merged <- merged %>%
remove_rownames %>%
filter(Codi != 12) %>%
column_to_rownames(var="Nom_Barri") %>%
select("n.tot","pc.esp","pc.ue27-esp","pc.20.34","2019-2014","n.esp.V1419",
"hotel2019","rest1614",
"RFD.2017",
"tot_ann","pmedio","pmedio.V1519","pm_ent.V1519","pm_priv.V1519",
"alq.num","alq.pm","alq.pm.V1519","alq.num.V1519",
"tot.comp","tot.eur","perc.nou.comp","perc.usat.comp","tot.comp.V1419",
"nou.eur.V1419","usat.eur.V1419",
)
list <- colnames(merged)
minmax <- list
minmax <- as.data.frame(minmax)
list2 <- list()SUMMARY
summary(merged)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. : 686 Min. :43.73 Min. : 0.880 Min. :13.19
## 1st Qu.:11558 1st Qu.:77.85 1st Qu.: 3.030 1st Qu.:17.07
## Median :21791 Median :83.44 Median : 4.947 Median :18.01
## Mean :22905 Mean :80.67 Mean : 7.199 Mean :18.96
## 3rd Qu.:31359 3rd Qu.:85.61 3rd Qu.: 8.534 3rd Qu.:20.11
## Max. :58642 Max. :94.09 Max. :43.067 Max. :36.59
## 2019-2014 n.esp.V1419 hotel2019 rest1614
## Min. :-8766.0 Min. :-11.479 Min. : 0.00 Min. :-75.000
## 1st Qu.: 205.0 1st Qu.: -3.256 1st Qu.: 20.25 1st Qu.: -5.000
## Median : 495.0 Median : -1.528 Median : 73.50 Median : 0.500
## Mean : 664.8 Mean : -1.296 Mean : 279.61 Mean : 1.069
## 3rd Qu.: 980.2 3rd Qu.: -0.157 3rd Qu.: 272.25 3rd Qu.: 6.000
## Max. : 3989.0 Max. : 35.550 Max. :3115.00 Max. : 79.000
## RFD.2017 tot_ann pmedio pmedio.V1519
## Min. : 38.60 Min. : 0.00 Min. : 0.00 Min. :-100.00
## 1st Qu.: 66.83 1st Qu.: 29.75 1st Qu.: 45.37 1st Qu.: -10.54
## Median : 83.10 Median : 99.50 Median : 70.81 Median : 19.29
## Mean : 94.42 Mean : 283.53 Mean : 90.80 Mean : 32.87
## 3rd Qu.:105.80 3rd Qu.: 268.25 3rd Qu.:105.40 3rd Qu.: 53.43
## Max. :248.80 Max. :2099.00 Max. :344.80 Max. : 310.03
## pm_ent.V1519 pm_priv.V1519 alq.num alq.pm
## Min. :-100.0000 Min. :-100.0000 Min. : 2.00 Min. : 0.0
## 1st Qu.: -0.0476 1st Qu.: 0.7378 1st Qu.: 52.75 1st Qu.: 754.3
## Median : 26.5180 Median : 25.4671 Median :109.50 Median : 865.2
## Mean : 53.7896 Mean : 21.4308 Mean :169.62 Mean : 869.9
## 3rd Qu.: 85.4627 3rd Qu.: 39.2555 3rd Qu.:249.75 3rd Qu.:1002.3
## Max. : 590.4439 Max. : 150.1412 Max. :659.00 Max. :1950.1
## alq.pm.V1519 alq.num.V1519 tot.comp tot.eur
## Min. : 0.00 Min. :-50.00 Min. : 5.0 Min. : 0.0
## 1st Qu.:29.82 1st Qu.: 19.11 1st Qu.: 80.0 1st Qu.: 193.2
## Median :37.83 Median : 42.79 Median :153.0 Median : 267.8
## Mean :35.09 Mean : 49.45 Mean :187.7 Mean : 312.7
## 3rd Qu.:44.33 3rd Qu.: 70.91 3rd Qu.:259.8 3rd Qu.: 369.3
## Max. :62.78 Max. :200.00 Max. :619.0 Max. :1125.3
## perc.nou.comp perc.usat.comp tot.comp.V1419 nou.eur.V1419
## Min. : 0.000 Min. : 50.00 Min. :-45.882 Min. :-100.000
## 1st Qu.: 0.000 1st Qu.: 86.65 1st Qu.: 3.823 1st Qu.: -1.095
## Median : 2.683 Median : 97.21 Median : 35.990 Median : 0.000
## Mean : 7.794 Mean : 91.99 Mean : 55.980 Mean : 22.117
## 3rd Qu.:12.123 3rd Qu.:100.00 3rd Qu.: 71.808 3rd Qu.: 74.119
## Max. :50.000 Max. :100.00 Max. :466.667 Max. : 472.729
## usat.eur.V1419
## Min. :-38.12
## 1st Qu.: 31.25
## Median : 42.34
## Mean : 49.42
## 3rd Qu.: 67.68
## Max. :201.26for (i in 1:25)
{
IRQ <- IQR(merged[,i])
Q <- quantile(merged[,i], c(0.25,0.5,0.75), type=7)
minmax[i,2] <- outl_min<-as.numeric(Q[1])-3*IRQ
minmax[i,3] <- outl_max<-as.numeric(Q[3])+3*IRQ
#bp <- boxplot(merged[,1][merged[,1]>outl_min & merged[,1]<outl_max], decreasing = FALSE)
out <- print(which(merged[,i] < outl_min | merged[,i] > outl_max))
if (length(out) != 0)
{
bout <-merged[which(merged[,i] < outl_min | merged[,i] > outl_max), ]
# print(bout)
assign(list[i], bout)
list2 <- append(list2, list[i])
}
}## integer(0)
## [1] 1 2
## [1] 2 3 4
## [1] 1 2 3 4
## [1] 2 67 68
## [1] 41
## [1] 1 2 6 7 8 30
## [1] 1 2 4 7 11 14 16 17 30 58 66 68
## [1] 20
## [1] 1 2 4 6 7 8 10 11 30
## [1] 24
## [1] 24
## [1] 24
## integer(0)
## integer(0)
## [1] 20 41 46 53 55 57
## integer(0)
## integer(0)
## integer(0)
## [1] 20
## [1] 41
## integer(0)
## [1] 38 46 54
## [1] 5
## [1] 11VALORES MINMAX outl_min<-(Q[1])-1.5IRQ outl_max<-(Q[3])-1.5IRQ
minmax## minmax V2 V3
## 1 n.tot -47846.000000 90762.750000
## 2 pc.esp 54.538055 108.922012
## 3 pc.ue27-esp -13.481301 25.045958
## 4 pc.20.34 7.963631 29.214940
## 5 2019-2014 -2120.750000 3306.000000
## 6 n.esp.V1419 -12.551570 9.138918
## 7 hotel2019 -735.750000 1028.250000
## 8 rest1614 -38.000000 39.000000
## 9 RFD.2017 -50.100000 222.725000
## 10 tot_ann -685.750000 983.750000
## 11 pmedio -134.720993 285.497382
## 12 pmedio.V1519 -202.428536 245.319966
## 13 pm_ent.V1519 -256.578237 341.993357
## 14 pm_priv.V1519 -114.815525 154.808832
## 15 alq.num -538.250000 840.750000
## 16 alq.pm 10.161773 1746.465743
## 17 alq.pm.V1519 -13.722392 87.874814
## 18 alq.num.V1519 -136.275462 226.291963
## 19 tot.comp -459.250000 799.000000
## 20 tot.eur -335.175000 897.700000
## 21 perc.nou.comp -36.370510 48.494014
## 22 perc.usat.comp 46.614173 140.039370
## 23 tot.comp.V1419 -200.132525 275.763269
## 24 nou.eur.V1419 -226.735623 299.760008
## 25 usat.eur.V1419 -78.045241 176.973360OUTLIERS DE CADA VARIABLE solo n.tot no tiene
for (i in list2)
{x <- get(i)
x <- x[i]
print(x)
}## pc.esp
## el Raval 48.09823
## el Barri Gòtic 43.72784
## pc.ue27-esp
## el Barri Gòtic 43.06665
## la Barceloneta 28.23924
## Sant Pere, Santa Caterina i la Ribera 36.62403
## pc.20.34
## el Raval 29.23370
## el Barri Gòtic 36.59020
## la Barceloneta 30.46200
## Sant Pere, Santa Caterina i la Ribera 30.17264
## 2019-2014
## el Barri Gòtic -8766
## el Poblenou 3989
## Diagonal Mar i el Front Marítim del Poblenou 3850
## n.esp.V1419
## la Clota 35.55046
## hotel2019
## el Raval 1085
## el Barri Gòtic 1112
## la Sagrada Família 1106
## la Dreta de l'Eixample 3115
## l'Antiga Esquerra de l'Eixample 1559
## la Vila de Gràcia 1327
## rest1614
## el Raval -75
## el Barri Gòtic -59
## Sant Pere, Santa Caterina i la Ribera -41
## la Dreta de l'Eixample 79
## el Poble Sec -48
## Hostafrancs 62
## Sants - Badal 43
## Sants 68
## la Vila de Gràcia -65
## el Bon Pastor 44
## la Vila Olímpica del Poblenou 68
## Diagonal Mar i el Front Marítim del Poblenou 45
## RFD.2017
## Pedralbes 248.8
## tot_ann
## el Raval 1781
## el Barri Gòtic 1423
## Sant Pere, Santa Caterina i la Ribera 1273
## la Sagrada Família 1101
## la Dreta de l'Eixample 2099
## l'Antiga Esquerra de l'Eixample 1079
## Sant Antoni 999
## el Poble Sec 1112
## la Vila de Gràcia 1122
## pmedio
## Sant Gervasi - la Bonanova 344.7963
## pmedio.V1519
## Sant Gervasi - la Bonanova 310.028
## pm_ent.V1519
## Sant Gervasi - la Bonanova 590.4439
## alq.pm
## Pedralbes 1950.055
## la Clota 0.000
## Can Peguera 0.000
## Torre Baró 0.000
## Vallbona 0.000
## Baró de Viver 0.000
## tot.eur
## Pedralbes 1125.3
## perc.nou.comp
## la Clota 50
## tot.comp.V1419
## Sant Genís dels Agudells 466.6667
## Can Peguera 300.0000
## Ciutat Meridiana 293.7500
## nou.eur.V1419
## el Fort Pienc 472.7294
## usat.eur.V1419
## el Poble Sec 201.2632SCALED
merged.sc <- scale(merged)
summary(merged.sc)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. :-1.51816 Min. :-4.0958 Min. :-0.8614 Min. :-1.4824
## 1st Qu.:-0.77532 1st Qu.:-0.3127 1st Qu.:-0.5683 1st Qu.:-0.4843
## Median :-0.07612 Median : 0.3075 Median :-0.3070 Median :-0.2420
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.57764 3rd Qu.: 0.5487 3rd Qu.: 0.1820 3rd Qu.: 0.2961
## Max. : 2.44180 Max. : 1.4883 Max. : 4.8896 Max. : 4.5333
## 2019-2014 n.esp.V1419 hotel2019 rest1614
## Min. :-6.2892 Min. :-1.95588 Min. :-0.56748 Min. :-2.89006
## 1st Qu.:-0.3066 1st Qu.:-0.37645 1st Qu.:-0.52638 1st Qu.:-0.23059
## Median :-0.1132 Median :-0.04467 Median :-0.41831 Median :-0.02163
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.2104 3rd Qu.: 0.21868 3rd Qu.:-0.01494 3rd Qu.: 0.18732
## Max. : 2.2169 Max. : 7.07674 Max. : 5.75454 Max. : 2.96077
## RFD.2017 tot_ann pmedio pmedio.V1519
## Min. :-1.3124 Min. :-0.63599 Min. :-1.3446 Min. :-1.8782
## 1st Qu.:-0.6488 1st Qu.:-0.56926 1st Qu.:-0.6727 1st Qu.:-0.6136
## Median :-0.2661 Median :-0.41280 Median :-0.2961 Median :-0.1919
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2677 3rd Qu.:-0.03427 3rd Qu.: 0.2162 3rd Qu.: 0.2906
## Max. : 3.6301 Max. : 4.07236 Max. : 3.7610 Max. : 3.9180
## pm_ent.V1519 pm_priv.V1519 alq.num alq.pm
## Min. :-1.5326 Min. :-3.0446 Min. :-1.0535 Min. :-2.51910
## 1st Qu.:-0.5365 1st Qu.:-0.5188 1st Qu.:-0.7346 1st Qu.:-0.33488
## Median :-0.2718 Median : 0.1012 Median :-0.3779 Median :-0.01371
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.3156 3rd Qu.: 0.4469 3rd Qu.: 0.5036 3rd Qu.: 0.38339
## Max. : 5.3480 Max. : 3.2271 Max. : 3.0758 Max. : 3.12773
## alq.pm.V1519 alq.num.V1519 tot.comp tot.eur
## Min. :-2.3040 Min. :-2.1005 Min. :-1.2347 Min. :-1.7110
## 1st Qu.:-0.3459 1st Qu.:-0.6408 1st Qu.:-0.7277 1st Qu.:-0.6537
## Median : 0.1803 Median :-0.1406 Median :-0.2342 Median :-0.2458
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.6073 3rd Qu.: 0.4532 3rd Qu.: 0.4873 3rd Qu.: 0.3101
## Max. : 1.8184 Max. : 3.1797 Max. : 2.9157 Max. : 4.4471
## perc.nou.comp perc.usat.comp tot.comp.V1419 nou.eur.V1419
## Min. :-0.7483 Min. :-3.9519 Min. :-1.2150 Min. :-1.2604
## 1st Qu.:-0.7483 1st Qu.:-0.5019 1st Qu.:-0.6221 1st Qu.:-0.2396
## Median :-0.4908 Median : 0.4922 Median :-0.2384 Median :-0.2283
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.4157 3rd Qu.: 0.7544 3rd Qu.: 0.1888 3rd Qu.: 0.5367
## Max. : 4.0522 Max. : 0.7544 Max. : 4.8984 Max. : 4.6508
## usat.eur.V1419
## Min. :-2.4209
## 1st Qu.:-0.5026
## Median :-0.1958
## Mean : 0.0000
## 3rd Qu.: 0.5048
## Max. : 4.1988CLUSTER
set.seed(123)
finalK <- kmeans(merged.sc, centers = 5, nstart = 100)
ann <- finalK$centers
ann <- as.data.frame(ann)
merged$cluster <- finalK$clusterANOVA MEAN+SD CLUSTER
for (i in 1:26)
{
min <- aggregate(merged[,i] ~ cluster, data = merged, FUN = mean)
sdd <- aggregate(merged[,i] ~ cluster, data = merged, FUN = sd)
sdmin <- merge(min,sdd,by="cluster",all.y=TRUE)
colnames(sdmin)<- c(paste0(list[i]),"mean","sd")
print(sdmin)
}## n.tot mean sd
## 1 1 43336.44 8588.847
## 2 2 16633.84 9499.795
## 3 3 26455.00 14922.839
## 4 4 16451.00 7940.149
## 5 5 15505.42 10515.134
## pc.esp mean sd
## 1 1 80.02038 5.934031
## 2 2 82.36516 5.765888
## 3 3 51.94028 7.582845
## 4 4 84.73486 3.334055
## 5 5 83.85759 5.318977
## pc.ue27-esp mean sd
## 1 1 9.143068 3.657832
## 2 2 3.961785 2.451312
## 3 3 32.556091 9.146157
## 4 4 8.083250 3.150472
## 5 5 4.196735 1.676709
## pc.20.34 mean sd
## 1 1 20.19599 2.385660
## 2 2 17.97830 2.030386
## 3 3 31.61463 3.358223
## 4 4 16.03821 1.598279
## 5 5 17.63229 1.484560
## 2019-2014 mean sd
## 1 1 1189.4375 1077.6702
## 2 2 550.6875 828.5086
## 3 3 -1803.5000 4748.5513
## 4 4 1423.2500 1253.9788
## 5 5 586.6667 875.3134
## n.esp.V1419 mean sd
## 1 1 -2.714491 2.076334
## 2 2 -1.292675 2.284297
## 3 3 -8.378314 2.243839
## 4 4 -0.731409 1.173330
## 5 5 2.573161 10.696455
## hotel2019 mean sd
## 1 1 849.56250 731.49190
## 2 2 69.96875 76.93880
## 3 3 751.50000 416.03886
## 4 4 114.25000 81.48926
## 5 5 31.66667 30.82600
## rest1614 mean sd
## 1 1 -0.0625000 37.914762
## 2 2 4.6562500 16.757491
## 3 3 -50.0000000 21.694853
## 4 4 15.1250000 26.739150
## 5 5 0.6666667 2.269695
## RFD.2017 mean sd
## 1 1 115.19375 32.34364
## 2 2 68.52187 18.97167
## 3 3 89.07500 16.38849
## 4 4 176.92500 42.94125
## 5 5 82.54167 17.11472
## tot_ann mean sd
## 1 1 716.000 520.98228
## 2 2 75.125 78.90286
## 3 3 1235.750 555.65659
## 4 4 93.000 77.69905
## 5 5 72.250 62.09395
## pmedio mean sd
## 1 1 146.26637 63.268923
## 2 2 59.04169 42.742610
## 3 3 86.93437 8.952207
## 4 4 165.13775 89.952548
## 5 5 53.28492 26.275791
## pmedio.V1519 mean sd
## 1 1 86.47770461 70.97761
## 2 2 11.22853192 44.83083
## 3 3 4.78081483 35.44938
## 4 4 75.58389830 105.78086
## 5 5 -0.01884583 62.98126
## pm_ent.V1519 mean sd
## 1 1 98.877303 73.86415
## 2 2 32.216796 72.72542
## 3 3 2.304752 38.18935
## 4 4 139.346023 192.24336
## 5 5 11.323927 78.53286
## pm_priv.V1519 mean sd
## 1 1 32.83508 23.63401
## 2 2 16.72192 46.16958
## 3 3 43.86672 10.54872
## 4 4 26.60151 41.32153
## 5 5 7.85612 41.52803
## alq.num mean sd
## 1 1 405.18750 130.54614
## 2 2 83.06250 59.68138
## 3 3 270.25000 153.13910
## 4 4 127.87500 88.45247
## 5 5 80.66667 51.50169
## alq.pm mean sd
## 1 1 1041.6091 165.8154
## 2 2 677.8560 280.4989
## 3 3 960.8175 107.5058
## 4 4 1433.6325 263.2189
## 5 5 747.1656 250.0849
## alq.pm.V1519 mean sd
## 1 1 40.45353 5.318256
## 2 2 31.05479 16.404754
## 3 3 45.96124 2.972726
## 4 4 36.65681 20.269038
## 5 5 34.00642 17.339406
## alq.num.V1519 mean sd
## 1 1 54.38179 20.52141
## 2 2 34.46026 50.30180
## 3 3 25.18087 48.46123
## 4 4 109.19727 50.64085
## 5 5 51.10443 31.45348
## tot.comp mean sd
## 1 1 317.8125 146.50516
## 2 2 147.1250 113.57980
## 3 3 286.0000 113.54588
## 4 4 91.7500 64.93458
## 5 5 153.3333 168.89444
## tot.eur mean sd
## 1 1 390.1938 112.56555
## 2 2 217.1219 98.75582
## 3 3 292.4000 96.19948
## 4 4 658.8125 235.38323
## 5 5 240.0250 68.10376
## perc.nou.comp mean sd
## 1 1 7.856486 6.206252
## 2 2 1.694017 2.691958
## 3 3 9.225648 8.981480
## 4 4 3.837367 6.124992
## 5 5 26.139675 9.686588
## perc.usat.comp mean sd
## 1 1 91.87414 6.275282
## 2 2 98.24355 2.742043
## 3 3 90.69345 8.942009
## 4 4 96.16263 6.124992
## 5 5 73.09209 9.555946
## tot.comp.V1419 mean sd
## 1 1 19.861046 31.31801
## 2 2 83.426558 74.60757
## 3 3 1.751993 12.83624
## 4 4 -10.043729 26.54555
## 5 5 93.038282 134.26008
## nou.eur.V1419 mean sd
## 1 1 60.03164 130.37796
## 2 2 -12.22792 64.86229
## 3 3 130.93821 126.18673
## 4 4 -50.54732 52.88656
## 5 5 75.32115 51.74995
## usat.eur.V1419 mean sd
## 1 1 58.61978 42.92035
## 2 2 49.49089 36.06916
## 3 3 49.15994 14.05369
## 4 4 29.27199 32.91476
## 5 5 50.50504 33.15581
## NA mean sd
## 1 1 1 0
## 2 2 2 0
## 3 3 3 0
## 4 4 4 0
## 5 5 5 0ANOVA
merged$cluster <- as.factor(merged$cluster)
for (i in 1:25)
{
maov <- aov(merged[,i] ~ cluster, data = merged)
vvv <- summary(maov)
print(paste0(list[i]))
print(vvv)
}## [1] "n.tot"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 8.978e+09 2.245e+09 24.14 2.15e-12 ***
## Residuals 67 6.230e+09 9.298e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.esp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3654 913.6 28.87 5.88e-14 ***
## Residuals 67 2120 31.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.ue27-esp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3082.2 770.5 69.92 <2e-16 ***
## Residuals 67 738.3 11.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.20.34"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 785.3 196.32 45.49 <2e-16 ***
## Residuals 67 289.1 4.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "2019-2014"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 33865854 8466463 4.51 0.00275 **
## Residuals 67 125781184 1877331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "n.esp.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 415 103.76 4.605 0.00241 **
## Residuals 67 1510 22.53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "hotel2019"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 8451099 2112775 16.11 2.73e-09 ***
## Residuals 67 8785914 131133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "rest1614"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 12447 3111.7 5.674 0.00054 ***
## Residuals 67 36742 548.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "RFD.2017"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 84632 21158 32.38 5.11e-15 ***
## Residuals 67 43785 654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot_ann"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 8835305 2208826 28.05 1.07e-13 ***
## Residuals 67 5275269 78735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 142656 35664 13.19 5.59e-08 ***
## Residuals 67 181154 2704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 91700 22925 5.827 0.000438 ***
## Residuals 67 263601 3934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_ent.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 138221 34555 4.014 0.0056 **
## Residuals 67 576717 8608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_priv.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 7229 1807 1.145 0.343
## Residuals 67 105715 1578
## [1] "alq.num"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1277022 319256 41.11 <2e-16 ***
## Residuals 67 520351 7766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 4408115 1102029 18.19 3.71e-10 ***
## Residuals 67 4059119 60584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1488 371.9 1.664 0.169
## Residuals 67 14976 223.5
## [1] "alq.num.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 38526 9632 5.349 0.000846 ***
## Residuals 67 120635 1801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 450026 112507 6.829 0.000113 ***
## Residuals 67 1103840 16475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.eur"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1411789 352947 24.66 1.42e-12 ***
## Residuals 67 959018 14314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "perc.nou.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 5363 1340.7 38.4 <2e-16 ***
## Residuals 67 2339 34.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "perc.usat.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 5683 1420.8 40.84 <2e-16 ***
## Residuals 67 2331 34.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.comp.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 108095 27024 4.631 0.00232 **
## Residuals 67 390978 5835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "nou.eur.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 184324 46081 6.403 2e-04 ***
## Residuals 67 482204 7197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "usat.eur.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 4616 1154 0.876 0.483
## Residuals 67 88231 1317