 5.698694 0.9980353
## 5-3 -0.52943900 -5.429773 4.370895 0.9998943
## 6-3 0.98324300 -3.017863 4.984349 0.9747905
## 7-3 -0.14286400 -5.043198 4.757470 1.0000000
## 8-3 0.04622075 -3.418839 3.511280 1.0000000
## 9-3 1.36141350 -2.639692 5.362519 0.8754310
## 5-4 -1.32779900 -6.986217 4.330619 0.9805734
## 6-4 0.18488300 -4.715451 5.085217 1.0000000
## 7-4 -0.94122400 -6.599642 4.717194 0.9977404
## 8-4 -0.75213925 -5.225512 3.721233 0.9975720
## 9-4 0.56305350 -4.337280 5.463387 0.9998336
## 6-5 1.51268200 -3.387652 6.413016 0.9190253
## 7-5 0.38657500 -5.271843 6.044993 0.9999968
## 8-5 0.57565975 -3.897713 5.049032 0.9996213
## 9-5 1.89085250 -3.009481 6.791186 0.7964246
## 7-6 -1.12610700 -6.026441 3.774227 0.9827796
## 8-6 -0.93702225 -4.402082 2.528037 0.9578029
## 9-6 0.37817050 -3.622935 4.379276 0.9999611
## 8-7 0.18908475 -4.284288 4.662457 0.9999999
## 9-7 1.50427750 -3.396056 6.404611 0.9210980
## 9-8 1.31519275 -2.149867 4.780252 0.8082872plot(tempServ_mapa)