merged <- merged %>%
remove_rownames %>%
column_to_rownames(var="Nom_Barri") %>%
select("n.tot","pc.esp","pc.ue27-esp","pc.20.34","2019-2014","n.esp.M1419",
"hotel2019","rest1614",
"RFD.2017",
"tot_ann","pmedio","pmedio.M1519","pm_ent.M1519","pm_priv.M1519",
"alq.num","alq.pm","alq.pm.M1519","alq.num.M1519",
"tot.comp","tot.eur","perc.nou.comp","perc.usat.comp","tot.comp.M1419",
"nou.eur.M1419","usat.eur.M1419",
)
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.:10864 1st Qu.:77.91 1st Qu.: 2.984 1st Qu.:17.17
## Median :21303 Median :83.48 Median : 4.630 Median :18.02
## Mean :22608 Mean :80.78 Mean : 7.114 Mean :18.99
## 3rd Qu.:31087 3rd Qu.:85.63 3rd Qu.: 8.498 3rd Qu.:20.43
## Max. :58642 Max. :94.09 Max. :43.067 Max. :36.59
## 2019-2014 n.esp.M1419 hotel2019 rest1614
## Min. :-8766.0 Min. :-2311.0 Min. : 0.0 Min. :-75.000
## 1st Qu.: 199.0 1st Qu.: -641.0 1st Qu.: 20.0 1st Qu.: -5.000
## Median : 494.0 Median : -231.0 Median : 72.0 Median : 1.000
## Mean : 658.3 Mean : -386.9 Mean : 276.1 Mean : 1.192
## 3rd Qu.: 931.0 3rd Qu.: 0.0 3rd Qu.: 272.0 3rd Qu.: 6.000
## Max. : 3989.0 Max. : 462.0 Max. :3115.0 Max. : 79.000
## RFD.2017 tot_ann pmedio pmedio.M1519
## Min. : 38.60 Min. : 0.0 Min. : 0.00 Min. :-73.15
## 1st Qu.: 65.10 1st Qu.: 29.0 1st Qu.: 45.41 1st Qu.: -7.15
## Median : 82.90 Median : 97.0 Median : 71.14 Median : 11.19
## Mean : 93.67 Mean : 279.8 Mean : 98.55 Mean : 30.76
## 3rd Qu.:105.70 3rd Qu.: 261.0 3rd Qu.:108.19 3rd Qu.: 30.17
## Max. :248.80 Max. :2099.0 Max. :656.43 Max. :459.18
## pm_ent.M1519 pm_priv.M1519 alq.num alq.pm
## Min. :-107.1184 Min. :-59.0000 Min. : 2.0 Min. : 0.0
## 1st Qu.: -0.0451 1st Qu.: 0.8694 1st Qu.: 52.0 1st Qu.: 749.2
## Median : 25.0000 Median : 8.5892 Median :109.0 Median : 864.6
## Mean : 69.2127 Mean : 7.0728 Mean :167.4 Mean : 858.0
## 3rd Qu.: 75.8696 3rd Qu.: 14.8999 3rd Qu.:247.0 3rd Qu.: 996.4
## Max. :1230.0000 Max. : 44.2917 Max. :659.0 Max. :1950.1
## alq.pm.M1519 alq.num.M1519 tot.comp tot.eur
## Min. :-303.8 Min. :-72.00 Min. : 5.0 Min. : 0.0
## 1st Qu.: 183.5 1st Qu.: 11.00 1st Qu.: 74.0 1st Qu.: 192.6
## Median : 237.3 Median : 26.00 Median :148.0 Median : 264.9
## Mean : 235.7 Mean : 53.27 Mean :185.2 Mean : 309.6
## 3rd Qu.: 291.6 3rd Qu.: 71.00 3rd Qu.:257.0 3rd Qu.: 367.7
## Max. : 714.4 Max. :244.00 Max. :619.0 Max. :1125.3
## perc.nou.comp perc.usat.comp tot.comp.M1419 nou.eur.M1419
## Min. : 0.000 Min. : 50.00 Min. :-62.00 Min. :-495.3
## 1st Qu.: 0.000 1st Qu.: 86.67 1st Qu.: 0.00 1st Qu.: 0.0
## Median : 2.672 Median : 97.12 Median : 37.00 Median : 0.0
## Mean : 7.688 Mean : 91.92 Mean : 40.15 Mean : 110.2
## 3rd Qu.:11.720 3rd Qu.:100.00 3rd Qu.: 60.00 3rd Qu.: 200.0
## Max. :50.000 Max. :100.00 Max. :291.00 Max. :2600.7
## usat.eur.M1419
## Min. :-141.30
## 1st Qu.: 54.90
## Median : 86.60
## Mean : 91.93
## 3rd Qu.: 119.20
## Max. : 517.50for (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] 2 3
## [1] 2 68 69
## integer(0)
## [1] 1 2 6 7 8 31
## [1] 1 2 4 7 11 15 17 18 31 59 67 69
## [1] 21
## [1] 1 2 4 6 7 8 10 11 31
## [1] 12 25
## [1] 10 12 25 27
## [1] 12 25 36
## [1] 53 56
## integer(0)
## [1] 12 21 42 47 54 56 58
## [1] 21 49 58
## integer(0)
## integer(0)
## [1] 21
## [1] 42
## integer(0)
## [1] 37 60
## [1] 5 21
## [1] 58 69VALORES MINMAX outl_min<-(Q[1])-1.5IRQ outl_max<-(Q[3])-1.5IRQ
minmax## minmax V2 V3
## 1 n.tot -49805.000000 91756.00000
## 2 pc.esp 54.755821 108.77837
## 3 pc.ue27-esp -13.557280 25.03878
## 4 pc.20.34 7.395624 30.20393
## 5 2019-2014 -1997.000000 3127.00000
## 6 n.esp.M1419 -2564.000000 1923.00000
## 7 hotel2019 -736.000000 1028.00000
## 8 rest1614 -38.000000 39.00000
## 9 RFD.2017 -56.700000 227.50000
## 10 tot_ann -667.000000 957.00000
## 11 pmedio -142.928507 296.53609
## 12 pmedio.M1519 -119.099765 142.11548
## 13 pm_ent.M1519 -227.789168 303.61363
## 14 pm_priv.M1519 -41.222191 56.99152
## 15 alq.num -533.000000 832.00000
## 16 alq.pm 7.556394 1737.98390
## 17 alq.pm.M1519 -140.674232 615.72862
## 18 alq.num.M1519 -169.000000 251.00000
## 19 tot.comp -475.000000 806.00000
## 20 tot.eur -332.700000 893.00000
## 21 perc.nou.comp -35.160681 46.88091
## 22 perc.usat.comp 46.666667 140.00000
## 23 tot.comp.M1419 -180.000000 240.00000
## 24 nou.eur.M1419 -600.000000 800.00000
## 25 usat.eur.M1419 -138.000000 312.10000OUTLIERS 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 Barri Gòtic 36.5902
## la Barceloneta 30.4620
## 2019-2014
## el Barri Gòtic -8766
## el Poblenou 3989
## Diagonal Mar i el Front Marítim del Poblenou 3850
## 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
## la Marina del Prat Vermell 656.4286
## Sant Gervasi - la Bonanova 344.7963
## pmedio.M1519
## Sant Antoni 173.7692
## la Marina del Prat Vermell 459.1786
## Sant Gervasi - la Bonanova 260.7054
## el Putxet i el Farró 163.8440
## pm_ent.M1519
## la Marina del Prat Vermell 1230.0000
## Sant Gervasi - la Bonanova 635.8342
## la Font d'en Fargues 491.0000
## pm_priv.M1519
## la Trinitat Nova -48.77778
## Vallbona -59.00000
## alq.pm
## la Marina del Prat Vermell 0.000
## Pedralbes 1950.055
## la Clota 0.000
## Can Peguera 0.000
## Torre Baró 0.000
## Vallbona 0.000
## Baró de Viver 0.000
## alq.pm.M1519
## Pedralbes 699.2251
## Canyelles 714.4444
## Baró de Viver -303.7962
## tot.eur
## Pedralbes 1125.3
## perc.nou.comp
## la Clota 50
## tot.comp.M1419
## el Carmel 242
## Sant Andreu 291
## nou.eur.M1419
## el Fort Pienc 2040.3
## Pedralbes 2600.7
## usat.eur.M1419
## Baró de Viver 517.5
## Diagonal Mar i el Front Marítim del Poblenou -141.3SCALED
merged.sc <- scale(merged)
summary(merged.sc)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. :-1.48582 Min. :-4.1126 Min. :-0.8517 Min. :-1.4973
## 1st Qu.:-0.79597 1st Qu.:-0.3188 1st Qu.:-0.5643 1st Qu.:-0.4693
## Median :-0.08843 Median : 0.2991 Median :-0.3394 Median :-0.2505
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.57472 3rd Qu.: 0.5377 3rd Qu.: 0.1890 3rd Qu.: 0.3720
## Max. : 2.44236 Max. : 1.4769 Max. : 4.9115 Max. : 4.5446
## 2019-2014 n.esp.M1419 hotel2019 rest1614
## Min. :-6.3247 Min. :-3.4001 Min. :-0.563112 Min. :-2.912694
## 1st Qu.:-0.3083 1st Qu.:-0.4490 1st Qu.:-0.522315 1st Qu.:-0.236702
## Median :-0.1103 Median : 0.2755 Median :-0.416242 Median :-0.007332
## Mean : 0.0000 Mean : 0.0000 Mean : 0.000000 Mean : 0.000000
## 3rd Qu.: 0.1830 3rd Qu.: 0.6837 3rd Qu.:-0.008271 3rd Qu.: 0.183811
## Max. : 2.2352 Max. : 1.5001 Max. : 5.791038 Max. : 2.974488
## RFD.2017 tot_ann pmedio pmedio.M1519
## Min. :-1.2894 Min. :-0.63052 Min. :-1.0458 Min. :-1.395585
## 1st Qu.:-0.6690 1st Qu.:-0.56517 1st Qu.:-0.5639 1st Qu.:-0.509145
## Median :-0.2522 Median :-0.41196 Median :-0.2909 Median :-0.262878
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.000000
## 3rd Qu.: 0.2816 3rd Qu.:-0.04244 3rd Qu.: 0.1023 3rd Qu.:-0.007947
## Max. : 3.6322 Max. : 4.09889 Max. : 5.9201 Max. : 5.754129
## pm_ent.M1519 pm_priv.M1519 alq.num alq.pm
## Min. :-1.00502 Min. :-4.03415 Min. :-1.0390 Min. :-2.39854
## 1st Qu.:-0.39474 1st Qu.:-0.37876 1st Qu.:-0.7248 1st Qu.:-0.30429
## Median :-0.25200 Median : 0.09258 Median :-0.3667 Median : 0.01837
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.03794 3rd Qu.: 0.47789 3rd Qu.: 0.5003 3rd Qu.: 0.38676
## Max. : 6.61604 Max. : 2.27244 Max. : 3.0887 Max. : 3.05272
## alq.pm.M1519 alq.num.M1519 tot.comp tot.eur
## Min. :-3.75275 Min. :-1.9267 Min. :-1.2142 Min. :-1.6886
## 1st Qu.:-0.36319 1st Qu.:-0.6502 1st Qu.:-0.7493 1st Qu.:-0.6381
## Median : 0.01123 Median :-0.4195 Median :-0.2506 Median :-0.2438
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.38844 3rd Qu.: 0.2726 3rd Qu.: 0.4839 3rd Qu.: 0.3169
## Max. : 3.33000 Max. : 2.9334 Max. : 2.9232 Max. : 4.4492
## perc.nou.comp perc.usat.comp tot.comp.M1419 nou.eur.M1419
## Min. :-0.7404 Min. :-3.9689 Min. :-1.67669 Min. :-1.3970
## 1st Qu.:-0.7404 1st Qu.:-0.4977 1st Qu.:-0.65903 1st Qu.:-0.2542
## Median :-0.4831 Median : 0.4921 Median :-0.05171 Median :-0.2542
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.3884 3rd Qu.: 0.7645 3rd Qu.: 0.32580 3rd Qu.: 0.2073
## Max. : 4.0752 Max. : 0.7645 Max. : 4.11741 Max. : 5.7465
## usat.eur.M1419
## Min. :-2.90132
## 1st Qu.:-0.46062
## Median :-0.06627
## Mean : 0.00000
## 3rd Qu.: 0.33927
## Max. : 5.29406CLUSTER
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 43620.75 7418.406
## 2 2 23885.40 11719.570
## 3 3 16979.04 11424.519
## 4 4 1199.00 NA
## 5 5 26455.00 14922.839
## pc.esp mean sd
## 1 1 78.06790 5.054885
## 2 2 84.57396 3.363099
## 3 3 82.99189 5.649230
## 4 4 89.07423 NA
## 5 5 51.94028 7.582845
## pc.ue27-esp mean sd
## 1 1 9.918585 3.407852
## 2 2 7.843218 2.820178
## 3 3 4.144301 2.477200
## 4 4 1.029963 NA
## 5 5 32.556091 9.146157
## pc.20.34 mean sd
## 1 1 21.21621 1.630111
## 2 2 16.66104 1.599499
## 3 3 17.76369 1.932159
## 4 4 21.35113 NA
## 5 5 31.61463 3.358223
## 2019-2014 mean sd
## 1 1 824.4167 690.7641
## 2 2 1704.5000 1356.1573
## 3 3 611.7609 876.6697
## 4 4 194.0000 NA
## 5 5 -1803.5000 4748.5513
## n.esp.M1419 mean sd
## 1 1 -1256.3333 554.1700
## 2 2 -108.0000 197.6557
## 3 3 -164.8261 314.7993
## 4 4 33.0000 NA
## 5 5 -1134.5000 335.9926
## hotel2019 mean sd
## 1 1 987.08333 782.43018
## 2 2 206.50000 252.73185
## 3 3 69.91304 98.38447
## 4 4 20.00000 NA
## 5 5 751.50000 416.03886
## rest1614 mean sd
## 1 1 2.666667 42.84079
## 2 2 9.100000 28.34490
## 3 3 3.347826 14.15033
## 4 4 10.000000 NA
## 5 5 -50.000000 21.69485
## RFD.2017 mean sd
## 1 1 110.73333 25.28734
## 2 2 170.79000 45.53610
## 3 3 74.02174 21.67601
## 4 4 40.00000 NA
## 5 5 89.07500 16.38849
## tot_ann mean sd
## 1 1 858.58333 522.26577
## 2 2 179.00000 153.51656
## 3 3 73.43478 72.57108
## 4 4 14.00000 NA
## 5 5 1235.75000 555.65659
## pmedio mean sd
## 1 1 143.17792 64.011706
## 2 2 174.12394 82.835873
## 3 3 59.36397 39.121909
## 4 4 656.42857 NA
## 5 5 86.93437 8.952207
## pmedio.M1519 mean sd
## 1 1 65.926332 56.01148
## 2 2 79.522892 82.10554
## 3 3 4.885877 29.59947
## 4 4 459.178571 NA
## 5 5 -6.238929 45.05626
## pm_ent.M1519 mean sd
## 1 1 86.22669 56.77086
## 2 2 158.86152 184.06332
## 3 3 27.12628 84.88256
## 4 4 1230.00000 NA
## 5 5 -12.15478 64.65317
## pm_priv.M1519 mean sd
## 1 1 16.008680 9.304427
## 2 2 6.534388 11.583430
## 3 3 3.547698 18.141372
## 4 4 22.250000 NA
## 5 5 18.355969 4.897977
## alq.num mean sd
## 1 1 432.50000 131.69075
## 2 2 204.80000 129.83990
## 3 3 84.65217 61.51322
## 4 4 5.00000 NA
## 5 5 270.25000 153.13910
## alq.pm mean sd
## 1 1 1029.4990 140.1934
## 2 2 1362.0327 284.3531
## 3 3 713.4320 282.9134
## 4 4 0.0000 NA
## 5 5 960.8175 107.5058
## alq.pm.M1519 mean sd
## 1 1 296.3276 37.86042
## 2 2 389.6908 141.99675
## 3 3 185.8124 134.97672
## 4 4 0.0000 NA
## 5 5 301.6976 27.76881
## alq.num.M1519 mean sd
## 1 1 154.91667 61.44393
## 2 2 87.20000 54.74547
## 3 3 19.65217 20.69806
## 4 4 3.00000 NA
## 5 5 62.75000 93.50356
## tot.comp mean sd
## 1 1 307.0000 139.74978
## 2 2 148.9000 96.30559
## 3 3 156.3913 145.08993
## 4 4 8.0000 NA
## 5 5 286.0000 113.54588
## tot.eur mean sd
## 1 1 374.7167 90.20024
## 2 2 630.5700 226.28612
## 3 3 229.1196 95.81728
## 4 4 89.0000 NA
## 5 5 292.4000 96.19948
## perc.nou.comp mean sd
## 1 1 5.280367 4.235937
## 2 2 8.237595 8.310219
## 3 3 8.229283 12.061428
## 4 4 0.000000 NA
## 5 5 9.225648 8.981480
## perc.usat.comp mean sd
## 1 1 94.36047 4.593647
## 2 2 91.76240 8.310219
## 3 3 91.52688 12.325113
## 4 4 87.50000 NA
## 5 5 90.69345 8.942009
## tot.comp.M1419 mean sd
## 1 1 45.91667 61.18446
## 2 2 -13.40000 33.37398
## 3 3 53.89130 61.17016
## 4 4 -1.00000 NA
## 5 5 9.00000 37.97368
## nou.eur.M1419 mean sd
## 1 1 237.54167 599.1013
## 2 2 249.02000 909.5623
## 3 3 41.28043 176.2401
## 4 4 0.00000 NA
## 5 5 200.75000 165.0946
## usat.eur.M1419 mean sd
## 1 1 121.72500 38.30365
## 2 2 137.85000 114.96392
## 3 3 75.66087 79.04202
## 4 4 33.10000 NA
## 5 5 89.50000 23.33652
## NA mean sd
## 1 1 1 0
## 2 2 2 0
## 3 3 3 0
## 4 4 4 NA
## 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 7.290e+09 1.822e+09 14.78 9.69e-09 ***
## Residuals 68 8.383e+09 1.233e+08
## ---
## 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 3853 963.2 32.89 2.97e-15 ***
## Residuals 68 1991 29.3
## ---
## 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 3131.6 782.9 73.29 <2e-16 ***
## Residuals 68 726.4 10.7
## ---
## 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 4 826.0 206.50 55.26 <2e-16 ***
## Residuals 68 254.1 3.74
## ---
## 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 35833524 8958381 4.911 0.00154 **
## Residuals 68 124032123 1824002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "n.esp.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 14529337 3632334 28.96 4.61e-14 ***
## Residuals 68 8527868 125410
## ---
## 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 9039618 2259904 18.6 2.3e-10 ***
## Residuals 68 8263870 121528
## ---
## 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 11425 2856.3 5.133 0.00113 **
## Residuals 68 37842 556.5
## ---
## 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 83692 20923 29.86 2.41e-14 ***
## Residuals 68 47645 701
## ---
## 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 9806483 2451621 38.1 <2e-16 ***
## Residuals 68 4375741 64349
## ---
## 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 463417 115854 44.78 <2e-16 ***
## Residuals 68 175943 2587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 258432 64608 31.23 9.24e-15 ***
## Residuals 68 140698 2069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_ent.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1539231 384808 38.64 <2e-16 ***
## Residuals 68 677133 9958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_priv.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 2272 568.1 2.267 0.0709 .
## Residuals 68 17042 250.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.num"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1240981 310245 36.18 3.49e-16 ***
## Residuals 68 583122 8575
## ---
## 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 4633279 1158320 17.2 8.66e-10 ***
## Residuals 68 4580378 67359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 468701 117175 7.816 3.04e-05 ***
## Residuals 68 1019391 14991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.num.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 190371 47593 28.39 7.04e-14 ***
## Residuals 68 114010 1677
## ---
## 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 301419 75355 3.99 0.00575 **
## Residuals 68 1284280 18886
## ---
## 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 1428894 357223 24.51 1.39e-12 ***
## Residuals 68 991251 14577
## ---
## 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 4 155 38.66 0.346 0.846
## Residuals 68 7607 111.87
## [1] "perc.usat.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 104 26.1 0.224 0.924
## Residuals 68 7929 116.6
## [1] "tot.comp.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 43336 10834 3.29 0.0159 *
## Residuals 68 223910 3293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "nou.eur.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 650738 162684 0.859 0.493
## Residuals 68 12873373 189314
## [1] "usat.eur.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 47399 11850 1.928 0.116
## Residuals 68 417867 6145