Machine Learning

1 MAPA DO CLUSTER

library(ggplot2)  
library(sf)  
library(geobr)   
library(ggspatial)  
library(tidyverse)

layout(matrix(c(1,2,3,3), 2, 2, byrow = TRUE))

1.1 RS

RS <- read_state(code_state = "RS", year = 2018, showProgress = F)

ggplot(RS) +
  aes(group = code_state) +
  geom_sf(size = 1L) +
  labs(x = "Longitude", y = "Latitude", title = "Rio Grande do Sul") +
  theme_bw()

1.2 BRASIL

BR <- read_state(code_state = "all", year = 2018, showProgress = F)

BRFINAL <- ggplot(BR) +
  aes(group = code_region) +
  geom_sf(size = 0.5, fill = "white") + 
  geom_sf(aes(group = code_state), data = RS, fill = "gray50") +
  labs(x = "Longitude", y = "Latitude", title = "Brazil") +
  annotation_north_arrow(style = north_arrow_fancy_orienteering()) + annotation_scale(location = "br") +
  theme_void()

BRFINAL2 <- ggplot(BR) +
  aes(group = code_region) +
  geom_sf(size = 0.5, fill = "white") + 
  geom_sf(aes(group = code_state), data = RS, fill = "#8575ff") +
  labs(x = "Longitude", y = "Latitude", title = "") + theme_void()
BRFINAL2

1.3 MUNICÍPIOS DO RS

munRS <- read_municipality(code_muni="RS", year=2018, showProgress = F)
munRS <- munRS[c(-2),]
munRS$Cluster <- c("Não")
munRS$Cluster <- ifelse(munRS$code_muni == 4300802 | munRS$code_muni == 4302105 | munRS$code_muni == 4303673 | munRS$code_muni == 4305108 |
                          munRS$code_muni == 4305959 |  munRS$code_muni == 4307906 | munRS$code_muni == 4308201 | munRS$code_muni == 4308607 |
                          munRS$code_muni == 4309407 |  munRS$code_muni == 4310439 | munRS$code_muni == 4313086 | munRS$code_muni == 4313359 |
                          munRS$code_muni == 4319000 |  munRS$code_muni == 4322806 | munRS$code_muni == 4323309 |  munRS$code_muni == 4312385 |
                          munRS$code_muni ==    4314548 |  munRS$code_muni == 4304804 | munRS$code_muni == 4317251 ,"Sim", "Não")

library(RColorBrewer)
munRS <- munRS %>%
  mutate(Cluster = ifelse(Cluster == "Sim", "Yes", "No"))

RSmun <- ggplot(munRS) +
  aes(fill = Cluster, group = code_state) +
  geom_sf(size = 0.5) +
  labs(x = "Longitude", y = "Latitude", title = "Rio Grande do Sul") +
  annotation_north_arrow(style = north_arrow_fancy_orienteering()) + annotation_scale(location = "br") +
  theme_void() + scale_fill_manual(values = c("white", "gray50")) + labs(color = "Cluster")

RSmun2 <- ggplot(munRS) +
  aes(fill = Cluster, group = code_state) +
  geom_sf(size = 0.5, show.legend = F) +
  labs(x = "", y = "", title = "") +
  scale_fill_manual(values = c("white", "#8575ff")) + theme_void()
RSmun2

1.4 CLUSTER

ClusterMun <- munRS
ClusterMun <- ClusterMun[c(18,44,70,92,97,121,164,169,176,190,209,259,280,285,319, 372, 403,483,489),]

Frequencia <- c(1,11,0,0,2,1,4,12,8,1,0,0,1,0,3,0,1,0,0)
ClusterMun <- cbind(ClusterMun,Frequencia)

ClusterMun$Categoria <- cut(ClusterMun$Frequencia, breaks = c(-1, 0, 2, 4, 8, 12),
                            labels = c("0", "1-2", "3-4", "5-8", "9-12"))

cluster <- ggplot(ClusterMun) +
  aes(group = code_state) +
  geom_sf(size = 0.5) +
  labs(title = "", x = "", y = "") +
  geom_sf_label(aes(label = name_muni), label.padding = unit(0.05, "lines"),
                label.r = unit(0.05, "lines"), inherit.aes = F, label.size = 0.1) +
  theme_bw()

clusterFinal <- ClusterMun %>% ggplot(aes(fill = Categoria)) + geom_sf(size = 0.5) +
  scale_fill_manual(values = c("#dfdbff","#c9c2ff", "#b2a8ff", "#9b8fff", "#8575ff")) +
  labs(title = "", x = "", y = "", fill = "Frequency") +
  geom_sf_text(aes(label = name_muni), check_overlap = F, size = 4) +  theme_bw()


clusterFinal

BRFINAL2

RSmun2

library(cowplot)

ggdraw (clusterFinal) +
  draw_plot(BRFINAL2, width = 0.18, height = 0.35, 
            x = 0.10, y = 0.68) +
  draw_plot(RSmun2, width = 0.18, height = 0.22, 
            x = 0.10, y = 0.15)

# As diferenças no mapa mostrado aqui e o mapa do artigo se dão pela dimenscionalidade que eu ajustei quando baixei E plotei o mapa no arquivo word. 

2 ANÁLISE FATORIAL EXPLORATÓRIA

library(foreign)
dados<- read.spss("C:/Users/user/Desktop/R/SNA/Netwine/SNA/Dados/Survey.sav")
attach(dados)
library(tibble)
dados <- as_tibble(dados)

dados[dados == 9999] <- NA

library(tidyverse)
library(psych)
library(GPArotation)
library(FactoMineR)
library(factoextra)
library(ggcorrplot)
library(RCurl)
library(knitr)
library(EGAnet)
library(EFAshiny)
library(moments)
library(gridExtra)
library(qgraph)
library(bootnet)
library(igraph)
library(EGAnet)

library(rmarkdown)
paged_table(dados)

dadosAFE <- dados[,c(17,18,19,20,21)] # Escala de inovação
dadosAFE <- dadosAFE[-c(38,41,43,44,45,48,49,50,51,52),] # removendo as vinícolas que não responderam

paged_table(dadosAFE)

2.1 DESCRITIVAS AFE

my_summary <- function(x) {
  funs <- c(mean, sd, skewness, kurtosis, median, mad)
  sapply(funs, function(f)f(x, na.rm = TRUE))
}

NumericStatistic <- apply(dadosAFE,2,my_summary)
row.names(NumericStatistic) <- c("Mean","SD","Skewness","Kurtosis","Median","Mad")
NumericStatistic <- as.data.frame(t(NumericStatistic))
NumericStatistic <- round(NumericStatistic,3)
NumericStatistic

##                                     Mean    SD Skewness Kurtosis Median   Mad
## Novos_produtos_ou_processos        2.761 1.523    0.181    1.530    2.5 2.224
## Aquisição_conhecimentos_externos   3.413 1.087   -0.142    2.062    3.5 0.741
## Treinamentos_capacitações          3.217 0.892   -0.438    3.146    3.0 1.483
## Ações_orientadas_mudanças_gestão   3.217 1.073   -0.550    2.561    3.0 1.483
## Ações_orientadas_mudanças_produtos 3.630 0.853   -0.305    2.531    4.0 1.483

dadosAFE$id <- seq(1:46)

library(reshape2)

dta_long <- melt(dadosAFE, id.vars = c("id"))
colnames(dta_long) <- c("id", "Item", "Response")
Histogram <- ggplot(dta_long, aes(x = Response, fill = Item)) +
  geom_histogram(bins = 10, show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()
Histogram

DensityPlot <- ggplot(dta_long, aes(x = Response, fill = Item))+
  geom_density(show.legend = F)+
  facet_wrap(~Item)+
  theme_bw()
DensityPlot 

dadosAFE$id <- NULL
# Estatísticas descritivas das escalas

2.1.1 Correlações AFE

library(corrplot)
CorMat <- cor(as.matrix(dadosAFE))
corrplot(CorMat,order="hclust",type="upper",method="ellipse",
         tl.pos = "lt", tl.cex = 0.8)
corrplot(CorMat,order="hclust",type="lower",method="number",
         diag=FALSE,tl.pos="n", cl.pos="n",add=TRUE,tl.cex = 0.8)

# Medidas de correlações entre as variáveis que formam o constructo

2.2 ESTATÍSTICAS AFE

# Reter x components, função = nfactors = x
# Rotação = "varimax, "quatimax", "promax", "oblimin", "simplimax", "cluster"
# Tipos: fm = "pa" (principal axis), fm = "ml" maximum likelyhood

correlação <- cor(dadosAFE, use = "pairwise.complete.obs")
kable(correlação)

	Novos_produtos_ou_processos	Aquisição_conhecimentos_externos	Treinamentos_capacitações	Ações_orientadas_mudanças_gestão	Ações_orientadas_mudanças_produtos
Novos_produtos_ou_processos	1.0000000	0.1952634	0.1535816	0.4268234	0.4267066
Aquisição_conhecimentos_externos	0.1952634	1.0000000	0.4782480	0.3404703	0.4561423
Treinamentos_capacitações	0.1535816	0.4782480	1.0000000	0.3904894	0.3124052
Ações_orientadas_mudanças_gestão	0.4268234	0.3404703	0.3904894	1.0000000	0.5268769
Ações_orientadas_mudanças_produtos	0.4267066	0.4561423	0.3124052	0.5268769	1.0000000

symnum(correlação)

##                                    N Aq__ T Açs_rntds_mdnçs_g Açs_rntds_mdnçs_p
## Novos_produtos_ou_processos        1                                           
## Aquisição_conhecimentos_externos     1                                         
## Treinamentos_capacitações            .    1                                    
## Ações_orientadas_mudanças_gestão   . .    . 1                                  
## Ações_orientadas_mudanças_produtos . .    . .                 1                
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1

2.2.1 Teste de Bartlett

cortest.bartlett(correlação, n = nrow(dadosAFE))

## $chisq
## [1] 50.60162
## 
## $p.value
## [1] 2.06829e-07
## 
## $df
## [1] 10

2.2.2 Teste De KMO

KMO(correlação)

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = correlação)
## Overall MSA =  0.73
## MSA for each item = 
##        Novos_produtos_ou_processos   Aquisição_conhecimentos_externos 
##                               0.75                               0.71 
##          Treinamentos_capacitações   Ações_orientadas_mudanças_gestão 
##                               0.71                               0.75 
## Ações_orientadas_mudanças_produtos 
##                               0.73

KMO(dadosAFE)

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = dadosAFE)
## Overall MSA =  0.73
## MSA for each item = 
##        Novos_produtos_ou_processos   Aquisição_conhecimentos_externos 
##                               0.75                               0.71 
##          Treinamentos_capacitações   Ações_orientadas_mudanças_gestão 
##                               0.71                               0.75 
## Ações_orientadas_mudanças_produtos 
##                               0.73

2.2.3 Linhas paralelas

scree(dadosAFE) # Gráfico de entulho do SPSS (Critério Kaiser)

fa(dadosAFE, cor = "cor")

## Factor Analysis using method =  minres
## Call: fa(r = dadosAFE, cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                                     MR1   h2   u2 com
## Novos_produtos_ou_processos        0.48 0.24 0.76   1
## Aquisição_conhecimentos_externos   0.59 0.35 0.65   1
## Treinamentos_capacitações          0.53 0.28 0.72   1
## Ações_orientadas_mudanças_gestão   0.71 0.51 0.49   1
## Ações_orientadas_mudanças_produtos 0.74 0.55 0.45   1
## 
##                 MR1
## SS loadings    1.92
## Proportion Var 0.38
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  10  and the objective function was  1.19 with Chi Square of  50.6
## The degrees of freedom for the model are 5  and the objective function was  0.16 
## 
## The root mean square of the residuals (RMSR) is  0.08 
## The df corrected root mean square of the residuals is  0.12 
## 
## The harmonic number of observations is  46 with the empirical chi square  6.52  with prob <  0.26 
## The total number of observations was  46  with Likelihood Chi Square =  6.82  with prob <  0.23 
## 
## Tucker Lewis Index of factoring reliability =  0.908
## RMSEA index =  0.086  and the 90 % confidence intervals are  0 0.24
## BIC =  -12.32
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy             
##                                                    MR1
## Correlation of (regression) scores with factors   0.88
## Multiple R square of scores with factors          0.78
## Minimum correlation of possible factor scores     0.56

nofactors <- fa.parallel(dadosAFE, fm = "pa", fa = "fa", cor = "cor") # Linhas paralelas, é um critério mais rigoroso que do SPSS

## Parallel analysis suggests that the number of factors =  1  and the number of components =  NA

sum(nofactors$fa.values > 1) # Critério Kaiser 1 (Componentes)

## [1] 1

sum(nofactors$fa.values > 0.7) # Critério Kaiser 0.7 (Fatores)

## [1] 1

entulho <- scree(dadosAFE)

entulho$pcv # Autovalores pelo critério Kaiser

## [1] 2.5031930 0.9783838 0.5960619 0.5276605 0.3947008

entulho$fv # Autovalores pelo critério das linhas paralelas

## [1]  1.91967491  0.28490771  0.04469639 -0.11879784 -0.21080618

2.2.4 Regra numérica para determinar o número de fatores

NumericRule <- VSS(dadosAFE, n = 2, plot = F, rotate = "promax", fm = "pa")
temp1 <- data.frame(nFactor = row.names(NumericRule$vss.stats), 
                    VSS1 = NumericRule$cfit.1, VSS2 = NumericRule$cfit.2, 
                    MAP = NumericRule$map)
temp2 <- NumericRule$vss.stats[,c(6:8,11)]
NumericRule <- cbind(temp1,temp2)
NumericRule # 1 fator é melhor, maior VSS, menor MAP, menor BIC

##   nFactor      VSS1      VSS2        MAP     RMSEA        BIC     SABIC
## 1       1 0.7358353 0.0000000 0.08786297 0.0863316 -12.317872 3.3595988
## 2       2 0.6368326 0.7026628 0.18466763 0.1102632  -2.247152 0.8883418
##         SRMR
## 1 0.08418020
## 2 0.03045586

# Using the Very Simple Structure Criterion (VSS), VSS for a given complexity will tend to peak at the optimal (most interpretable) number of factors (Revelle and Rocklin, 1979)
# Wayne Velicer's Minimum Average Partial (MAP) criterion. 
# BIC

2.3 EFA

set.seed(123)
EFA <- fa(dadosAFE, nfactors = 1, 
          rotate = "promax", fm = "pa",
          scores = T, missing = F, cor = "cor",
          n.iter = 10)
EFA

## Factor Analysis with confidence intervals using method = fa(r = dadosAFE, nfactors = 1, n.iter = 10, rotate = "promax", 
##     scores = T, missing = F, fm = "pa", cor = "cor")
## Factor Analysis using method =  pa
## Call: fa(r = dadosAFE, nfactors = 1, n.iter = 10, rotate = "promax", 
##     scores = T, missing = F, fm = "pa", cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                                     PA1   h2   u2 com
## Novos_produtos_ou_processos        0.48 0.24 0.76   1
## Aquisição_conhecimentos_externos   0.59 0.35 0.65   1
## Treinamentos_capacitações          0.53 0.28 0.72   1
## Ações_orientadas_mudanças_gestão   0.71 0.51 0.49   1
## Ações_orientadas_mudanças_produtos 0.74 0.55 0.45   1
## 
##                 PA1
## SS loadings    1.92
## Proportion Var 0.38
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  10  and the objective function was  1.19 with Chi Square of  50.6
## The degrees of freedom for the model are 5  and the objective function was  0.16 
## 
## The root mean square of the residuals (RMSR) is  0.08 
## The df corrected root mean square of the residuals is  0.12 
## 
## The harmonic number of observations is  46 with the empirical chi square  6.52  with prob <  0.26 
## The total number of observations was  46  with Likelihood Chi Square =  6.83  with prob <  0.23 
## 
## Tucker Lewis Index of factoring reliability =  0.908
## RMSEA index =  0.086  and the 90 % confidence intervals are  0 0.24
## BIC =  -12.32
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy             
##                                                    PA1
## Correlation of (regression) scores with factors   0.88
## Multiple R square of scores with factors          0.78
## Minimum correlation of possible factor scores     0.55
## 
##  Coefficients and bootstrapped confidence intervals 
##                                     low  PA1 upper
## Novos_produtos_ou_processos        0.32 0.48  0.81
## Aquisição_conhecimentos_externos   0.35 0.59  0.90
## Treinamentos_capacitações          0.20 0.53  0.85
## Ações_orientadas_mudanças_gestão   0.45 0.71  0.95
## Ações_orientadas_mudanças_produtos 0.64 0.74  0.89

options(scienpen = 999)
# Calculo da AVE^2
SS<-colSums(EFA$Structure^2)
SS/length(EFA$communality)

##       PA1 
## 0.3837802

mean(EFA$communality)

## [1] 0.3837802

# Avaliar se o Tucker Lewis Index of factoring reliability > .90 
# Avaliar se o RMSEA < 0.08 e o SRMR < 0.10

1-((EFA$STATISTIC-EFA$dof)/(EFA$null.chisq-EFA$null.dof)) #CFI Comparative Fit Index > 0.90

## [1] 0.9550428

attributes(EFA) # Atributos da EFA

## $names
##  [1] "residual"               "dof"                    "chi"                   
##  [4] "nh"                     "rms"                    "EPVAL"                 
##  [7] "crms"                   "EBIC"                   "ESABIC"                
## [10] "fit"                    "fit.off"                "sd"                    
## [13] "factors"                "complexity"             "n.obs"                 
## [16] "objective"              "criteria"               "STATISTIC"             
## [19] "PVAL"                   "Call"                   "null.model"            
## [22] "null.dof"               "null.chisq"             "TLI"                   
## [25] "RMSEA"                  "BIC"                    "SABIC"                 
## [28] "r.scores"               "R2"                     "valid"                 
## [31] "weights"                "rotation"               "communality"           
## [34] "communalities"          "uniquenesses"           "values"                
## [37] "e.values"               "loadings"               "model"                 
## [40] "fm"                     "Structure"              "communality.iterations"
## [43] "method"                 "scores"                 "R2.scores"             
## [46] "r"                      "np.obs"                 "fn"                    
## [49] "Vaccounted"             "cis"                   
## 
## $class
## [1] "psych" "fa.ci"

EFA$communality # Mostrar as comunalidades

##        Novos_produtos_ou_processos   Aquisição_conhecimentos_externos 
##                          0.2350313                          0.3498182 
##          Treinamentos_capacitações   Ações_orientadas_mudanças_gestão 
##                          0.2795044                          0.5051139 
## Ações_orientadas_mudanças_produtos 
##                          0.5494329

EFA$uniquenesses # Mostras a unicidade

##        Novos_produtos_ou_processos   Aquisição_conhecimentos_externos 
##                          0.7649687                          0.6501818 
##          Treinamentos_capacitações   Ações_orientadas_mudanças_gestão 
##                          0.7204956                          0.4948861 
## Ações_orientadas_mudanças_produtos 
##                          0.4505671

EFA$TLI

## [1] 0.9082928

EFA$R2.scores

## [1] 0.77713

EFA$RMSEA

##      RMSEA      lower      upper confidence 
##  0.0863316  0.0000000  0.2396494  0.9000000

EFA$loadings # Mostrar as cargas fatoriais

## 
## Loadings:
##                                    PA1  
## Novos_produtos_ou_processos        0.485
## Aquisição_conhecimentos_externos   0.591
## Treinamentos_capacitações          0.529
## Ações_orientadas_mudanças_gestão   0.711
## Ações_orientadas_mudanças_produtos 0.741
## 
##                  PA1
## SS loadings    1.919
## Proportion Var 0.384

fa.diagram(EFA, simple = T,cut = 0.33,
           sort = T,errors = T,e.size = 0.05)

2.3.1 Alpha de Cronbach

Fator_S <- c(1:5)

psych::alpha(dadosAFE[, Fator_S]) # Alfa padronizado = 0.75

## 
## Reliability analysis   
## Call: psych::alpha(x = dadosAFE[, Fator_S])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.72      0.75    0.73      0.37 2.9 0.066  3.2 0.76     0.41
## 
##  lower alpha upper     95% confidence boundaries
## 0.59 0.72 0.85 
## 
##  Reliability if an item is dropped:
##                                    raw_alpha std.alpha G6(smc) average_r S/N
## Novos_produtos_ou_processos             0.74      0.74    0.70      0.42 2.9
## Aquisição_conhecimentos_externos        0.67      0.70    0.66      0.37 2.4
## Treinamentos_capacitações               0.69      0.72    0.68      0.40 2.6
## Ações_orientadas_mudanças_gestão        0.62      0.67    0.64      0.34 2.0
## Ações_orientadas_mudanças_produtos      0.63      0.66    0.63      0.33 2.0
##                                    alpha se var.r med.r
## Novos_produtos_ou_processos           0.063 0.007  0.42
## Aquisição_conhecimentos_externos      0.075 0.016  0.41
## Treinamentos_capacitações             0.074 0.013  0.43
## Ações_orientadas_mudanças_gestão      0.091 0.019  0.37
## Ações_orientadas_mudanças_produtos    0.088 0.017  0.37
## 
##  Item statistics 
##                                     n raw.r std.r r.cor r.drop mean   sd
## Novos_produtos_ou_processos        46  0.71  0.63  0.48   0.40  2.8 1.52
## Aquisição_conhecimentos_externos   46  0.67  0.70  0.60   0.47  3.4 1.09
## Treinamentos_capacitações          46  0.61  0.66  0.54   0.43  3.2 0.89
## Ações_orientadas_mudanças_gestão   46  0.76  0.76  0.69   0.59  3.2 1.07
## Ações_orientadas_mudanças_produtos 46  0.75  0.77  0.71   0.62  3.6 0.85
## 
## Non missing response frequency for each item
##                                       1    2    3    4    5 miss
## Novos_produtos_ou_processos        0.30 0.20 0.11 0.22 0.17    0
## Aquisição_conhecimentos_externos   0.02 0.22 0.26 0.33 0.17    0
## Treinamentos_capacitações          0.04 0.13 0.43 0.35 0.04    0
## Ações_orientadas_mudanças_gestão   0.09 0.15 0.28 0.41 0.07    0
## Ações_orientadas_mudanças_produtos 0.00 0.11 0.28 0.48 0.13    0

2.4 SALVANDO OS RESULTADOS

#library(openxlsx)
#write.xlsx(scores, 'scores.xlsx')
#library(readxl)
#scores <- read_excel("C:/Users/user/Desktop/R/SNA/Netwine/SNA/Dados/scores.xlsx",             col_types = c("text", "numeric"))
#dados2 <- dados
#dados2 <- cbind(dados2, scores$PA1)
#names(dados2)[names(dados2) == "scores$PA1"] <- "Fator_inovação"
#dados[dados == "NA"] <- NA
#library(haven)
#write_sav(dados2, "dados_sna.sav")
# Processo apenas para salvar os resultados em uma nova base de dados 

3 SNA

library(igraph)
library(readxl)
citation("igraph")

## 
## To cite 'igraph' in publications use:
## 
##   Csardi G, Nepusz T: The igraph software package for complex network
##   research, InterJournal, Complex Systems 1695. 2006. http://igraph.org
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {The igraph software package for complex network research},
##     author = {Gabor Csardi and Tamas Nepusz},
##     journal = {InterJournal},
##     volume = {Complex Systems},
##     pages = {1695},
##     year = {2006},
##     url = {http://igraph.org},
##   }

Nos1 <- read_excel("C:/Users/user/Desktop/R/SNA/Netwine/Recebeu inf tec/ligacoes.xlsx")
Lacos1 <- read_excel("C:/Users/user/Desktop/R/SNA/Netwine/NETWINE_Reciprocidade/Recebeu inf tec/Nos.xlsx")

3.1 Criando a rede

net1 <- graph.data.frame(d = Nos1, vertices = Lacos1, directed = T) # Criando a rede
V(net1)$Municipio 

##  [1] "Bento_Gonçalves" "Farroupilha"     "Caxias_do_Sul"   "Bento_Gonçalves"
##  [5] "Flores_da_Cunha" "Farroupilha"     "Bento_Gonçalves" "Pinto_Bandeira" 
##  [9] "Garibaldi"       "Farroupilha"     "Flores_da_Cunha" "Bento_Gonçalves"
## [13] "Flores_da_Cunha" "Flores_da_Cunha" "Bento_Gonçalves" "Flores_da_Cunha"
## [17] "Farroupilha"     "Flores_da_Cunha" "Bento_Gonçalves" "Garibaldi"      
## [21] "Bento_Gonçalves" "Bento_Gonçalves" "Caxias_do_Sul"   "Garibaldi"      
## [25] "Flores_da_Cunha" "Garibaldi"       "Guapore"         "Guapore"        
## [29] "Bento_Gonçalves" "Garibaldi"       "Flores_da_Cunha" "Bento_Gonçalves"
## [33] "Garibaldi"       "Flores_da_Cunha" "Garibaldi"       "São_Marcos"     
## [37] "Antonio_Prado"   "Bento_Gonçalves" "Garibaldi"       "Pinto_Bandeira" 
## [41] "Farroupilha"     "Bento_Gonçalves" "Farroupilha"     "Vacaria"        
## [45] "Bento_Gonçalves" "Flores_da_Cunha" "Flores_da_Cunha" "Garibaldi"      
## [49] "Bento_Gonçalves" "Bento_Gonçalves" "Bento_Gonçalves" "Canela"         
## [53] "Pinto_Bandeira"  "Flores_da_Cunha" "Nova_Padua"      "Cotipora"

E(net1)

## + 333/333 edges from a030f5e (vertex names):
##  [1] V1->V6  V1->V10 V1->V38 V1->V39 V1->V40 V2->V3  V2->V6  V2->V20 V2->V26
## [10] V2->V40 V3->V2  V3->V41 V3->V4  V3->V5  V3->V6  V3->V8  V3->V9  V3->V12
## [19] V3->V42 V3->V13 V3->V14 V3->V15 V3->V43 V3->V19 V3->V44 V3->V22 V3->V24
## [28] V3->V25 V3->V26 V3->V31 V3->V34 V3->V36 V4->V42 V4->V45 V4->V19 V5->V8 
## [37] V5->V10 V5->V11 V5->V46 V5->V13 V5->V16 V5->V18 V5->V44 V5->V26 V5->V47
## [46] V5->V31 V5->V34 V6->V2  V6->V3  V6->V4  V6->V10 V6->V43 V6->V29 V6->V40
## [55] V7->V4  V8->V3  V8->V4  V8->V5  V8->V6  V8->V9  V8->V48 V8->V42 V8->V15
## [64] V8->V38 V8->V49 V8->V19 V8->V44 V8->V21 V8->V22 V8->V23 V8->V24 V8->V26
## [73] V8->V29 V8->V47 V8->V50 V8->V51 V8->V34 V8->V35 V8->V40 V9->V4  V9->V5 
## [82] V9->V8  V9->V42 V9->V38 V9->V19 V9->V23 V9->V24 V9->V26 V9->V47 V9->V51
## + ... omitted several edges

E(net1)$Peso

##   [1] 2 2 2 2 2 1 2 1 1 1 1 1 2 1 2 3 2 1 1 1 1 2 1 2 1 1 1 1 2 1 1 1 1 3 1 3 3
##  [38] 3 3 2 3 3 3 3 2 3 1 3 2 1 3 1 1 3 3 3 2 1 1 2 1 1 2 1 1 1 1 2 1 1 1 1 1 1
##  [75] 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 1 1 2 2 2 3 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [112] 2 2 2 2 2 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 3 3 3 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## [149] 1 2 1 1 1 2 3 2 1 2 1 2 2 2 2 3 1 1 1 1 2 2 2 1 1 2 3 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 2 3 1 1 1 2 3 2 3 1 1 2 2 2 1 2 2 2 2 3 3 3 3 3 1 3 3 3 1 2 2 2 2 2 1
## [223] 1 2 2 2 2 2 2 2 1 2 2 2 3 1 1 1 1 2 2 2 1 1 2 1 1 3 2 1 3 2 2 3 2 3 1 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 3 3 2 3 1 1 2 2 1 2 1 1 1 1 1 2 1 1
## [297] 2 1 1 1 2 2 2 1 3 3 2 1 1 3 1 2 3 2 3 2 2 3 2 2 3 2 2 1 1 1 1 1 1 1 1 1 1

E(net1)$Freq

## NULL

class(net1)

## [1] "igraph"

net1

## IGRAPH a030f5e DN-- 56 333 -- 
## + attr: name (v/c), Vinicola (v/c), Cod_mun (v/n), Municipio (v/c),
## | Producao_total (v/n), Peso (e/n)
## + edges from a030f5e (vertex names):
##  [1] V1->V6  V1->V10 V1->V38 V1->V39 V1->V40 V2->V3  V2->V6  V2->V20 V2->V26
## [10] V2->V40 V3->V2  V3->V41 V3->V4  V3->V5  V3->V6  V3->V8  V3->V9  V3->V12
## [19] V3->V42 V3->V13 V3->V14 V3->V15 V3->V43 V3->V19 V3->V44 V3->V22 V3->V24
## [28] V3->V25 V3->V26 V3->V31 V3->V34 V3->V36 V4->V42 V4->V45 V4->V19 V5->V8 
## [37] V5->V10 V5->V11 V5->V46 V5->V13 V5->V16 V5->V18 V5->V44 V5->V26 V5->V47
## [46] V5->V31 V5->V34 V6->V2  V6->V3  V6->V4  V6->V10 V6->V43 V6->V29 V6->V40
## [55] V7->V4  V8->V3  V8->V4  V8->V5  V8->V6  V8->V9  V8->V48 V8->V42 V8->V15
## + ... omitted several edges

# Grafo simples
plot(net1, edge.arrow.size = 0.1, vertex.label.color = "black", main = "Netwine")

set.seed(222)
plot(net1, vertex.color = "orange", edge.arrow.size = 0.1, vertex.size = 30, vertex.label.cex = 1, vertex.label.color = "black",
     main = "Netwine")

3.2 Medidas da rede

# Densidade - arestas presentes/arestas possíveis
edge_density(net1, loops = F)

## [1] 0.1081169

# Reciprocidade - proporção de vínculos recíprocos (rede direcionada)
reciprocity(net1)

## [1] 0.3483483

# Transitividade - avalia a probabilidade dos vértices adjacentes a um vértice estarem conectados 
transitivity(net1, type = "global")

## [1] 0.3660131

transitividadeLocal1 <- transitivity(net1, type = "local")
triad_census(net1)

##  [1] 16094  7190  1660   594   421   393   296   503   138     2   121    50
## [13]   130    32    69    27

# Diâmetro - maior distância geodésica - comprimento do caminho mais curso entre dois vértices
diameter(net1, directed = T, weight = NA)

## [1] 5

# Graus - número de arestas conectadas de cada nó
degree(net1, mode = "all", normalized = F) # todos 

##  V1  V2  V3  V4  V5  V6  V7  V8  V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 
##   8  10  31  20  19  14   1  38  17  37  16  12  11  17  10  11   6  20  28  11 
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40 
##  17   7  12  19  14  26   3   7   8  13  12   5   9  32  12   2  16  10   4   5 
## V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 
##   5  27   3   9   2   6  12   2   8   2   8   3   5   1   3   0

degree(net1, mode = "in") # Flechas que estão chegando

##  V1  V2  V3  V4  V5  V6  V7  V8  V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 
##   3   5   9  17   7   7   0  14   5  15   4   6   8   6   5   5   0   6  19   4 
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40 
##   5   4   4   7   3  20   2   1   3   1   7   1   4  12   3   1   5  10   4   5 
## V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 
##   5  17   3   9   2   6  12   2   8   2   8   3   5   1   3   0

degree(net1, mode = "out") # Flechas que estão saindo

##  V1  V2  V3  V4  V5  V6  V7  V8  V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 
##   5   5  22   3  12   7   1  24  12  22  12   6   3  11   5   6   6  14   9   7 
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40 
##  12   3   8  12  11   6   1   6   5  12   5   4   5  20   9   1  11   0   0   0 
## V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 
##   0  10   0   0   0   0   0   0   0   0   0   0   0   0   0   0

centr_degree(net1, mode = "in", normalized = T)

## $res
##  [1]  3  5  9 17  7  7  0 14  5 15  4  6  8  6  5  5  0  6 19  4  5  4  4  7  3
## [26] 20  2  1  3  1  7  1  4 12  3  1  5 10  4  5  5 17  3  9  2  6 12  2  8  2
## [51]  8  3  5  1  3  0
## 
## $centralization
## [1] 0.2555195
## 
## $theoretical_max
## [1] 3080

grauTotal1 <- degree(net1, mode = "all")
grauIn1 <- degree(net1, mode = "in") 
grauOut1 <- degree(net1, mode = "out")
dyad_census(net1)

## $mut
## [1] 58
## 
## $asym
## [1] 217
## 
## $null
## [1] 1265

triad_census(net1)

##  [1] 16094  7190  1660   594   421   393   296   503   138     2   121    50
## [13]   130    32    69    27

sd(grauTotal1)

## [1] 9.213979

# Distribuição dos grau
grau.dist1 <- degree_distribution(net1, cumulative = T, mode = "all")
plot(x = 0:max(grauTotal1), y = 1-grau.dist1, pch = 19, cex = 1.5, col = "orange",
     xlab = "Grau", ylab = "Freq. acumulada", main = "Escreva o título aqui")

# Proximidade
closeness1 <- closeness(net1, mode = "all", weight = NA)
centr_clo1 <- centr_clo(net1, mode = "all", normalized = T)$res
eigen_centrality1 <- eigen_centrality(net1, directed = T, weights = NA)$vector
centr_eigen1 <- centr_eigen(net1, directed = T, normalized = T)$vector
closeness(net1, vids = V(net1),
          mode = c("out", "in", "all","total"), weights = NULL, normalized = FALSE)

##           V1           V2           V3           V4           V5           V6 
## 0.0033222591 0.0034364261 0.0039215686 0.0031948882 0.0037174721 0.0035335689 
##           V7           V8           V9          V10          V11          V12 
## 0.0032258065 0.0039840637 0.0036630037 0.0038910506 0.0037037037 0.0035335689 
##          V13          V14          V15          V16          V17          V18 
## 0.0033444816 0.0035587189 0.0034602076 0.0033112583 0.0044052863 0.0036900369 
##          V19          V20          V21          V22          V23          V24 
## 0.0036231884 0.0033222591 0.0037037037 0.0029239766 0.0034722222 0.0036630037 
##          V25          V26          V27          V28          V29          V30 
## 0.0037313433 0.0034129693 0.0030303030 0.0034965035 0.0034129693 0.0036496350 
##          V31          V32          V33          V34          V35          V36 
## 0.0034246575 0.0031948882 0.0033003300 0.0038759690 0.0035335689 0.0032573290 
##          V37          V38          V39          V40          V41          V42 
## 0.0035335689 0.0003246753 0.0003246753 0.0003246753 0.0003246753 0.0035842294 
##          V43          V44          V45          V46          V47          V48 
## 0.0003246753 0.0003246753 0.0003246753 0.0003246753 0.0003246753 0.0003246753 
##          V49          V50          V51          V52          V53          V54 
## 0.0003246753 0.0003246753 0.0003246753 0.0003246753 0.0003246753 0.0003246753 
##          V55          V56 
## 0.0003246753 0.0003246753

# Entrelaçamento
betweenness1 <- betweenness(net1, directed = T, weights = NA, normalized = F)
edge_betweenness(net1, directed = T, weights = NA)

##   [1] 10.427778 40.820116  2.333333 16.733333  3.506061 41.094444  5.794444
##   [8] 22.697253  5.627778  6.159307 13.162288  8.050000  5.107576  9.931227
##  [15]  7.095310 24.326984 10.504762 21.082540  3.963492  3.255556 49.728571
##  [22] 13.700000 12.661111  3.111111  5.019048 10.374206 25.059318 29.748413
##  [29]  3.911111 14.287673 16.409524 37.194444 58.822819 29.575758 73.535828
##  [36] 19.930988 11.515115 13.266306  4.869048  2.116667 10.958730  6.769048
##  [43]  1.533333  3.616667  1.920635  7.676984  5.683333  5.324242 33.698687
##  [50]  4.940909 21.907454  5.655556 12.116270  4.158442 53.000000 22.306746
##  [57]  5.274242 15.583894 11.333947  6.338889 31.216667  2.816667 16.217857
##  [64]  8.833333  7.590476  3.518723  8.371429 26.849242 12.457540 19.869841
##  [71] 22.475660  6.651623 12.881385  4.212302 18.980952  5.015584 14.736183
##  [78] 90.006116 21.161039  2.190909  7.437179 11.574603  3.946825  3.261905
##  [85]  3.426623  6.836508 10.491847  4.726190  1.253968  1.600433 10.014683
##  [92] 33.562771 16.324625 42.961905  9.000433 19.278846 11.396970 16.667857
##  [99] 12.190476  5.977778 19.683333 11.166667 38.847086  8.488131 11.397619
## [106] 17.146429 38.142829  7.233333  9.305556 19.019048  6.633766 31.820635
## [113] 28.191558  2.583333 20.610426  2.807576  6.584524  3.016667  4.361111
## [120]  2.869048  6.590115  3.761111  6.316667  5.616667  5.134199 33.563803
## [127]  3.226190 27.046415  5.054798  3.850000  9.242063 36.923016 14.016667
## [134]  5.016667  4.833333 33.799315 14.485714  4.795238 18.466667 13.854762
## [141]  3.000000 16.716667  9.226623 38.000000  5.535714  2.592857 32.248399
## [148]  5.427381  7.778211 15.985714  3.250000  5.870707 20.880456 13.039272
## [155]  2.369048 46.878805 25.133333 11.341667  1.000000  5.733333  5.416667
## [162]  4.375000 10.751787  3.307576 17.224423  5.508009  8.517100  2.250000
## [169]  9.519048 12.167100  4.053247  1.916667  4.813492  2.783333  7.922727
## [176] 13.626623  8.750433 65.341592 47.567360 22.783983 10.432540  6.288889
## [183] 12.930037 20.720238 68.672941 11.947894 17.037454  3.566667 22.058425
## [190]  4.914560  5.758333  4.674242  2.424675 23.462721  6.165090 25.335376
## [197]  6.783333  7.176659  4.166667 13.016342 11.468204  3.500000 41.176190
## [204] 31.202500 22.517305 34.243013  3.086905  3.708766 10.277381  1.166667
## [211]  8.169084  1.000000 21.131349  3.015152 12.580952 13.309957 19.351437
## [218]  2.485714 14.582179 17.311941  5.203968  2.285714  4.336905 15.787302
## [225]  4.869048  1.920635 45.209921 19.610750 11.776984  2.817857  5.287302
## [232]  1.200000  2.292857  1.920635  2.887302  8.340476  7.559524  9.938492
## [239] 24.860448 39.710700  6.594444 14.631385 36.057850  7.583766 55.226190
## [246] 25.663095  8.209524  1.700000  9.051190  7.886905  5.316667 35.818651
## [253]  3.333766  8.342857  3.903571  9.319048 16.695346  4.798846  6.409957
## [260]  2.225433  1.000000  1.000000  5.836147  2.833333  6.483009  2.174242
## [267]  4.250000  6.503608  7.896775 24.887698 10.280952 13.582143 12.997619
## [274]  7.232540  4.700000  1.476190 42.767460 22.551732  8.424242 18.775974
## [281] 13.763370  5.891342 16.351732  4.832418  9.715657 14.779509  8.167172
## [288] 11.156133 23.855628  9.561905  2.724242 19.550830 12.394059  4.178247
## [295]  3.969048 28.594949 14.223088  3.369913  1.878968 15.682912  3.167100
## [302] 41.971429  6.701623  3.444048 23.636147  9.453571 13.733333 13.080286
## [309]  2.200000 42.827381  1.750000 52.194444  9.612410  1.833333  9.033983
## [316] 12.548160  2.785714  8.523160  2.033333  5.119913 32.339286  1.250000
## [323] 13.654870 19.283333 64.019936  5.167100 45.233788 20.585761 10.227778
## [330] 12.844048  7.259163  7.920635  9.466017

centr_betw(net1, directed = T, normalized = T)

## $res
##  [1]  21.8206210  29.3732268 275.6842657 109.9344045  37.8568543  35.8015596
##  [7]   0.0000000 342.7003386  14.7616744 362.4376512  18.2514430  29.9832695
## [13]   3.9563492 110.7140332  12.0325619  40.2882867   0.0000000  52.3611305
## [19] 211.4880120  17.9575758 123.8777584   7.8472222   9.0493506  94.6547203
## [25]  21.6321789  77.4385947   3.2261905   5.8273810   8.7178932   8.2099206
## [31]  17.6451881   4.1761905  17.4066600 198.1249362  64.8263903   0.1944444
## [37]  46.7341631   0.0000000   0.0000000   0.0000000   0.0000000 150.0075591
## [43]   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000
## [49]   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000
## [55]   0.0000000   0.0000000
## 
## $centralization
## [1] 0.1084145
## 
## $theoretical_max
## [1] 163350

# Distância
mean_distance(net1, directed = T)

## [1] 2.307887

# Burt’s constraint  -  Structural holes#
Burtconstraint <- constraint(net1, nodes = V(net1), weights = NULL)

# Força #
strength1 <- strength(net1, vids = V(net1), mode = "all", loops = TRUE, weights = NULL)

#Homofilia - tendência de um nó se conectar a outro nó similar através de uma varíavel/número de nós
assortativity_degree(net1, directed=T) #Correlação entre os nós.

## [1] -0.1501508

# Autoridade e Hub
hub1 <- hub_score(net1)$vector #hub
autoridade1 <- authority.score(net1)$vector # autoridade

par(mfrow = c(1,2))
set.seed(123)

plot(net1,
     vertex.size = hub1*50,
     vertex.color = rainbow(56),
     edge.arrow.size = 0.1,
     layout = layout.kamada.kawai, main = "HUBS", vertex.label.color = "black")

plot(net1,
     vertex.size = autoridade1*50,
     vertex.color = rainbow(56),
     edge.arrow.size = 0.1,
     layout = layout.kamada.kawai, main = "Autoridade", vertex.label.color = "black")

dev.off()

## null device 
##           1

# Cohesive_blocks - Modelo centro-periferia #
# Geração do modelo centro-periferia
# Cada vinícola recebeu um rótulo de acordo com a sua posição (centro X periferia)
# Os dados foram depois exportados como arquivo pajek no software Gephi para a formação da rede disponível no artigo

BLOKS1 <- cohesive_blocks(as.undirected(net1), labels = TRUE)
BLOKS1$parent

## [1] 0 1 2 3 4 5 6 7

BLOKS1$cohesion

## [1] 0 1 2 3 4 5 6 7

BLOKS1$blocks

## [[1]]
## + 56/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V7  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19
## [20] V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## [39] V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56
## 
## [[2]]
## + 55/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V7  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19
## [20] V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## [39] V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55
## 
## [[3]]
## + 53/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## [20] V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39
## [39] V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V55
## 
## [[4]]
## + 49/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## [20] V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V37 V38 V39 V40
## [39] V41 V42 V43 V44 V46 V47 V49 V51 V52 V53 V55
## 
## [[5]]
## + 45/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## [20] V21 V22 V23 V24 V25 V26 V28 V29 V30 V31 V32 V33 V34 V35 V37 V38 V39 V40 V41
## [39] V42 V44 V46 V47 V49 V51 V53
## 
## [[6]]
## + 43/56 vertices, named, from a0ed695:
##  [1] V1  V2  V3  V4  V5  V6  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## [20] V21 V22 V23 V24 V25 V26 V28 V29 V30 V31 V33 V34 V35 V37 V38 V40 V41 V42 V44
## [39] V46 V47 V49 V51 V53
## 
## [[7]]
## + 37/56 vertices, named, from a0ed695:
##  [1] V2  V3  V4  V5  V6  V8  V9  V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## [20] V22 V23 V24 V25 V26 V30 V31 V33 V34 V35 V37 V38 V42 V44 V46 V47 V49 V51
## 
## [[8]]
## + 29/56 vertices, named, from a0ed695:
##  [1] V3  V4  V5  V8  V9  V10 V11 V12 V14 V15 V16 V18 V19 V21 V22 V23 V24 V25 V26
## [20] V30 V31 V34 V35 V37 V38 V42 V47 V49 V51

hierarchy(BLOKS1)

## IGRAPH a18c49a D--- 8 7 -- 
## + edges from a18c49a:
## [1] 1->2 2->3 3->4 4->5 5->6 6->7 7->8

max_cohesion(BLOKS1)

##  [1] 5 6 7 7 7 6 1 7 7 7 7 7 6 7 7 7 6 7 7 6 7 7 7 7 7 7 3 5 5 7 7 4 6 7 7 2 7 7
## [39] 4 5 5 7 3 6 2 6 7 2 7 2 7 3 5 1 3 0

length(BLOKS1)

## [1] 8

blok1 <- max_cohesion(BLOKS1)
blok1 <- ifelse(blok1 == 7, 1 , 0)
V(net1)$blokss1 <- blok1

blok1 <- factor(blok1,label = c("Periferia","Centro"), levels = 0:1)

#export_pajek(mwBlocks, mw, file="/tmp/mwBlocks.paj")
par(mar = c(0,0,0,0))
plot(BLOKS1, net1, vertex.size = grauTotal1, edge.arrow.size = 0.1,
     vertex.label = V(net1)$id)

plot(net1, vertex.size = grauTotal1, edge.arrow.size = 0.1,
     vertex.label = V(net1)$id, vertex.color = V(net1)$blokss1)

coreness <- graph.coreness(net1, mode = "all")
colbar <- rainbow(max(coreness))
plot(net1, vertex.color=colbar[coreness], 
     vertex.frame.color=colbar[coreness],
     edge.arrow.size = 0.001, vertex.size = grauTotal1)

# eccentricity #

eccentricity1 <- eccentricity(net1, vids = V(net1), mode = c("all", "out", "in",
                                                             "total"))

eccentricity2 <- eccentricity(net1, vids = V(net1), mode = c("all"))

# Ego #

ego_size1 <- ego_size(net1, order = 1, nodes = V(net1), mode = "all", mindist = 0)

# Circunferência #

girth(net1, circle = TRUE)

## $girth
## [1] 3
## 
## $circle
## + 3/56 vertices, named, from a030f5e:
## [1] V6  V1  V10

# power_centrality #

power_centrality1 <- power_centrality(net1, nodes = V(net1), loops = FALSE,
                                      exponent = 1, rescale = FALSE, tol = 1e-07, sparse = TRUE)

3.3 Subgrupos e comunidades - Cliques

graf.sym1 <- as.undirected(net1, mode = "collapse",
                           edge.attr.comb = list(weight = "sum", "ignore"))

cliques(as.undirected(net1))

## [[1]]
## + 1/56 vertex, named, from a229d17:
## [1] V10
## 
## [[2]]
## + 1/56 vertex, named, from a229d17:
## [1] V8
## 
## [[3]]
## + 1/56 vertex, named, from a229d17:
## [1] V14
## 
## [[4]]
## + 1/56 vertex, named, from a229d17:
## [1] V16
## 
## [[5]]
## + 1/56 vertex, named, from a229d17:
## [1] V20
## 
## [[6]]
## + 1/56 vertex, named, from a229d17:
## [1] V39
## 
## [[7]]
## + 1/56 vertex, named, from a229d17:
## [1] V55
## 
## [[8]]
## + 1/56 vertex, named, from a229d17:
## [1] V45
## 
## [[9]]
## + 1/56 vertex, named, from a229d17:
## [1] V7
## 
## [[10]]
## + 1/56 vertex, named, from a229d17:
## [1] V56
## 
## [[11]]
## + 1/56 vertex, named, from a229d17:
## [1] V42
## 
## [[12]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V42
## 
## [[13]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V42
## 
## [[14]]
## + 1/56 vertex, named, from a229d17:
## [1] V26
## 
## [[15]]
## + 2/56 vertices, named, from a229d17:
## [1] V20 V26
## 
## [[16]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V26
## 
## [[17]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V26
## 
## [[18]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V26
## 
## [[19]]
## + 1/56 vertex, named, from a229d17:
## [1] V38
## 
## [[20]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V38
## 
## [[21]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V38
## 
## [[22]]
## + 1/56 vertex, named, from a229d17:
## [1] V6
## 
## [[23]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V20
## 
## [[24]]
## + 2/56 vertices, named, from a229d17:
## [1] V6 V8
## 
## [[25]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V10
## 
## [[26]]
## + 1/56 vertex, named, from a229d17:
## [1] V44
## 
## [[27]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V44
## 
## [[28]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V44
## 
## [[29]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V44
## 
## [[30]]
## + 1/56 vertex, named, from a229d17:
## [1] V46
## 
## [[31]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V46
## 
## [[32]]
## + 1/56 vertex, named, from a229d17:
## [1] V28
## 
## [[33]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V28
## 
## [[34]]
## + 1/56 vertex, named, from a229d17:
## [1] V53
## 
## [[35]]
## + 2/56 vertices, named, from a229d17:
## [1] V20 V53
## 
## [[36]]
## + 1/56 vertex, named, from a229d17:
## [1] V22
## 
## [[37]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V22
## 
## [[38]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V22
## 
## [[39]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V22
## 
## [[40]]
## + 1/56 vertex, named, from a229d17:
## [1] V52
## 
## [[41]]
## + 2/56 vertices, named, from a229d17:
## [1] V20 V52
## 
## [[42]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V52
## 
## [[43]]
## + 1/56 vertex, named, from a229d17:
## [1] V48
## 
## [[44]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V48
## 
## [[45]]
## + 1/56 vertex, named, from a229d17:
## [1] V36
## 
## [[46]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V36
## 
## [[47]]
## + 1/56 vertex, named, from a229d17:
## [1] V54
## 
## [[48]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V54
## 
## [[49]]
## + 1/56 vertex, named, from a229d17:
## [1] V50
## 
## [[50]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V50
## 
## [[51]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V50
## 
## [[52]]
## + 1/56 vertex, named, from a229d17:
## [1] V3
## 
## [[53]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V36
## 
## [[54]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V36
## 
## [[55]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V22
## 
## [[56]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V22
## 
## [[57]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V22
## 
## [[58]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V44
## 
## [[59]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V44
## 
## [[60]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V44
## 
## [[61]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V44
## 
## [[62]]
## + 2/56 vertices, named, from a229d17:
## [1] V3 V6
## 
## [[63]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V6 V8
## 
## [[64]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V6  V10
## 
## [[65]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V26
## 
## [[66]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V26
## 
## [[67]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V26
## 
## [[68]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V26
## 
## [[69]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V42
## 
## [[70]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V42
## 
## [[71]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V42
## 
## [[72]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V14
## 
## [[73]]
## + 2/56 vertices, named, from a229d17:
## [1] V3 V8
## 
## [[74]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V10
## 
## [[75]]
## + 1/56 vertex, named, from a229d17:
## [1] V47
## 
## [[76]]
## + 2/56 vertices, named, from a229d17:
## [1] V42 V47
## 
## [[77]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V42 V47
## 
## [[78]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V42 V47
## 
## [[79]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V47
## 
## [[80]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V47
## 
## [[81]]
## + 1/56 vertex, named, from a229d17:
## [1] V11
## 
## [[82]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V52
## 
## [[83]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V53
## 
## [[84]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V26
## 
## [[85]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V11 V26
## 
## [[86]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V16
## 
## [[87]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V11
## 
## [[88]]
## + 1/56 vertex, named, from a229d17:
## [1] V30
## 
## [[89]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V38
## 
## [[90]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V30 V38
## 
## [[91]]
## + 2/56 vertices, named, from a229d17:
## [1] V26 V30
## 
## [[92]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V26 V30
## 
## [[93]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V42
## 
## [[94]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V30 V42
## 
## [[95]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V39
## 
## [[96]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V30
## 
## [[97]]
## + 1/56 vertex, named, from a229d17:
## [1] V51
## 
## [[98]]
## + 2/56 vertices, named, from a229d17:
## [1] V26 V51
## 
## [[99]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V26 V51
## 
## [[100]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V26 V51
## 
## [[101]]
## + 2/56 vertices, named, from a229d17:
## [1] V42 V51
## 
## [[102]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V42 V51
## 
## [[103]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V42 V51
## 
## [[104]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V51
## 
## [[105]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V51
## 
## [[106]]
## + 1/56 vertex, named, from a229d17:
## [1] V40
## 
## [[107]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V40
## 
## [[108]]
## + 3/56 vertices, named, from a229d17:
## [1] V6  V8  V40
## 
## [[109]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V40
## 
## [[110]]
## + 1/56 vertex, named, from a229d17:
## [1] V32
## 
## [[111]]
## + 2/56 vertices, named, from a229d17:
## [1] V32 V44
## 
## [[112]]
## + 2/56 vertices, named, from a229d17:
## [1] V32 V39
## 
## [[113]]
## + 1/56 vertex, named, from a229d17:
## [1] V29
## 
## [[114]]
## + 2/56 vertices, named, from a229d17:
## [1] V29 V53
## 
## [[115]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V29
## 
## [[116]]
## + 3/56 vertices, named, from a229d17:
## [1] V6  V8  V29
## 
## [[117]]
## + 2/56 vertices, named, from a229d17:
## [1] V29 V42
## 
## [[118]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V29 V42
## 
## [[119]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V29
## 
## [[120]]
## + 1/56 vertex, named, from a229d17:
## [1] V27
## 
## [[121]]
## + 2/56 vertices, named, from a229d17:
## [1] V27 V28
## 
## [[122]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V27
## 
## [[123]]
## + 1/56 vertex, named, from a229d17:
## [1] V17
## 
## [[124]]
## + 2/56 vertices, named, from a229d17:
## [1] V17 V38
## 
## [[125]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V17 V38
## 
## [[126]]
## + 2/56 vertices, named, from a229d17:
## [1] V17 V26
## 
## [[127]]
## + 3/56 vertices, named, from a229d17:
## [1] V17 V20 V26
## 
## [[128]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V17 V26
## 
## [[129]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V17 V26
## 
## [[130]]
## + 2/56 vertices, named, from a229d17:
## [1] V17 V20
## 
## [[131]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V17
## 
## [[132]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V17
## 
## [[133]]
## + 1/56 vertex, named, from a229d17:
## [1] V34
## 
## [[134]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V51
## 
## [[135]]
## + 3/56 vertices, named, from a229d17:
## [1] V26 V34 V51
## 
## [[136]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V26 V34 V51
## 
## [[137]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V26 V34 V51
## 
## [[138]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V42 V51
## 
## [[139]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V34 V42 V51
## 
## [[140]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V34 V42 V51
## 
## [[141]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V34 V51
## 
## [[142]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V34 V51
## 
## [[143]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V34
## 
## [[144]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V26 V34
## 
## [[145]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V11 V26 V34
## 
## [[146]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V16 V34
## 
## [[147]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V11 V34
## 
## [[148]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V47
## 
## [[149]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V42 V47
## 
## [[150]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V34 V42 V47
## 
## [[151]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V34 V42 V47
## 
## [[152]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V34 V47
## 
## [[153]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V34 V47
## 
## [[154]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V34
## 
## [[155]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V34 V44
## 
## [[156]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V34 V44
## 
## [[157]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V34 V44
## 
## [[158]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V6  V34
## 
## [[159]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V6  V8  V34
## 
## [[160]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V6  V10 V34
## 
## [[161]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V26 V34
## 
## [[162]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V26 V34
## 
## [[163]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V26 V34
## 
## [[164]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V34 V42
## 
## [[165]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V34 V42
## 
## [[166]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V34 V42
## 
## [[167]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V34
## 
## [[168]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V34
## 
## [[169]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V46
## 
## [[170]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V34 V46
## 
## [[171]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V44
## 
## [[172]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V34 V44
## 
## [[173]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V34 V44
## 
## [[174]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V34
## 
## [[175]]
## + 3/56 vertices, named, from a229d17:
## [1] V6  V8  V34
## 
## [[176]]
## + 3/56 vertices, named, from a229d17:
## [1] V6  V10 V34
## 
## [[177]]
## + 2/56 vertices, named, from a229d17:
## [1] V26 V34
## 
## [[178]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V26 V34
## 
## [[179]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V26 V34
## 
## [[180]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V42
## 
## [[181]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V34 V42
## 
## [[182]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V34 V42
## 
## [[183]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V34
## 
## [[184]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V34
## 
## [[185]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V34
## 
## [[186]]
## + 1/56 vertex, named, from a229d17:
## [1] V4
## 
## [[187]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V32
## 
## [[188]]
## + 2/56 vertices, named, from a229d17:
## [1] V3 V4
## 
## [[189]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V22
## 
## [[190]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V22
## 
## [[191]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V22
## 
## [[192]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V4 V6
## 
## [[193]]
## + 4/56 vertices, named, from a229d17:
## [1] V3 V4 V6 V8
## 
## [[194]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V6  V10
## 
## [[195]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V42
## 
## [[196]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V42
## 
## [[197]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V42
## 
## [[198]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V4 V8
## 
## [[199]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V10
## 
## [[200]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V22
## 
## [[201]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V16 V22
## 
## [[202]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V22
## 
## [[203]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V22
## 
## [[204]]
## + 2/56 vertices, named, from a229d17:
## [1] V4 V6
## 
## [[205]]
## + 3/56 vertices, named, from a229d17:
## [1] V4 V6 V8
## 
## [[206]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V6  V10
## 
## [[207]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V42
## 
## [[208]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V42
## 
## [[209]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V42
## 
## [[210]]
## + 2/56 vertices, named, from a229d17:
## [1] V4 V7
## 
## [[211]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V45
## 
## [[212]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V16
## 
## [[213]]
## + 2/56 vertices, named, from a229d17:
## [1] V4 V8
## 
## [[214]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V10
## 
## [[215]]
## + 1/56 vertex, named, from a229d17:
## [1] V33
## 
## [[216]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V33
## 
## [[217]]
## + 3/56 vertices, named, from a229d17:
## [1] V26 V30 V33
## 
## [[218]]
## + 3/56 vertices, named, from a229d17:
## [1] V30 V33 V42
## 
## [[219]]
## + 2/56 vertices, named, from a229d17:
## [1] V26 V33
## 
## [[220]]
## + 3/56 vertices, named, from a229d17:
## [1] V20 V26 V33
## 
## [[221]]
## + 2/56 vertices, named, from a229d17:
## [1] V33 V42
## 
## [[222]]
## + 2/56 vertices, named, from a229d17:
## [1] V33 V45
## 
## [[223]]
## + 2/56 vertices, named, from a229d17:
## [1] V20 V33
## 
## [[224]]
## + 1/56 vertex, named, from a229d17:
## [1] V13
## 
## [[225]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V13
## 
## [[226]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V13 V26
## 
## [[227]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V13 V14 V26
## 
## [[228]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V13 V26
## 
## [[229]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V13 V42
## 
## [[230]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V13 V42
## 
## [[231]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V13 V14
## 
## [[232]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V13
## 
## [[233]]
## + 2/56 vertices, named, from a229d17:
## [1] V13 V28
## 
## [[234]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V13 V28
## 
## [[235]]
## + 2/56 vertices, named, from a229d17:
## [1] V13 V26
## 
## [[236]]
## + 3/56 vertices, named, from a229d17:
## [1] V13 V14 V26
## 
## [[237]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V13 V26
## 
## [[238]]
## + 2/56 vertices, named, from a229d17:
## [1] V13 V42
## 
## [[239]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V13 V42
## 
## [[240]]
## + 2/56 vertices, named, from a229d17:
## [1] V13 V14
## 
## [[241]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V13
## 
## [[242]]
## + 1/56 vertex, named, from a229d17:
## [1] V49
## 
## [[243]]
## + 2/56 vertices, named, from a229d17:
## [1] V29 V49
## 
## [[244]]
## + 3/56 vertices, named, from a229d17:
## [1] V29 V42 V49
## 
## [[245]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V29 V42 V49
## 
## [[246]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V29 V49
## 
## [[247]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V49
## 
## [[248]]
## + 3/56 vertices, named, from a229d17:
## [1] V30 V42 V49
## 
## [[249]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V49
## 
## [[250]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V16 V49
## 
## [[251]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V11 V49
## 
## [[252]]
## + 2/56 vertices, named, from a229d17:
## [1] V22 V49
## 
## [[253]]
## + 3/56 vertices, named, from a229d17:
## [1] V16 V22 V49
## 
## [[254]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V22 V49
## 
## [[255]]
## + 2/56 vertices, named, from a229d17:
## [1] V42 V49
## 
## [[256]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V42 V49
## 
## [[257]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V49
## 
## [[258]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V49
## 
## [[259]]
## + 1/56 vertex, named, from a229d17:
## [1] V2
## 
## [[260]]
## + 2/56 vertices, named, from a229d17:
## [1] V2  V40
## 
## [[261]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V6  V40
## 
## [[262]]
## + 2/56 vertices, named, from a229d17:
## [1] V2 V3
## 
## [[263]]
## + 3/56 vertices, named, from a229d17:
## [1] V2 V3 V6
## 
## [[264]]
## + 4/56 vertices, named, from a229d17:
## [1] V2  V3  V6  V10
## 
## [[265]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V3  V26
## 
## [[266]]
## + 4/56 vertices, named, from a229d17:
## [1] V2  V3  V10 V26
## 
## [[267]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V3  V10
## 
## [[268]]
## + 2/56 vertices, named, from a229d17:
## [1] V2 V6
## 
## [[269]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V6  V20
## 
## [[270]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V6  V10
## 
## [[271]]
## + 2/56 vertices, named, from a229d17:
## [1] V2  V26
## 
## [[272]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V20 V26
## 
## [[273]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V10 V26
## 
## [[274]]
## + 2/56 vertices, named, from a229d17:
## [1] V2  V20
## 
## [[275]]
## + 2/56 vertices, named, from a229d17:
## [1] V2  V10
## 
## [[276]]
## + 1/56 vertex, named, from a229d17:
## [1] V1
## 
## [[277]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V40
## 
## [[278]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V6  V40
## 
## [[279]]
## + 2/56 vertices, named, from a229d17:
## [1] V1 V6
## 
## [[280]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V6  V20
## 
## [[281]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V6  V10
## 
## [[282]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V38
## 
## [[283]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V10 V38
## 
## [[284]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V39
## 
## [[285]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V20
## 
## [[286]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V10
## 
## [[287]]
## + 1/56 vertex, named, from a229d17:
## [1] V43
## 
## [[288]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V43
## 
## [[289]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V6  V43
## 
## [[290]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V6  V10 V43
## 
## [[291]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V43
## 
## [[292]]
## + 2/56 vertices, named, from a229d17:
## [1] V6  V43
## 
## [[293]]
## + 3/56 vertices, named, from a229d17:
## [1] V6  V10 V43
## 
## [[294]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V43
## 
## [[295]]
## + 1/56 vertex, named, from a229d17:
## [1] V41
## 
## [[296]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V41
## 
## [[297]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V16 V41
## 
## [[298]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V41
## 
## [[299]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V41 V42
## 
## [[300]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V41
## 
## [[301]]
## + 2/56 vertices, named, from a229d17:
## [1] V41 V42
## 
## [[302]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V41
## 
## [[303]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V41
## 
## [[304]]
## + 1/56 vertex, named, from a229d17:
## [1] V9
## 
## [[305]]
## + 2/56 vertices, named, from a229d17:
## [1] V4 V9
## 
## [[306]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V4 V9
## 
## [[307]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V9  V42
## 
## [[308]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V9  V42
## 
## [[309]]
## + 4/56 vertices, named, from a229d17:
## [1] V3 V4 V8 V9
## 
## [[310]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V9  V42
## 
## [[311]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V42
## 
## [[312]]
## + 3/56 vertices, named, from a229d17:
## [1] V4 V8 V9
## 
## [[313]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V34
## 
## [[314]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V34 V51
## 
## [[315]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V26 V34 V51
## 
## [[316]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V26 V34 V51
## 
## [[317]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V34 V42 V51
## 
## [[318]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V34 V42 V51
## 
## [[319]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V34 V51
## 
## [[320]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V34 V47
## 
## [[321]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V34 V42 V47
## 
## [[322]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V34 V42 V47
## 
## [[323]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V34 V47
## 
## [[324]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V9  V34
## 
## [[325]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V26 V34
## 
## [[326]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V26 V34
## 
## [[327]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V34 V42
## 
## [[328]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V34 V42
## 
## [[329]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V34
## 
## [[330]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V26 V34
## 
## [[331]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V26 V34
## 
## [[332]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V34 V42
## 
## [[333]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V34 V42
## 
## [[334]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V34
## 
## [[335]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V51
## 
## [[336]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V26 V51
## 
## [[337]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V26 V51
## 
## [[338]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V42 V51
## 
## [[339]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V42 V51
## 
## [[340]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V51
## 
## [[341]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V47
## 
## [[342]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V42 V47
## 
## [[343]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V42 V47
## 
## [[344]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V47
## 
## [[345]]
## + 2/56 vertices, named, from a229d17:
## [1] V3 V9
## 
## [[346]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V9  V26
## 
## [[347]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V26
## 
## [[348]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V9  V42
## 
## [[349]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V42
## 
## [[350]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V8 V9
## 
## [[351]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V38
## 
## [[352]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V38
## 
## [[353]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V26
## 
## [[354]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V26
## 
## [[355]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V42
## 
## [[356]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V42
## 
## [[357]]
## + 2/56 vertices, named, from a229d17:
## [1] V8 V9
## 
## [[358]]
## + 1/56 vertex, named, from a229d17:
## [1] V31
## 
## [[359]]
## + 2/56 vertices, named, from a229d17:
## [1] V31 V34
## 
## [[360]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V31 V34
## 
## [[361]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V31 V34
## 
## [[362]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V31 V34 V42
## 
## [[363]]
## + 3/56 vertices, named, from a229d17:
## [1] V31 V34 V42
## 
## [[364]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V31
## 
## [[365]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V31
## 
## [[366]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V31 V42
## 
## [[367]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V31
## 
## [[368]]
## + 2/56 vertices, named, from a229d17:
## [1] V31 V42
## 
## [[369]]
## + 2/56 vertices, named, from a229d17:
## [1] V31 V55
## 
## [[370]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V31
## 
## [[371]]
## + 1/56 vertex, named, from a229d17:
## [1] V37
## 
## [[372]]
## + 2/56 vertices, named, from a229d17:
## [1] V34 V37
## 
## [[373]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V34 V37
## 
## [[374]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V37 V47
## 
## [[375]]
## + 4/56 vertices, named, from a229d17:
## [1] V34 V37 V42 V47
## 
## [[376]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V37 V46
## 
## [[377]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V37 V44
## 
## [[378]]
## + 3/56 vertices, named, from a229d17:
## [1] V34 V37 V42
## 
## [[379]]
## + 2/56 vertices, named, from a229d17:
## [1] V27 V37
## 
## [[380]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V27 V37
## 
## [[381]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V37
## 
## [[382]]
## + 2/56 vertices, named, from a229d17:
## [1] V37 V47
## 
## [[383]]
## + 3/56 vertices, named, from a229d17:
## [1] V37 V42 V47
## 
## [[384]]
## + 2/56 vertices, named, from a229d17:
## [1] V37 V46
## 
## [[385]]
## + 2/56 vertices, named, from a229d17:
## [1] V37 V44
## 
## [[386]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V37 V44
## 
## [[387]]
## + 2/56 vertices, named, from a229d17:
## [1] V37 V38
## 
## [[388]]
## + 2/56 vertices, named, from a229d17:
## [1] V37 V42
## 
## [[389]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V37
## 
## [[390]]
## + 1/56 vertex, named, from a229d17:
## [1] V15
## 
## [[391]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V15
## 
## [[392]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V15
## 
## [[393]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V15 V42
## 
## [[394]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V15 V42
## 
## [[395]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V15
## 
## [[396]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V15 V42
## 
## [[397]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V15 V42
## 
## [[398]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V15 V16
## 
## [[399]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V15
## 
## [[400]]
## + 2/56 vertices, named, from a229d17:
## [1] V15 V30
## 
## [[401]]
## + 3/56 vertices, named, from a229d17:
## [1] V15 V30 V42
## 
## [[402]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V15
## 
## [[403]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V15 V42
## 
## [[404]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V15 V42
## 
## [[405]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V15
## 
## [[406]]
## + 2/56 vertices, named, from a229d17:
## [1] V15 V42
## 
## [[407]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V15 V42
## 
## [[408]]
## + 2/56 vertices, named, from a229d17:
## [1] V15 V16
## 
## [[409]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V15
## 
## [[410]]
## + 1/56 vertex, named, from a229d17:
## [1] V35
## 
## [[411]]
## + 2/56 vertices, named, from a229d17:
## [1] V2  V35
## 
## [[412]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V26 V35
## 
## [[413]]
## + 4/56 vertices, named, from a229d17:
## [1] V2  V20 V26 V35
## 
## [[414]]
## + 4/56 vertices, named, from a229d17:
## [1] V2  V10 V26 V35
## 
## [[415]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V20 V35
## 
## [[416]]
## + 3/56 vertices, named, from a229d17:
## [1] V2  V10 V35
## 
## [[417]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V35
## 
## [[418]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V35
## 
## [[419]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V35
## 
## [[420]]
## + 2/56 vertices, named, from a229d17:
## [1] V35 V51
## 
## [[421]]
## + 3/56 vertices, named, from a229d17:
## [1] V26 V35 V51
## 
## [[422]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V26 V35 V51
## 
## [[423]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V26 V35 V51
## 
## [[424]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V35 V51
## 
## [[425]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V35 V51
## 
## [[426]]
## + 2/56 vertices, named, from a229d17:
## [1] V30 V35
## 
## [[427]]
## + 3/56 vertices, named, from a229d17:
## [1] V26 V30 V35
## 
## [[428]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V26 V30 V35
## 
## [[429]]
## + 3/56 vertices, named, from a229d17:
## [1] V30 V35 V39
## 
## [[430]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V30 V35
## 
## [[431]]
## + 2/56 vertices, named, from a229d17:
## [1] V28 V35
## 
## [[432]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V28 V35
## 
## [[433]]
## + 2/56 vertices, named, from a229d17:
## [1] V26 V35
## 
## [[434]]
## + 3/56 vertices, named, from a229d17:
## [1] V20 V26 V35
## 
## [[435]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V26 V35
## 
## [[436]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V26 V35
## 
## [[437]]
## + 2/56 vertices, named, from a229d17:
## [1] V35 V39
## 
## [[438]]
## + 2/56 vertices, named, from a229d17:
## [1] V20 V35
## 
## [[439]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V35
## 
## [[440]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V35
## 
## [[441]]
## + 1/56 vertex, named, from a229d17:
## [1] V18
## 
## [[442]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V37
## 
## [[443]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V34 V37
## 
## [[444]]
## + 4/56 vertices, named, from a229d17:
## [1] V11 V18 V34 V37
## 
## [[445]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V34 V37 V47
## 
## [[446]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V34 V37 V46
## 
## [[447]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V18 V37
## 
## [[448]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V37 V47
## 
## [[449]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V37 V46
## 
## [[450]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V18 V37
## 
## [[451]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V31
## 
## [[452]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V31 V34
## 
## [[453]]
## + 4/56 vertices, named, from a229d17:
## [1] V11 V18 V31 V34
## 
## [[454]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V18 V31
## 
## [[455]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V31 V55
## 
## [[456]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V18 V31
## 
## [[457]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V34
## 
## [[458]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V34 V51
## 
## [[459]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V26 V34 V51
## 
## [[460]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V18 V26 V34 V51
## 
## [[461]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V34 V51
## 
## [[462]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V18 V34
## 
## [[463]]
## + 4/56 vertices, named, from a229d17:
## [1] V11 V18 V26 V34
## 
## [[464]]
## + 4/56 vertices, named, from a229d17:
## [1] V11 V16 V18 V34
## 
## [[465]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V34 V47
## 
## [[466]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V34 V47
## 
## [[467]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V34 V46
## 
## [[468]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V34 V46
## 
## [[469]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V26 V34
## 
## [[470]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V26 V34
## 
## [[471]]
## + 3/56 vertices, named, from a229d17:
## [1] V16 V18 V34
## 
## [[472]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V34
## 
## [[473]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V51
## 
## [[474]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V26 V51
## 
## [[475]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V26 V51
## 
## [[476]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V51
## 
## [[477]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V18
## 
## [[478]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V18 V53
## 
## [[479]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V18 V26
## 
## [[480]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V16 V18
## 
## [[481]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V47
## 
## [[482]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V47
## 
## [[483]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V53
## 
## [[484]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V46
## 
## [[485]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V46
## 
## [[486]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V26
## 
## [[487]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V18 V26
## 
## [[488]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V26
## 
## [[489]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V55
## 
## [[490]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V18
## 
## [[491]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V18
## 
## [[492]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V18
## 
## [[493]]
## + 1/56 vertex, named, from a229d17:
## [1] V24
## 
## [[494]]
## + 2/56 vertices, named, from a229d17:
## [1] V15 V24
## 
## [[495]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V15 V24
## 
## [[496]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V15 V24
## 
## [[497]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V15 V24 V42
## 
## [[498]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V15 V24 V42
## 
## [[499]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V15 V24
## 
## [[500]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V15 V24 V42
## 
## [[501]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V15 V24 V42
## 
## [[502]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V15 V24
## 
## [[503]]
## + 3/56 vertices, named, from a229d17:
## [1] V15 V24 V30
## 
## [[504]]
## + 4/56 vertices, named, from a229d17:
## [1] V15 V24 V30 V42
## 
## [[505]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V15 V24
## 
## [[506]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V15 V24 V42
## 
## [[507]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V15 V24 V42
## 
## [[508]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V15 V24
## 
## [[509]]
## + 3/56 vertices, named, from a229d17:
## [1] V15 V24 V42
## 
## [[510]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V15 V24 V42
## 
## [[511]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V15 V24
## 
## [[512]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V24
## 
## [[513]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V9  V24
## 
## [[514]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V9  V24
## 
## [[515]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V9  V24 V42
## 
## [[516]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V9  V24 V42
## 
## [[517]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V9  V24
## 
## [[518]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V9  V24 V42
## 
## [[519]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V24 V42
## 
## [[520]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V24
## 
## [[521]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V24 V47
## 
## [[522]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V24 V42 V47
## 
## [[523]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V24 V42 V47
## 
## [[524]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V24 V47
## 
## [[525]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V9  V24
## 
## [[526]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V24 V42
## 
## [[527]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V24 V42
## 
## [[528]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V24
## 
## [[529]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V24 V38
## 
## [[530]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V24 V38
## 
## [[531]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V24 V42
## 
## [[532]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V24 V42
## 
## [[533]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V24
## 
## [[534]]
## + 2/56 vertices, named, from a229d17:
## [1] V1  V24
## 
## [[535]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V24 V38
## 
## [[536]]
## + 4/56 vertices, named, from a229d17:
## [1] V1  V10 V24 V38
## 
## [[537]]
## + 3/56 vertices, named, from a229d17:
## [1] V1  V10 V24
## 
## [[538]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V49
## 
## [[539]]
## + 3/56 vertices, named, from a229d17:
## [1] V24 V30 V49
## 
## [[540]]
## + 4/56 vertices, named, from a229d17:
## [1] V24 V30 V42 V49
## 
## [[541]]
## + 3/56 vertices, named, from a229d17:
## [1] V22 V24 V49
## 
## [[542]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V22 V24 V49
## 
## [[543]]
## + 3/56 vertices, named, from a229d17:
## [1] V24 V42 V49
## 
## [[544]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V24 V42 V49
## 
## [[545]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V24 V49
## 
## [[546]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V24
## 
## [[547]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V24
## 
## [[548]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V22 V24
## 
## [[549]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V22 V24
## 
## [[550]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V22 V24
## 
## [[551]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V24 V42
## 
## [[552]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V24 V42
## 
## [[553]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V24 V42
## 
## [[554]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V24
## 
## [[555]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V24
## 
## [[556]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V22 V24
## 
## [[557]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V22 V24
## 
## [[558]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V22 V24
## 
## [[559]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V24 V42
## 
## [[560]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V24 V42
## 
## [[561]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V24 V42
## 
## [[562]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V24
## 
## [[563]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V24
## 
## [[564]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V30
## 
## [[565]]
## + 3/56 vertices, named, from a229d17:
## [1] V24 V30 V38
## 
## [[566]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V24 V30 V38
## 
## [[567]]
## + 3/56 vertices, named, from a229d17:
## [1] V24 V30 V42
## 
## [[568]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V24 V30 V42
## 
## [[569]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V24 V30
## 
## [[570]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V47
## 
## [[571]]
## + 3/56 vertices, named, from a229d17:
## [1] V24 V42 V47
## 
## [[572]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V24 V42 V47
## 
## [[573]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V24 V42 V47
## 
## [[574]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V24 V47
## 
## [[575]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V24 V47
## 
## [[576]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V24
## 
## [[577]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V22 V24
## 
## [[578]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V22 V24
## 
## [[579]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V22 V24
## 
## [[580]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V24 V42
## 
## [[581]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V24 V42
## 
## [[582]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V24 V42
## 
## [[583]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V24
## 
## [[584]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V24
## 
## [[585]]
## + 2/56 vertices, named, from a229d17:
## [1] V22 V24
## 
## [[586]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V22 V24
## 
## [[587]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V22 V24
## 
## [[588]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V38
## 
## [[589]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V24 V38
## 
## [[590]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V24 V38
## 
## [[591]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V42
## 
## [[592]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V24 V42
## 
## [[593]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V24 V42
## 
## [[594]]
## + 2/56 vertices, named, from a229d17:
## [1] V24 V55
## 
## [[595]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V24
## 
## [[596]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V24
## 
## [[597]]
## + 1/56 vertex, named, from a229d17:
## [1] V19
## 
## [[598]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V35
## 
## [[599]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V19 V35
## 
## [[600]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V19 V35
## 
## [[601]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V19 V35
## 
## [[602]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V30 V35
## 
## [[603]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V26 V30 V35
## 
## [[604]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V19 V26 V30 V35
## 
## [[605]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V30 V35
## 
## [[606]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V28 V35
## 
## [[607]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V28 V35
## 
## [[608]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V26 V35
## 
## [[609]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V20 V26 V35
## 
## [[610]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V19 V26 V35
## 
## [[611]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V26 V35
## 
## [[612]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V20 V35
## 
## [[613]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V19 V35
## 
## [[614]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V35
## 
## [[615]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V31
## 
## [[616]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V31 V34
## 
## [[617]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V19 V31 V34
## 
## [[618]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V19 V31 V34 V42
## 
## [[619]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V31 V34 V42
## 
## [[620]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V19 V31
## 
## [[621]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V19 V31 V42
## 
## [[622]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V14 V19 V31
## 
## [[623]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V31 V42
## 
## [[624]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V19 V31
## 
## [[625]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V19
## 
## [[626]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V9  V19
## 
## [[627]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V9  V19
## 
## [[628]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V9  V19 V42
## 
## [[629]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V9  V19 V42
## 
## [[630]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V9  V19
## 
## [[631]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V9  V19 V42
## 
## [[632]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V19 V42
## 
## [[633]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V19
## 
## [[634]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V19 V34
## 
## [[635]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V19 V34
## 
## [[636]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V9  V19 V26 V34
## 
## [[637]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19 V26 V34
## 
## [[638]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V9  V19 V34 V42
## 
## [[639]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19 V34 V42
## 
## [[640]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19 V34
## 
## [[641]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V19 V26 V34
## 
## [[642]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V19 V26 V34
## 
## [[643]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V19 V34 V42
## 
## [[644]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V19 V34 V42
## 
## [[645]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V19 V34
## 
## [[646]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V9  V19
## 
## [[647]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V19 V26
## 
## [[648]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19 V26
## 
## [[649]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V9  V19 V42
## 
## [[650]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19 V42
## 
## [[651]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V9  V19
## 
## [[652]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V19 V26
## 
## [[653]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V19 V26
## 
## [[654]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V19 V42
## 
## [[655]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V19 V42
## 
## [[656]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V19
## 
## [[657]]
## + 2/56 vertices, named, from a229d17:
## [1] V13 V19
## 
## [[658]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V13 V19
## 
## [[659]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V13 V19 V26
## 
## [[660]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V13 V14 V19 V26
## 
## [[661]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V13 V19 V26
## 
## [[662]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V13 V19 V42
## 
## [[663]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V13 V19 V42
## 
## [[664]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V13 V14 V19
## 
## [[665]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V13 V19
## 
## [[666]]
## + 3/56 vertices, named, from a229d17:
## [1] V13 V19 V28
## 
## [[667]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V13 V19 V28
## 
## [[668]]
## + 3/56 vertices, named, from a229d17:
## [1] V13 V19 V26
## 
## [[669]]
## + 4/56 vertices, named, from a229d17:
## [1] V13 V14 V19 V26
## 
## [[670]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V13 V19 V26
## 
## [[671]]
## + 3/56 vertices, named, from a229d17:
## [1] V13 V19 V42
## 
## [[672]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V13 V19 V42
## 
## [[673]]
## + 3/56 vertices, named, from a229d17:
## [1] V13 V14 V19
## 
## [[674]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V13 V19
## 
## [[675]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V33
## 
## [[676]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V30 V33
## 
## [[677]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V26 V30 V33
## 
## [[678]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V30 V33 V42
## 
## [[679]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V26 V33
## 
## [[680]]
## + 4/56 vertices, named, from a229d17:
## [1] V19 V20 V26 V33
## 
## [[681]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V33 V42
## 
## [[682]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V20 V33
## 
## [[683]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V19
## 
## [[684]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V19
## 
## [[685]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V19 V42
## 
## [[686]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V19 V42
## 
## [[687]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V19 V42
## 
## [[688]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V19
## 
## [[689]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V10 V19
## 
## [[690]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V19 V42
## 
## [[691]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V19 V42
## 
## [[692]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V19 V42
## 
## [[693]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V19
## 
## [[694]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V19
## 
## [[695]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V34
## 
## [[696]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V19 V34
## 
## [[697]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V19 V26 V34
## 
## [[698]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V19 V26 V34
## 
## [[699]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V19 V26 V34
## 
## [[700]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V19 V34 V42
## 
## [[701]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V19 V34 V42
## 
## [[702]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V19 V34 V42
## 
## [[703]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V19 V34
## 
## [[704]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V19 V34
## 
## [[705]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V26 V34
## 
## [[706]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V19 V26 V34
## 
## [[707]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V26 V34
## 
## [[708]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V34 V42
## 
## [[709]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V19 V34 V42
## 
## [[710]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V34 V42
## 
## [[711]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V19 V34
## 
## [[712]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V34
## 
## [[713]]
## + 2/56 vertices, named, from a229d17:
## [1] V17 V19
## 
## [[714]]
## + 3/56 vertices, named, from a229d17:
## [1] V17 V19 V26
## 
## [[715]]
## + 4/56 vertices, named, from a229d17:
## [1] V17 V19 V20 V26
## 
## [[716]]
## + 4/56 vertices, named, from a229d17:
## [1] V14 V17 V19 V26
## 
## [[717]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V17 V19 V26
## 
## [[718]]
## + 3/56 vertices, named, from a229d17:
## [1] V17 V19 V20
## 
## [[719]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V17 V19
## 
## [[720]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V17 V19
## 
## [[721]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V29
## 
## [[722]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V29 V53
## 
## [[723]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V29 V42
## 
## [[724]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V19 V29 V42
## 
## [[725]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V19 V29
## 
## [[726]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V30
## 
## [[727]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V26 V30
## 
## [[728]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V26 V30
## 
## [[729]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V30 V42
## 
## [[730]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V19 V30 V42
## 
## [[731]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V30
## 
## [[732]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V19
## 
## [[733]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V19 V26
## 
## [[734]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V14 V19 V26
## 
## [[735]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V19 V26
## 
## [[736]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V19 V26
## 
## [[737]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V19 V42
## 
## [[738]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V19 V42
## 
## [[739]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V19 V42
## 
## [[740]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V19
## 
## [[741]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V19
## 
## [[742]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V19
## 
## [[743]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V53
## 
## [[744]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V20 V53
## 
## [[745]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V28
## 
## [[746]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V28
## 
## [[747]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V26
## 
## [[748]]
## + 3/56 vertices, named, from a229d17:
## [1] V19 V20 V26
## 
## [[749]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V19 V26
## 
## [[750]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V19 V26
## 
## [[751]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V26
## 
## [[752]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V42
## 
## [[753]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V19 V42
## 
## [[754]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V19 V42
## 
## [[755]]
## + 2/56 vertices, named, from a229d17:
## [1] V19 V20
## 
## [[756]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V19
## 
## [[757]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V19
## 
## [[758]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V19
## 
## [[759]]
## + 1/56 vertex, named, from a229d17:
## [1] V23
## 
## [[760]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V37
## 
## [[761]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V34 V37
## 
## [[762]]
## + 4/56 vertices, named, from a229d17:
## [1] V23 V34 V37 V47
## 
## [[763]]
## + 5/56 vertices, named, from a229d17:
## [1] V23 V34 V37 V42 V47
## 
## [[764]]
## + 4/56 vertices, named, from a229d17:
## [1] V23 V34 V37 V42
## 
## [[765]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V37 V47
## 
## [[766]]
## + 4/56 vertices, named, from a229d17:
## [1] V23 V37 V42 V47
## 
## [[767]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V37 V38
## 
## [[768]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V37 V42
## 
## [[769]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V23
## 
## [[770]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V9  V23
## 
## [[771]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V9  V23 V42
## 
## [[772]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V23 V42
## 
## [[773]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V23
## 
## [[774]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V23 V34
## 
## [[775]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V23 V34 V47
## 
## [[776]]
## + 5/56 vertices, named, from a229d17:
## [1] V9  V23 V34 V42 V47
## 
## [[777]]
## + 6/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V34 V42 V47
## 
## [[778]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V34 V47
## 
## [[779]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V23 V26 V34
## 
## [[780]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V26 V34
## 
## [[781]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V23 V34 V42
## 
## [[782]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V34 V42
## 
## [[783]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V34
## 
## [[784]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V23 V47
## 
## [[785]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V23 V42 V47
## 
## [[786]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V42 V47
## 
## [[787]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V47
## 
## [[788]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V23 V38
## 
## [[789]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V38
## 
## [[790]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V23 V26
## 
## [[791]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V26
## 
## [[792]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V23 V42
## 
## [[793]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V23 V42
## 
## [[794]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V23
## 
## [[795]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V23
## 
## [[796]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V23 V42
## 
## [[797]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V23 V42
## 
## [[798]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V23
## 
## [[799]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V34
## 
## [[800]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V34 V47
## 
## [[801]]
## + 4/56 vertices, named, from a229d17:
## [1] V23 V34 V42 V47
## 
## [[802]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V23 V34 V42 V47
## 
## [[803]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V23 V34 V47
## 
## [[804]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V26 V34
## 
## [[805]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V23 V26 V34
## 
## [[806]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V34 V42
## 
## [[807]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V23 V34 V42
## 
## [[808]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V34
## 
## [[809]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V40
## 
## [[810]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V40
## 
## [[811]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V47
## 
## [[812]]
## + 3/56 vertices, named, from a229d17:
## [1] V23 V42 V47
## 
## [[813]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V23 V42 V47
## 
## [[814]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V47
## 
## [[815]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V38
## 
## [[816]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V38
## 
## [[817]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V26
## 
## [[818]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V26
## 
## [[819]]
## + 2/56 vertices, named, from a229d17:
## [1] V23 V42
## 
## [[820]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V23 V42
## 
## [[821]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V23
## 
## [[822]]
## + 1/56 vertex, named, from a229d17:
## [1] V25
## 
## [[823]]
## + 2/56 vertices, named, from a229d17:
## [1] V18 V25
## 
## [[824]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V37
## 
## [[825]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V34 V37
## 
## [[826]]
## + 5/56 vertices, named, from a229d17:
## [1] V18 V25 V34 V37 V47
## 
## [[827]]
## + 5/56 vertices, named, from a229d17:
## [1] V18 V25 V34 V37 V46
## 
## [[828]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V37 V47
## 
## [[829]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V37 V46
## 
## [[830]]
## + 4/56 vertices, named, from a229d17:
## [1] V14 V18 V25 V37
## 
## [[831]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V31
## 
## [[832]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V31 V34
## 
## [[833]]
## + 4/56 vertices, named, from a229d17:
## [1] V14 V18 V25 V31
## 
## [[834]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V34
## 
## [[835]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V34 V47
## 
## [[836]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V34 V47
## 
## [[837]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V34 V46
## 
## [[838]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V34 V46
## 
## [[839]]
## + 4/56 vertices, named, from a229d17:
## [1] V18 V25 V26 V34
## 
## [[840]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V26 V34
## 
## [[841]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V34
## 
## [[842]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V47
## 
## [[843]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V47
## 
## [[844]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V46
## 
## [[845]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V46
## 
## [[846]]
## + 3/56 vertices, named, from a229d17:
## [1] V18 V25 V26
## 
## [[847]]
## + 4/56 vertices, named, from a229d17:
## [1] V14 V18 V25 V26
## 
## [[848]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V18 V25 V26
## 
## [[849]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V18 V25
## 
## [[850]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V18 V25
## 
## [[851]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V37
## 
## [[852]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V34 V37
## 
## [[853]]
## + 4/56 vertices, named, from a229d17:
## [1] V25 V34 V37 V47
## 
## [[854]]
## + 4/56 vertices, named, from a229d17:
## [1] V25 V34 V37 V46
## 
## [[855]]
## + 4/56 vertices, named, from a229d17:
## [1] V25 V34 V37 V44
## 
## [[856]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V37 V47
## 
## [[857]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V37 V46
## 
## [[858]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V37 V44
## 
## [[859]]
## + 4/56 vertices, named, from a229d17:
## [1] V14 V25 V37 V44
## 
## [[860]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V25 V37
## 
## [[861]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V31
## 
## [[862]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V31 V34
## 
## [[863]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V25 V31 V34
## 
## [[864]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V25 V31
## 
## [[865]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V14 V25 V31
## 
## [[866]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V25 V31
## 
## [[867]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V33
## 
## [[868]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V26 V33
## 
## [[869]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V34
## 
## [[870]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V34 V47
## 
## [[871]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V25 V34 V47
## 
## [[872]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V25 V34 V47
## 
## [[873]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V25 V34
## 
## [[874]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V25 V34 V44
## 
## [[875]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V25 V34 V44
## 
## [[876]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V25 V34 V44
## 
## [[877]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V25 V26 V34
## 
## [[878]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V25 V26 V34
## 
## [[879]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V10 V25 V26 V34
## 
## [[880]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V25 V34
## 
## [[881]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V25 V34
## 
## [[882]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V34 V46
## 
## [[883]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V25 V34 V46
## 
## [[884]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V34 V44
## 
## [[885]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V25 V34 V44
## 
## [[886]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V25 V34 V44
## 
## [[887]]
## + 3/56 vertices, named, from a229d17:
## [1] V25 V26 V34
## 
## [[888]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V25 V26 V34
## 
## [[889]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V25 V26 V34
## 
## [[890]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V25 V34
## 
## [[891]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V25 V34
## 
## [[892]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V47
## 
## [[893]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V25 V47
## 
## [[894]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V25 V47
## 
## [[895]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V25
## 
## [[896]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V25 V44
## 
## [[897]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V14 V25 V44
## 
## [[898]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V25 V44
## 
## [[899]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V25 V44
## 
## [[900]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V25 V26
## 
## [[901]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V14 V25 V26
## 
## [[902]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V25 V26
## 
## [[903]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V10 V25 V26
## 
## [[904]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V14 V25
## 
## [[905]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V25
## 
## [[906]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V10 V25
## 
## [[907]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V46
## 
## [[908]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V25 V46
## 
## [[909]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V44
## 
## [[910]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V25 V44
## 
## [[911]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V25 V44
## 
## [[912]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V25 V44
## 
## [[913]]
## + 2/56 vertices, named, from a229d17:
## [1] V25 V26
## 
## [[914]]
## + 3/56 vertices, named, from a229d17:
## [1] V14 V25 V26
## 
## [[915]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V25 V26
## 
## [[916]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V25 V26
## 
## [[917]]
## + 2/56 vertices, named, from a229d17:
## [1] V14 V25
## 
## [[918]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V25
## 
## [[919]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V25
## 
## [[920]]
## + 1/56 vertex, named, from a229d17:
## [1] V21
## 
## [[921]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V24
## 
## [[922]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V24
## 
## [[923]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V9  V21 V24
## 
## [[924]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V9  V21 V24 V42
## 
## [[925]]
## + 6/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V21 V24 V42
## 
## [[926]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V21 V24
## 
## [[927]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V24 V47
## 
## [[928]]
## + 5/56 vertices, named, from a229d17:
## [1] V9  V21 V24 V42 V47
## 
## [[929]]
## + 6/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V24 V42 V47
## 
## [[930]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V24 V47
## 
## [[931]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V24 V38
## 
## [[932]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V24 V38
## 
## [[933]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V24 V42
## 
## [[934]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V24 V42
## 
## [[935]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V24
## 
## [[936]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V21 V24
## 
## [[937]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V21 V24 V42
## 
## [[938]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V21 V24 V42
## 
## [[939]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V10 V21 V24 V42
## 
## [[940]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V21 V24
## 
## [[941]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V21 V24
## 
## [[942]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V24 V47
## 
## [[943]]
## + 4/56 vertices, named, from a229d17:
## [1] V21 V24 V42 V47
## 
## [[944]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V21 V24 V42 V47
## 
## [[945]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V21 V24 V42 V47
## 
## [[946]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V24 V47
## 
## [[947]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V24 V47
## 
## [[948]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V24 V38
## 
## [[949]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V24 V38
## 
## [[950]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V24 V38
## 
## [[951]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V24 V42
## 
## [[952]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V24 V42
## 
## [[953]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V24 V42
## 
## [[954]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V24
## 
## [[955]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V24
## 
## [[956]]
## + 2/56 vertices, named, from a229d17:
## [1] V9  V21
## 
## [[957]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V9  V21
## 
## [[958]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V9  V21 V42
## 
## [[959]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V21 V42
## 
## [[960]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V9  V21
## 
## [[961]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V34
## 
## [[962]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V34 V47
## 
## [[963]]
## + 5/56 vertices, named, from a229d17:
## [1] V9  V21 V34 V42 V47
## 
## [[964]]
## + 6/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V34 V42 V47
## 
## [[965]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V34 V47
## 
## [[966]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V26 V34
## 
## [[967]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V26 V34
## 
## [[968]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V34 V42
## 
## [[969]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V34 V42
## 
## [[970]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V34
## 
## [[971]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V47
## 
## [[972]]
## + 4/56 vertices, named, from a229d17:
## [1] V9  V21 V42 V47
## 
## [[973]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V42 V47
## 
## [[974]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V47
## 
## [[975]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V38
## 
## [[976]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V38
## 
## [[977]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V26
## 
## [[978]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V26
## 
## [[979]]
## + 3/56 vertices, named, from a229d17:
## [1] V9  V21 V42
## 
## [[980]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V9  V21 V42
## 
## [[981]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V9  V21
## 
## [[982]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V21
## 
## [[983]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V21 V32
## 
## [[984]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V21 V42
## 
## [[985]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V21 V42
## 
## [[986]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V10 V21 V42
## 
## [[987]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V16 V21
## 
## [[988]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V21
## 
## [[989]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V10 V21
## 
## [[990]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V34
## 
## [[991]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V34 V47
## 
## [[992]]
## + 4/56 vertices, named, from a229d17:
## [1] V21 V34 V42 V47
## 
## [[993]]
## + 5/56 vertices, named, from a229d17:
## [1] V8  V21 V34 V42 V47
## 
## [[994]]
## + 5/56 vertices, named, from a229d17:
## [1] V10 V21 V34 V42 V47
## 
## [[995]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V34 V47
## 
## [[996]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V34 V47
## 
## [[997]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V26 V34
## 
## [[998]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V26 V34
## 
## [[999]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V26 V34
## 
## [[1000]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V34 V42
## 
## [[1001]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V34 V42
## 
## [[1002]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V34 V42
## 
## [[1003]]
## + 3/56 vertices, named, from a229d17:
## [1] V16 V21 V34
## 
## [[1004]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V34
## 
## [[1005]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V34
## 
## [[1006]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V32
## 
## [[1007]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V47
## 
## [[1008]]
## + 3/56 vertices, named, from a229d17:
## [1] V21 V42 V47
## 
## [[1009]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V21 V42 V47
## 
## [[1010]]
## + 4/56 vertices, named, from a229d17:
## [1] V10 V21 V42 V47
## 
## [[1011]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V47
## 
## [[1012]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V47
## 
## [[1013]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V48
## 
## [[1014]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V48
## 
## [[1015]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V38
## 
## [[1016]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V38
## 
## [[1017]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V38
## 
## [[1018]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V26
## 
## [[1019]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V26
## 
## [[1020]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V26
## 
## [[1021]]
## + 2/56 vertices, named, from a229d17:
## [1] V21 V42
## 
## [[1022]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V21 V42
## 
## [[1023]]
## + 3/56 vertices, named, from a229d17:
## [1] V10 V21 V42
## 
## [[1024]]
## + 2/56 vertices, named, from a229d17:
## [1] V16 V21
## 
## [[1025]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V21
## 
## [[1026]]
## + 2/56 vertices, named, from a229d17:
## [1] V10 V21
## 
## [[1027]]
## + 1/56 vertex, named, from a229d17:
## [1] V12
## 
## [[1028]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V19
## 
## [[1029]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V12 V19
## 
## [[1030]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V12 V19
## 
## [[1031]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V12 V19 V42
## 
## [[1032]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12 V19 V42
## 
## [[1033]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12 V19
## 
## [[1034]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V12 V19 V42
## 
## [[1035]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V12 V19 V42
## 
## [[1036]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V12 V19
## 
## [[1037]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V19 V30
## 
## [[1038]]
## + 4/56 vertices, named, from a229d17:
## [1] V12 V19 V30 V42
## 
## [[1039]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V12 V19
## 
## [[1040]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V12 V19 V42
## 
## [[1041]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V12 V19 V42
## 
## [[1042]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V12 V19
## 
## [[1043]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V19 V28
## 
## [[1044]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V19 V42
## 
## [[1045]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V12 V19 V42
## 
## [[1046]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V12 V19
## 
## [[1047]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V15
## 
## [[1048]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V12 V15
## 
## [[1049]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V12 V15
## 
## [[1050]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V12 V15 V42
## 
## [[1051]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12 V15 V42
## 
## [[1052]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12 V15
## 
## [[1053]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V12 V15 V42
## 
## [[1054]]
## + 5/56 vertices, named, from a229d17:
## [1] V4  V8  V12 V15 V42
## 
## [[1055]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V12 V15 V16
## 
## [[1056]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V12 V15
## 
## [[1057]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V15 V30
## 
## [[1058]]
## + 4/56 vertices, named, from a229d17:
## [1] V12 V15 V30 V42
## 
## [[1059]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V12 V15
## 
## [[1060]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V12 V15 V42
## 
## [[1061]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V8  V12 V15 V42
## 
## [[1062]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V12 V15
## 
## [[1063]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V15 V42
## 
## [[1064]]
## + 4/56 vertices, named, from a229d17:
## [1] V8  V12 V15 V42
## 
## [[1065]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V15 V16
## 
## [[1066]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V12 V15
## 
## [[1067]]
## + 2/56 vertices, named, from a229d17:
## [1] V4  V12
## 
## [[1068]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V4  V12
## 
## [[1069]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V12 V42
## 
## [[1070]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12 V42
## 
## [[1071]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V4  V8  V12
## 
## [[1072]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V12 V42
## 
## [[1073]]
## + 4/56 vertices, named, from a229d17:
## [1] V4  V8  V12 V42
## 
## [[1074]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V12 V16
## 
## [[1075]]
## + 3/56 vertices, named, from a229d17:
## [1] V4  V8  V12
## 
## [[1076]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V30
## 
## [[1077]]
## + 3/56 vertices, named, from a229d17:
## [1] V12 V30 V42
## 
## [[1078]]
## + 2/56 vertices, named, from a229d17:
## [1] V11 V12
## 
## [[1079]]
## + 3/56 vertices, named, from a229d17:
## [1] V11 V12 V16
## 
## [[1080]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V11 V12
## 
## [[1081]]
## + 2/56 vertices, named, from a229d17:
## [1] V3  V12
## 
## [[1082]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V12 V42
## 
## [[1083]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V8  V12 V42
## 
## [[1084]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V8  V12
## 
## [[1085]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V28
## 
## [[1086]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V42
## 
## [[1087]]
## + 3/56 vertices, named, from a229d17:
## [1] V8  V12 V42
## 
## [[1088]]
## + 2/56 vertices, named, from a229d17:
## [1] V12 V16
## 
## [[1089]]
## + 2/56 vertices, named, from a229d17:
## [1] V8  V12
## 
## [[1090]]
## + 1/56 vertex, named, from a229d17:
## [1] V5
## 
## [[1091]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V18
## 
## [[1092]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V18 V31
## 
## [[1093]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V18 V31 V34
## 
## [[1094]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V11 V18 V31 V34
## 
## [[1095]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V18 V31
## 
## [[1096]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V14 V18 V31
## 
## [[1097]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V18 V34
## 
## [[1098]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V18 V34
## 
## [[1099]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V11 V18 V26 V34
## 
## [[1100]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V11 V16 V18 V34
## 
## [[1101]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V18 V34 V47
## 
## [[1102]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V34 V47
## 
## [[1103]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V18 V34 V46
## 
## [[1104]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V34 V46
## 
## [[1105]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V18 V26 V34
## 
## [[1106]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V26 V34
## 
## [[1107]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V16 V18 V34
## 
## [[1108]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V34
## 
## [[1109]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V11 V18
## 
## [[1110]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V18 V26
## 
## [[1111]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V16 V18
## 
## [[1112]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V18 V47
## 
## [[1113]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V47
## 
## [[1114]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V18 V46
## 
## [[1115]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V46
## 
## [[1116]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V18 V26
## 
## [[1117]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V14 V18 V26
## 
## [[1118]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V18 V26
## 
## [[1119]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V16 V18
## 
## [[1120]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V14 V18
## 
## [[1121]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V18
## 
## [[1122]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V31
## 
## [[1123]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V31 V34
## 
## [[1124]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V31 V34
## 
## [[1125]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V31 V34
## 
## [[1126]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V11 V31
## 
## [[1127]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V31
## 
## [[1128]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V14 V31
## 
## [[1129]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V14 V31
## 
## [[1130]]
## + 2/56 vertices, named, from a229d17:
## [1] V5 V9
## 
## [[1131]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V9  V34
## 
## [[1132]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V9  V34 V47
## 
## [[1133]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V8  V9  V34 V47
## 
## [[1134]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V9  V34
## 
## [[1135]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V9  V26 V34
## 
## [[1136]]
## + 6/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V9  V26 V34
## 
## [[1137]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V9  V34
## 
## [[1138]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V9  V26 V34
## 
## [[1139]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V8  V9  V26 V34
## 
## [[1140]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V9  V34
## 
## [[1141]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V9  V47
## 
## [[1142]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V9  V47
## 
## [[1143]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V5 V9
## 
## [[1144]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V9  V26
## 
## [[1145]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V9  V26
## 
## [[1146]]
## + 4/56 vertices, named, from a229d17:
## [1] V3 V5 V8 V9
## 
## [[1147]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V9  V26
## 
## [[1148]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V9  V26
## 
## [[1149]]
## + 3/56 vertices, named, from a229d17:
## [1] V5 V8 V9
## 
## [[1150]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V13
## 
## [[1151]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V13
## 
## [[1152]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V13 V26
## 
## [[1153]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V13 V14 V26
## 
## [[1154]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V13 V26
## 
## [[1155]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V13 V14
## 
## [[1156]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V13
## 
## [[1157]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V13 V26
## 
## [[1158]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V13 V14 V26
## 
## [[1159]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V13 V26
## 
## [[1160]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V13 V14
## 
## [[1161]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V13
## 
## [[1162]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V34
## 
## [[1163]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V11 V34
## 
## [[1164]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V26 V34
## 
## [[1165]]
## + 5/56 vertices, named, from a229d17:
## [1] V5  V8  V11 V26 V34
## 
## [[1166]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V11 V16 V34
## 
## [[1167]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V11 V34
## 
## [[1168]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V34 V47
## 
## [[1169]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V34 V47
## 
## [[1170]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V34 V47
## 
## [[1171]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V34
## 
## [[1172]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V34 V44
## 
## [[1173]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V34 V44
## 
## [[1174]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V34 V44
## 
## [[1175]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V26 V34
## 
## [[1176]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V26 V34
## 
## [[1177]]
## + 5/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V26 V34
## 
## [[1178]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V34
## 
## [[1179]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V34
## 
## [[1180]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V34 V46
## 
## [[1181]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V34 V46
## 
## [[1182]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V34 V44
## 
## [[1183]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V34 V44
## 
## [[1184]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V34 V44
## 
## [[1185]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V26 V34
## 
## [[1186]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V26 V34
## 
## [[1187]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V10 V26 V34
## 
## [[1188]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V16 V34
## 
## [[1189]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V8  V34
## 
## [[1190]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V34
## 
## [[1191]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V11
## 
## [[1192]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V11 V26
## 
## [[1193]]
## + 4/56 vertices, named, from a229d17:
## [1] V5  V8  V11 V26
## 
## [[1194]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V11 V16
## 
## [[1195]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V8  V11
## 
## [[1196]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V47
## 
## [[1197]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V8  V47
## 
## [[1198]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V47
## 
## [[1199]]
## + 2/56 vertices, named, from a229d17:
## [1] V3 V5
## 
## [[1200]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V44
## 
## [[1201]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V14 V44
## 
## [[1202]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V44
## 
## [[1203]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V44
## 
## [[1204]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V26
## 
## [[1205]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V14 V26
## 
## [[1206]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V8  V26
## 
## [[1207]]
## + 4/56 vertices, named, from a229d17:
## [1] V3  V5  V10 V26
## 
## [[1208]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V14
## 
## [[1209]]
## + 3/56 vertices, named, from a229d17:
## [1] V3 V5 V8
## 
## [[1210]]
## + 3/56 vertices, named, from a229d17:
## [1] V3  V5  V10
## 
## [[1211]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V46
## 
## [[1212]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V46
## 
## [[1213]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V44
## 
## [[1214]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V14 V44
## 
## [[1215]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V8  V44
## 
## [[1216]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V44
## 
## [[1217]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V26
## 
## [[1218]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V14 V26
## 
## [[1219]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V8  V26
## 
## [[1220]]
## + 3/56 vertices, named, from a229d17:
## [1] V5  V10 V26
## 
## [[1221]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V16
## 
## [[1222]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V14
## 
## [[1223]]
## + 2/56 vertices, named, from a229d17:
## [1] V5 V8
## 
## [[1224]]
## + 2/56 vertices, named, from a229d17:
## [1] V5  V10

largest.cliques(as.undirected(net1))

## [[1]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V47 V9  V34 V42 V23
## 
## [[2]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V47 V9  V34 V42 V21
## 
## [[3]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V47 V9  V24 V21 V42
## 
## [[4]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V21 V9  V42 V4  V24
## 
## [[5]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V34 V9  V19 V42
## 
## [[6]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V34 V9  V19 V26
## 
## [[7]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V34 V9  V5  V26
## 
## [[8]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V4  V42 V15 V24
## 
## [[9]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V4  V42 V15 V12
## 
## [[10]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V4  V42 V12 V19
## 
## [[11]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V4  V42 V9  V24
## 
## [[12]]
## + 6/56 vertices, named, from a28325e:
## [1] V8  V3  V4  V42 V9  V19

max_cliques(as.undirected(net1))

## [[1]]
## + 1/56 vertex, named, from a28410b:
## [1] V56
## 
## [[2]]
## + 2/56 vertices, named, from a28410b:
## [1] V7 V4
## 
## [[3]]
## + 2/56 vertices, named, from a28410b:
## [1] V54 V14
## 
## [[4]]
## + 3/56 vertices, named, from a28410b:
## [1] V36 V3  V10
## 
## [[5]]
## + 3/56 vertices, named, from a28410b:
## [1] V48 V8  V21
## 
## [[6]]
## + 2/56 vertices, named, from a28410b:
## [1] V50 V10
## 
## [[7]]
## + 2/56 vertices, named, from a28410b:
## [1] V50 V8 
## 
## [[8]]
## + 2/56 vertices, named, from a28410b:
## [1] V45 V33
## 
## [[9]]
## + 2/56 vertices, named, from a28410b:
## [1] V45 V4 
## 
## [[10]]
## + 4/56 vertices, named, from a28410b:
## [1] V43 V3  V10 V6 
## 
## [[11]]
## + 2/56 vertices, named, from a28410b:
## [1] V27 V28
## 
## [[12]]
## + 3/56 vertices, named, from a28410b:
## [1] V27 V14 V37
## 
## [[13]]
## + 2/56 vertices, named, from a28410b:
## [1] V52 V20
## 
## [[14]]
## + 2/56 vertices, named, from a28410b:
## [1] V52 V11
## 
## [[15]]
## + 2/56 vertices, named, from a28410b:
## [1] V52 V10
## 
## [[16]]
## + 2/56 vertices, named, from a28410b:
## [1] V55 V24
## 
## [[17]]
## + 3/56 vertices, named, from a28410b:
## [1] V55 V18 V31
## 
## [[18]]
## + 2/56 vertices, named, from a28410b:
## [1] V32 V44
## 
## [[19]]
## + 2/56 vertices, named, from a28410b:
## [1] V32 V39
## 
## [[20]]
## + 3/56 vertices, named, from a28410b:
## [1] V32 V4  V21
## 
## [[21]]
## + 3/56 vertices, named, from a28410b:
## [1] V39 V30 V35
## 
## [[22]]
## + 2/56 vertices, named, from a28410b:
## [1] V39 V1 
## 
## [[23]]
## + 3/56 vertices, named, from a28410b:
## [1] V29 V53 V19
## 
## [[24]]
## + 3/56 vertices, named, from a28410b:
## [1] V29 V8  V6 
## 
## [[25]]
## + 4/56 vertices, named, from a28410b:
## [1] V29 V8  V42 V19
## 
## [[26]]
## + 4/56 vertices, named, from a28410b:
## [1] V29 V8  V42 V49
## 
## [[27]]
## + 4/56 vertices, named, from a28410b:
## [1] V28 V19 V10 V13
## 
## [[28]]
## + 4/56 vertices, named, from a28410b:
## [1] V28 V19 V10 V35
## 
## [[29]]
## + 3/56 vertices, named, from a28410b:
## [1] V28 V19 V12
## 
## [[30]]
## + 3/56 vertices, named, from a28410b:
## [1] V53 V20 V19
## 
## [[31]]
## + 3/56 vertices, named, from a28410b:
## [1] V53 V11 V18
## 
## [[32]]
## + 4/56 vertices, named, from a28410b:
## [1] V1  V38 V24 V10
## 
## [[33]]
## + 3/56 vertices, named, from a28410b:
## [1] V1  V6  V10
## 
## [[34]]
## + 3/56 vertices, named, from a28410b:
## [1] V1  V6  V20
## 
## [[35]]
## + 3/56 vertices, named, from a28410b:
## [1] V1  V6  V40
## 
## [[36]]
## + 3/56 vertices, named, from a28410b:
## [1] V41 V16 V11
## 
## [[37]]
## + 3/56 vertices, named, from a28410b:
## [1] V41 V3  V14
## 
## [[38]]
## + 3/56 vertices, named, from a28410b:
## [1] V41 V3  V42
## 
## [[39]]
## + 3/56 vertices, named, from a28410b:
## [1] V40 V23 V8 
## 
## [[40]]
## + 3/56 vertices, named, from a28410b:
## [1] V40 V6  V2 
## 
## [[41]]
## + 3/56 vertices, named, from a28410b:
## [1] V40 V6  V8 
## 
## [[42]]
## + 4/56 vertices, named, from a28410b:
## [1] V20 V26 V2  V35
## 
## [[43]]
## + 4/56 vertices, named, from a28410b:
## [1] V20 V26 V19 V17
## 
## [[44]]
## + 4/56 vertices, named, from a28410b:
## [1] V20 V26 V19 V33
## 
## [[45]]
## + 4/56 vertices, named, from a28410b:
## [1] V20 V26 V19 V35
## 
## [[46]]
## + 3/56 vertices, named, from a28410b:
## [1] V20 V6  V2 
## 
## [[47]]
## + 4/56 vertices, named, from a28410b:
## [1] V17 V14 V26 V19
## 
## [[48]]
## + 4/56 vertices, named, from a28410b:
## [1] V17 V8  V26 V19
## 
## [[49]]
## + 3/56 vertices, named, from a28410b:
## [1] V17 V8  V38
## 
## [[50]]
## + 5/56 vertices, named, from a28410b:
## [1] V46 V18 V34 V5  V10
## 
## [[51]]
## + 5/56 vertices, named, from a28410b:
## [1] V46 V18 V34 V25 V10
## 
## [[52]]
## + 5/56 vertices, named, from a28410b:
## [1] V46 V18 V34 V25 V37
## 
## [[53]]
## + 4/56 vertices, named, from a28410b:
## [1] V2  V10 V6  V3 
## 
## [[54]]
## + 4/56 vertices, named, from a28410b:
## [1] V2  V10 V26 V3 
## 
## [[55]]
## + 4/56 vertices, named, from a28410b:
## [1] V2  V10 V26 V35
## 
## [[56]]
## + 3/56 vertices, named, from a28410b:
## [1] V33 V25 V26
## 
## [[57]]
## + 4/56 vertices, named, from a28410b:
## [1] V33 V19 V30 V26
## 
## [[58]]
## + 4/56 vertices, named, from a28410b:
## [1] V33 V19 V30 V42
## 
## [[59]]
## + 4/56 vertices, named, from a28410b:
## [1] V6 V3 V4 V8
## 
## [[60]]
## + 4/56 vertices, named, from a28410b:
## [1] V6  V3  V4  V10
## 
## [[61]]
## + 4/56 vertices, named, from a28410b:
## [1] V6  V3  V34 V8 
## 
## [[62]]
## + 4/56 vertices, named, from a28410b:
## [1] V6  V3  V34 V10
## 
## [[63]]
## + 4/56 vertices, named, from a28410b:
## [1] V49 V30 V42 V24
## 
## [[64]]
## + 3/56 vertices, named, from a28410b:
## [1] V49 V16 V11
## 
## [[65]]
## + 3/56 vertices, named, from a28410b:
## [1] V49 V16 V22
## 
## [[66]]
## + 3/56 vertices, named, from a28410b:
## [1] V49 V8  V11
## 
## [[67]]
## + 4/56 vertices, named, from a28410b:
## [1] V49 V8  V24 V42
## 
## [[68]]
## + 4/56 vertices, named, from a28410b:
## [1] V49 V8  V24 V22
## 
## [[69]]
## + 5/56 vertices, named, from a28410b:
## [1] V13 V3  V26 V5  V10
## 
## [[70]]
## + 5/56 vertices, named, from a28410b:
## [1] V13 V3  V26 V5  V14
## 
## [[71]]
## + 5/56 vertices, named, from a28410b:
## [1] V13 V3  V26 V19 V10
## 
## [[72]]
## + 5/56 vertices, named, from a28410b:
## [1] V13 V3  V26 V19 V14
## 
## [[73]]
## + 5/56 vertices, named, from a28410b:
## [1] V13 V3  V42 V19 V10
## 
## [[74]]
## + 3/56 vertices, named, from a28410b:
## [1] V22 V4  V16
## 
## [[75]]
## + 5/56 vertices, named, from a28410b:
## [1] V22 V4  V24 V3  V8 
## 
## [[76]]
## + 5/56 vertices, named, from a28410b:
## [1] V22 V4  V24 V3  V10
## 
## [[77]]
## + 4/56 vertices, named, from a28410b:
## [1] V35 V10 V4  V19
## 
## [[78]]
## + 5/56 vertices, named, from a28410b:
## [1] V35 V10 V26 V30 V19
## 
## [[79]]
## + 4/56 vertices, named, from a28410b:
## [1] V35 V10 V26 V51
## 
## [[80]]
## + 4/56 vertices, named, from a28410b:
## [1] V35 V8  V4  V19
## 
## [[81]]
## + 4/56 vertices, named, from a28410b:
## [1] V35 V8  V26 V19
## 
## [[82]]
## + 4/56 vertices, named, from a28410b:
## [1] V35 V8  V26 V51
## 
## [[83]]
## + 5/56 vertices, named, from a28410b:
## [1] V51 V34 V26 V8  V9 
## 
## [[84]]
## + 5/56 vertices, named, from a28410b:
## [1] V51 V34 V26 V18 V10
## 
## [[85]]
## + 5/56 vertices, named, from a28410b:
## [1] V51 V34 V42 V9  V8 
## 
## [[86]]
## + 4/56 vertices, named, from a28410b:
## [1] V51 V34 V42 V10
## 
## [[87]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V15 V42 V12
## 
## [[88]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V15 V42 V24
## 
## [[89]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V12 V19 V42
## 
## [[90]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V10 V38 V24
## 
## [[91]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V10 V42 V19
## 
## [[92]]
## + 4/56 vertices, named, from a28410b:
## [1] V30 V10 V42 V24
## 
## [[93]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V3  V26 V8 
## 
## [[94]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V3  V26 V10
## 
## [[95]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V34 V3  V31
## 
## [[96]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V3  V44 V8 
## 
## [[97]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V3  V44 V10
## 
## [[98]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V34 V8  V47
## 
## [[99]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V18 V26 V10
## 
## [[100]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V34 V18 V31
## 
## [[101]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V18 V47 V10
## 
## [[102]]
## + 5/56 vertices, named, from a28410b:
## [1] V25 V34 V18 V47 V37
## 
## [[103]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V34 V44 V37
## 
## [[104]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V3  V44
## 
## [[105]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V3  V31
## 
## [[106]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V3  V26
## 
## [[107]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V18 V26
## 
## [[108]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V18 V31
## 
## [[109]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V18 V37
## 
## [[110]]
## + 4/56 vertices, named, from a28410b:
## [1] V25 V14 V44 V37
## 
## [[111]]
## + 4/56 vertices, named, from a28410b:
## [1] V24 V10 V38 V21
## 
## [[112]]
## + 5/56 vertices, named, from a28410b:
## [1] V24 V10 V42 V3  V4 
## 
## [[113]]
## + 5/56 vertices, named, from a28410b:
## [1] V24 V10 V42 V21 V4 
## 
## [[114]]
## + 5/56 vertices, named, from a28410b:
## [1] V24 V10 V42 V21 V47
## 
## [[115]]
## + 5/56 vertices, named, from a28410b:
## [1] V24 V8  V38 V9  V21
## 
## [[116]]
## + 6/56 vertices, named, from a28410b:
## [1] V24 V8  V42 V15 V4  V3 
## 
## [[117]]
## + 6/56 vertices, named, from a28410b:
## [1] V24 V8  V42 V9  V4  V3 
## 
## [[118]]
## + 6/56 vertices, named, from a28410b:
## [1] V24 V8  V42 V9  V4  V21
## 
## [[119]]
## + 6/56 vertices, named, from a28410b:
## [1] V24 V8  V42 V9  V47 V21
## 
## [[120]]
## + 6/56 vertices, named, from a28410b:
## [1] V34 V42 V3  V19 V9  V8 
## 
## [[121]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V42 V3  V19 V10
## 
## [[122]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V42 V3  V19 V31
## 
## [[123]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V42 V47 V10 V21
## 
## [[124]]
## + 6/56 vertices, named, from a28410b:
## [1] V34 V42 V47 V21 V9  V8 
## 
## [[125]]
## + 6/56 vertices, named, from a28410b:
## [1] V34 V42 V47 V23 V9  V8 
## 
## [[126]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V42 V47 V23 V37
## 
## [[127]]
## + 4/56 vertices, named, from a28410b:
## [1] V34 V37 V11 V18
## 
## [[128]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V23 V8  V9  V26
## 
## [[129]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V21 V26 V8  V9 
## 
## [[130]]
## + 4/56 vertices, named, from a28410b:
## [1] V34 V21 V26 V10
## 
## [[131]]
## + 3/56 vertices, named, from a28410b:
## [1] V34 V21 V16
## 
## [[132]]
## + 6/56 vertices, named, from a28410b:
## [1] V34 V19 V3  V26 V8  V9 
## 
## [[133]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V19 V3  V26 V10
## 
## [[134]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V16 V18 V11
## 
## [[135]]
## + 6/56 vertices, named, from a28410b:
## [1] V34 V5  V26 V3  V9  V8 
## 
## [[136]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V26 V3  V10
## 
## [[137]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V26 V11 V18
## 
## [[138]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V26 V11 V8 
## 
## [[139]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V26 V18 V10
## 
## [[140]]
## + 4/56 vertices, named, from a28410b:
## [1] V34 V5  V31 V3 
## 
## [[141]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V31 V18 V11
## 
## [[142]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V44 V3  V8 
## 
## [[143]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V44 V3  V10
## 
## [[144]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V47 V9  V8 
## 
## [[145]]
## + 5/56 vertices, named, from a28410b:
## [1] V34 V5  V47 V18 V10
## 
## [[146]]
## + 6/56 vertices, named, from a28410b:
## [1] V4  V42 V8  V9  V19 V3 
## 
## [[147]]
## + 5/56 vertices, named, from a28410b:
## [1] V4  V42 V8  V9  V23
## 
## [[148]]
## + 6/56 vertices, named, from a28410b:
## [1] V4  V42 V8  V12 V3  V19
## 
## [[149]]
## + 6/56 vertices, named, from a28410b:
## [1] V4  V42 V8  V12 V3  V15
## 
## [[150]]
## + 5/56 vertices, named, from a28410b:
## [1] V4  V42 V10 V19 V3 
## 
## [[151]]
## + 4/56 vertices, named, from a28410b:
## [1] V4  V16 V15 V12
## 
## [[152]]
## + 3/56 vertices, named, from a28410b:
## [1] V4  V16 V21
## 
## [[153]]
## + 4/56 vertices, named, from a28410b:
## [1] V23 V38 V9  V8 
## 
## [[154]]
## + 3/56 vertices, named, from a28410b:
## [1] V23 V38 V37
## 
## [[155]]
## + 4/56 vertices, named, from a28410b:
## [1] V19 V3  V14 V31
## 
## [[156]]
## + 4/56 vertices, named, from a28410b:
## [1] V18 V5  V14 V26
## 
## [[157]]
## + 4/56 vertices, named, from a28410b:
## [1] V18 V5  V14 V31
## 
## [[158]]
## + 3/56 vertices, named, from a28410b:
## [1] V16 V11 V12
## 
## [[159]]
## + 4/56 vertices, named, from a28410b:
## [1] V44 V3  V5  V14
## 
## [[160]]
## + 4/56 vertices, named, from a28410b:
## [1] V14 V3  V5  V31
## 
## [[161]]
## + 3/56 vertices, named, from a28410b:
## [1] V12 V8  V11

count_max_cliques(as.undirected(net1))

## [1] 161

vcol <- rep("grey80", vcount(as.undirected(net1)))
vcol[unlist(largest.cliques(as.undirected(net1)))] = "gold"
plot(as.undirected(as.undirected(net1)), vertex.label = V(graf.sym1)$Vinicola, vertex.color = vcol)

3.4 Visualização dos grafos no R

# Visualização dos grafos no R

plot(net1, vertex.color = rainbow(56),
     vertex.size = 30,
     edge.arrow.size = 0.1,
     layout = layout.kamada.kawai, vertex.label.color = "black",  vertex.label.cex = 1,
     main = "Network")

# Tamanho dos nós igual baseado no degree
plot(net1, vertex.color = rainbow(56),
     vertex.size = grauTotal1,
     edge.arrow.size = 0.01,
     layout = layout.kamada.kawai, vertex.label.color = "black",  vertex.label.cex = 1,
     main = "Network")

# Colocar a largura da aresta baseada no peso
E(net1)$width <- E(net1)$Peso
plot(net1, vertex.color =rainbow(56),
     vertex.size = grauTotal1,
     edge.arrow.size = 0.1,
     layout = layout.kamada.kawai,
     vertex.label.color = "black",  vertex.label.cex = 1,
     main = "Network")

# Por municípios #

E(net1)$width <- E(net1)$Peso
plot(net1, vertex.color = V(net1)[Cod_mun],
     vertex.label = V(net1)$Vinicola,
     vertex.size = grauTotal1,
     edge.arrow.size = 0.1,
     layout = layout.kamada.kawai,
     vertex.label.color = "black",  vertex.label.cex = 1,
     main = "Network")

3.5 Salvando os resultados

# Salvar a rede p/ o igraph
# Aqui eu peguei os vetores que eu criei de cada métrica de SNA e concatenei na base de dados original, deixei em comentário pois não precisamos rodar

#write.graph(graph = net1, file = "net1.gml", format = "gml")

#library(foreign)
#dados<- read.spss("C:/Users/user/Desktop/R/SNA/Netwine/SNA/Dados/dados_sna.sav")
#attach(dados)
#library(tibble)
#dados <- as_tibble(dados)

#dadosSNA1 <- cbind(autoridade1, betweenness1, blok1, Burtconstraint, centr_clo1,                    centr_eigen1, closeness1, eccentricity1, ego_size1, eigen_centrality1,                    grauIn1, grauOut1, grauTotal1, hub1, power_centrality1,                    strength1, transitividadeLocal1)


#dados2 <- cbind(dados, dadosSNA1)
#dados2$blok1 <- factor(dados2$blok1, labels = c("Periferia", "Centro"), levels = 1:2)

#library(haven)
#write_sav(dados2, "net1.sav")

4 MCA

4.1 Importação e manipulação da base de dados

library(FactoMineR)
library(foreign)
dados<- read.spss("C:/Users/user/Desktop/R/SNA/Netwine/Recebeu inf tec/net1.sav")
attach(dados)
library(tibble)
dados <- as_tibble(dados)

dados[dados == 9999] <- NA
dados <- dados[-c(38,41,43,44,45,48,49,50,51,52),] # removendo as vinícolas que não responderam
dadosMCAnet1 <- dados[,c(4,10,11,22,23,24,25,26,27,28,29,30,31,32,33,34,52)]
summary(dadosMCAnet1)

##  Producao_cat_fino         A9                         A11             B41A   
##  Baixa:11          Sem_DO/IG:30   Atendimento_ao_turista:39   Não_clones:20  
##  Média:12          Com_DO/IG:16   Não_Pretende          : 6   Sim_clones:26  
##  Alta :23                         Não_turista           : 1                  
##             B41B                         B41C                 B41D   
##  Não_irrigação:41   Não_sistema_treinamento:13   Não_fermentação:21  
##  Sim_irrigação: 5   Sim_sistema_treinamento:33   Sim_fermentação:25  
##                                                                      
##                    B41E                    B41F   
##  Não_enzima/levedura : 9   Não_envelhecimento:20  
##  Sim__enzima/levedura:37   Sim_envelhecimento:26  
##                                                   
##                               B41G            B41H               B41I   
##  Não_recipientes_envelhecimento :26   Não_cortes:17   Não_embalagem: 3  
##  Sim__recipientes_envelhecimento:20   Sim_cortes:29   Sim_embalagem:43  
##                                                                         
##              B41J                   B41K                        B41L   
##  Não_divulgação: 9   Não_canais_vendas:12   Não_estratégias_preços:16  
##  Sim_divulgação:37   Sim_canais_venda :34   Sim_estratégias_preço :30  
##                                                                        
##              B41M          blok1   
##  Não_premiações: 9   Periferia:20  
##  Sim_premiações:37   Centro   :26  
## 

4.2 Tratamento das variáveis

# Manuseio da base de dados

colnames(dadosMCAnet1) <- c("Produção", "DO/IG", "Turismo", "Clones",
                            "Irrigação", "Treinamento", "Fermentação", "Enzima/Levedura",
                            "Envelhecimento", "Recipiente", "Corte", "Embalagem",
                            "Divulgação", "Vendas", "Preço", "Premiação", "Position")

dadosMCAnet1$Produção <- factor(dadosMCAnet1$Produção, labels = c("S_P", "M_P", "B_P")) # S_P = Small Production; M_P = Medium Production, B_P = Big Production
dadosMCAnet1$`DO/IG` <- factor(dadosMCAnet1$`DO/IG`, labels = c("N_GI", "Y_GI")) # N_GI = No Geographic Indication; Y_GI = Yes Geographic Indication
dadosMCAnet1$Turismo <- factor(dadosMCAnet1$Turismo, labels = c("Y_T", "NI_T", "N_T")) # Y_T = Yes Tourism; NI_T = No but intend to have tourism; N_T = No tourism
dadosMCAnet1$Clones <- factor(dadosMCAnet1$Clones, labels = c("N_Clon", "Y_Clon")) # N_Clon = No Clones; Y_Clon = Yes Clones
dadosMCAnet1$Irrigação <- factor(dadosMCAnet1$Irrigação, labels = c("N_Irrig", "Y_Irrig")) # No Irrigation; Y_Irrig = Yes Irrigation
dadosMCAnet1$Treinamento <- factor(dadosMCAnet1$Treinamento, labels = c("N_Train", "Y_Train")) # N_Train = No Training; Y_Train = Yes Training
dadosMCAnet1$Fermentação <- factor(dadosMCAnet1$Fermentação, labels = c("N_Ferm", "Y_Ferm")) # N_Ferm = No Fermentation; Y_Ferm = Yes Fermentation
dadosMCAnet1$`Enzima/Levedura` <- factor(dadosMCAnet1$`Enzima/Levedura`, labels = c("N_Enz", "Y_Enz")) # N_Enz = No Enzyme; Y_Enz = Yes Enzyme
dadosMCAnet1$Corte <- factor(dadosMCAnet1$Corte, labels = c("N_Cut", "Y_Cut")) # N_Cut = No Cut; Y_Cut = Yes Cut
dadosMCAnet1$Envelhecimento <- factor(dadosMCAnet1$Envelhecimento, labels = c("N_Aging", "Y_Aging")) # N_Aging = No Aging; Y_Aging = Yes Aging
dadosMCAnet1$Recipiente <- factor(dadosMCAnet1$Recipiente, labels = c("N_Vess", "Y_Vess")) # N_Vess = No Vessel; Y_Vess = Yes Vessel
dadosMCAnet1$Embalagem <- factor(dadosMCAnet1$Embalagem, labels = c("N_Pack", "Y_Pack")) # N_Pack = No Packing; Y_Pack = Yes Packing
dadosMCAnet1$Divulgação <- factor(dadosMCAnet1$Divulgação, labels = c("N_Div", "Y_Div")) # N_Div = No Divulgation; Y_Div = Yes Divulgation
dadosMCAnet1$Vendas <- factor(dadosMCAnet1$Vendas, labels = c("N_Sal", "Y_Sal")) # N_Sal = No Sales; Y_Sal = Yes Sales
dadosMCAnet1$Preço <- factor(dadosMCAnet1$Preço, labels = c("N_Pric", "Y_Pric")) # N_Pric = No Prices; Y_Pric = Yes Prices
dadosMCAnet1$Premiação <- factor(dadosMCAnet1$Premiação, labels = c("N_Award", "Y_Award")) # N_Award = No Awards; Y_Award = Yes Awards 
dadosMCAnet1$Position <- factor(dadosMCAnet1$Position, labels = c("Periphery", "Center")) # Perip = Periphery; Center = Center

4.3 4.2 Análise MCA

# Questões de 22 a 34: Atividades de inovação - categoria ativa
# Questões 4,8,10,11 e 52: Características das vinícolas - categoria suplementar
# Questão 
# Variáveis suplementarias não são utilizadas para formar os constructos, mas sim para interpretá-los

res.MCA <- MCA(dadosMCAnet1, quali.sup=c(1,2,3,17),graph=FALSE,
               method = "Burt", ncp = 5)

4.3.1 Plots MCA Principais não formatados por cores

par(mar = c(0,0,0,0))
plot.MCA(res.MCA, choix='var',col.quali.sup='#006400')

plot.MCA(res.MCA, choix='var', invisible = c("quali.sup", "quanti.sup"))

plot.MCA(res.MCA,col.quali.sup='#006400',label =c('ind','var','quali.sup'), xlim = c(-0.8,1.5), ylim = c(-0.5,1.5))

plot.MCA(res.MCA,col.quali.sup='#006400',label =c('ind','var','quali.sup'), xlim = c(-0.5,1.1), ylim = c(-0.3,1.4), invisible = c("ind"))

plot.MCA(res.MCA,col.quali.sup='#006400',label =c('ind','var','quali.sup'), xlim = c(-0.5,1.1), ylim = c(-0.3,0.9), invisible = c("quali.sup", "ind"))

plot.MCA(res.MCA,col.quali.sup='#006400',label =c('ind','var','quali.sup'), xlim = c(-0.3,1), ylim = c(-0.5,1.4), invisible = c("var", "ind"))

4.3.2 Estatísticas da MCA

summary(res.MCA, nbelements = Inf)

## 
## Call:
## MCA(X = dadosMCAnet1, ncp = 5, quali.sup = c(1, 2, 3, 17), graph = FALSE,  
##      method = "Burt") 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance               0.064   0.021   0.013   0.008   0.007   0.005   0.003
## % of var.             50.157  16.354  10.469   5.969   5.582   3.601   2.682
## Cumulative % of var.  50.157  66.511  76.980  82.949  88.532  92.133  94.815
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13
## Variance               0.002   0.002   0.001   0.001   0.000   0.000
## % of var.              1.762   1.427   0.890   0.516   0.343   0.246
## Cumulative % of var.  96.577  98.004  98.895  99.411  99.754 100.000
## 
## Individuals
##              Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr   cos2
## 1         | -0.722  4.471  0.655 |  0.151  0.344  0.029 | -0.133  0.331  0.022
## 2         |  0.592  3.010  0.352 | -0.299  1.341  0.089 | -0.387  2.821  0.151
## 3         | -0.471  1.908  0.381 |  0.028  0.012  0.001 | -0.082  0.128  0.012
## 4         | -0.412  1.459  0.372 | -0.251  0.950  0.138 | -0.205  0.786  0.092
## 5         | -0.297  0.755  0.083 |  0.297  1.323  0.083 | -0.287  1.544  0.077
## 6         | -0.412  1.459  0.372 | -0.251  0.950  0.138 | -0.205  0.786  0.092
## 7         |  0.465  1.860  0.195 | -0.153  0.354  0.021 |  0.840 13.245  0.633
## 8         |  0.408  1.430  0.165 |  0.067  0.067  0.004 | -0.038  0.027  0.001
## 9         |  0.120  0.124  0.021 | -0.290  1.267  0.123 | -0.115  0.249  0.019
## 10        | -0.337  0.976  0.186 | -0.034  0.018  0.002 |  0.151  0.429  0.037
## 11        | -0.238  0.486  0.088 | -0.080  0.097  0.010 | -0.313  1.844  0.153
## 12        | -0.554  2.634  0.274 |  0.266  1.066  0.063 |  0.570  6.113  0.291
## 13        |  0.084  0.060  0.008 | -0.210  0.664  0.049 |  0.756 10.731  0.629
## 14        |  0.506  2.194  0.297 | -0.471  3.337  0.258 | -0.168  0.528  0.033
## 15        |  0.236  0.476  0.069 | -0.254  0.970  0.081 |  0.009  0.001  0.000
## 16        |  0.556  2.651  0.238 |  0.317  1.509  0.077 | -0.551  5.702  0.234
## 17        |  0.505  2.188  0.306 | -0.385  2.224  0.178 |  0.069  0.090  0.006
## 18        |  0.756  4.905  0.446 |  0.003  0.000  0.000 | -0.326  1.998  0.083
## 19        |  0.465  1.860  0.195 | -0.153  0.354  0.021 |  0.840 13.245  0.633
## 20        |  0.433  1.609  0.127 |  0.181  0.491  0.022 | -0.076  0.107  0.004
## 21        | -0.397  1.352  0.202 | -0.101  0.153  0.013 |  0.112  0.235  0.016
## 22        | -0.603  3.119  0.730 | -0.061  0.057  0.008 | -0.215  0.873  0.093
## 23        | -0.442  1.678  0.363 | -0.115  0.199  0.025 | -0.145  0.398  0.039
## 24        | -0.561  2.702  0.377 |  0.097  0.143  0.011 | -0.063  0.074  0.005
## 25        |  0.991  8.431  0.287 |  1.348 27.318  0.531 |  0.108  0.217  0.003
## 26        |  0.006  0.000  0.000 | -0.390  2.290  0.221 | -0.304  1.732  0.134
## 27        |  0.415  1.480  0.228 | -0.406  2.479  0.219 |  0.189  0.668  0.047
## 28        |  0.080  0.055  0.011 | -0.530  4.224  0.497 |  0.223  0.937  0.088
## 29        | -0.238  0.486  0.066 | -0.109  0.180  0.014 |  0.612  7.033  0.434
## 30        |  1.377 16.289  0.757 |  0.334  1.681  0.045 | -0.237  1.057  0.022
## 31        |  0.080  0.055  0.011 | -0.530  4.224  0.497 |  0.223  0.937  0.088
## 32        |  0.145  0.180  0.025 | -0.103  0.161  0.013 | -0.462  4.019  0.256
## 33        | -0.468  1.884  0.418 | -0.123  0.229  0.029 |  0.018  0.006  0.001
## 34        |  0.198  0.335  0.042 | -0.143  0.310  0.022 | -0.231  1.000  0.057
## 35        |  0.077  0.051  0.009 | -0.379  2.159  0.230 |  0.123  0.283  0.024
## 36        | -0.399  1.367  0.076 |  1.211 22.039  0.699 |  0.378  2.684  0.068
## 37        | -0.603  3.119  0.730 | -0.061  0.057  0.008 | -0.215  0.873  0.093
## 38        | -0.435  1.625  0.230 |  0.054  0.043  0.003 |  0.488  4.468  0.289
## 39        | -0.603  3.119  0.730 | -0.061  0.057  0.008 | -0.215  0.873  0.093
## 40        | -0.412  1.459  0.372 | -0.251  0.950  0.138 | -0.205  0.786  0.092
## 41        |  0.416  1.488  0.117 |  0.156  0.366  0.016 |  0.521  5.099  0.182
## 42        | -0.754  4.879  0.401 |  0.635  6.061  0.285 | -0.122  0.281  0.011
## 43        |  0.083  0.060  0.007 |  0.319  1.525  0.099 | -0.282  1.499  0.078
## 44        |  0.324  0.903  0.065 |  0.436  2.857  0.117 | -0.088  0.147  0.005
## 45        | -0.635  3.461  0.360 |  0.422  2.681  0.159 | -0.205  0.790  0.038
## 46        |  0.674  3.906  0.237 | -0.121  0.221  0.008 | -0.352  2.327  0.065
##            
## 1         |
## 2         |
## 3         |
## 4         |
## 5         |
## 6         |
## 7         |
## 8         |
## 9         |
## 10        |
## 11        |
## 12        |
## 13        |
## 14        |
## 15        |
## 16        |
## 17        |
## 18        |
## 19        |
## 20        |
## 21        |
## 22        |
## 23        |
## 24        |
## 25        |
## 26        |
## 27        |
## 28        |
## 29        |
## 30        |
## 31        |
## 32        |
## 33        |
## 34        |
## 35        |
## 36        |
## 37        |
## 38        |
## 39        |
## 40        |
## 41        |
## 42        |
## 43        |
## 44        |
## 45        |
## 46        |
## 
## Categories
##              Dim.1    ctr   cos2 v.test    Dim.2    ctr   cos2 v.test    Dim.3
## N_Clon    |  0.299  4.660  0.576  1.758 | -0.057  0.522  0.021 -0.336 |  0.060
## Y_Clon    | -0.230  3.584  0.576 -1.758 |  0.044  0.402  0.021  0.336 | -0.046
## N_Irrig   |  0.012  0.014  0.010  0.221 | -0.099  3.203  0.756 -1.898 | -0.002
## Y_Irrig   | -0.095  0.117  0.010 -0.221 |  0.810 26.263  0.756  1.898 |  0.014
## N_Train   |  0.522  9.229  0.722  2.197 |  0.149  2.310  0.059  0.628 |  0.015
## Y_Train   | -0.206  3.635  0.722 -2.197 | -0.059  0.910  0.059 -0.628 | -0.006
## N_Ferm    |  0.240  3.163  0.417  1.477 | -0.063  0.673  0.029 -0.389 |  0.191
## Y_Ferm    | -0.202  2.657  0.417 -1.477 |  0.053  0.566  0.029  0.389 | -0.160
## N_Enz     |  0.088  0.184  0.018  0.293 |  0.268  5.154  0.165  0.885 |  0.568
## Y_Enz     | -0.022  0.045  0.018 -0.293 | -0.065  1.254  0.165 -0.885 | -0.138
## N_Aging   |  0.367  7.019  0.670  2.158 | -0.173  4.791  0.149 -1.018 |  0.106
## Y_Aging   | -0.282  5.400  0.670 -2.158 |  0.133  3.686  0.149  1.018 | -0.081
## N_Vess    |  0.273  5.039  0.653  2.085 | -0.155  5.015  0.212 -1.188 |  0.007
## Y_Vess    | -0.354  6.550  0.653 -2.085 |  0.202  6.520  0.212  1.188 | -0.009
## N_Cut     |  0.272  3.291  0.389  1.399 |  0.106  1.515  0.058  0.542 |  0.126
## Y_Cut     | -0.160  1.929  0.389 -1.399 | -0.062  0.888  0.058 -0.542 | -0.074
## N_Pack    |  1.014  8.050  0.555  1.797 |  0.520  6.498  0.146  0.922 | -0.161
## Y_Pack    | -0.071  0.562  0.555 -1.797 | -0.036  0.453  0.146 -0.922 |  0.011
## N_Div     |  0.503  5.945  0.458  1.665 |  0.468 15.785  0.396  1.549 | -0.124
## Y_Div     | -0.122  1.446  0.458 -1.665 | -0.114  3.840  0.396 -1.549 |  0.030
## N_Sal     |  0.539  9.096  0.704  2.148 |  0.033  0.107  0.003  0.133 | -0.248
## Y_Sal     | -0.190  3.210  0.704 -2.148 | -0.012  0.038  0.003 -0.133 |  0.088
## N_Pric    |  0.438  8.011  0.721  2.146 |  0.043  0.234  0.007  0.209 | -0.165
## Y_Pric    | -0.234  4.272  0.721 -2.146 | -0.023  0.125  0.007 -0.209 |  0.088
## N_Award   | -0.315  2.327  0.240 -1.042 |  0.321  7.440  0.250  1.064 |  0.100
## Y_Award   |  0.077  0.566  0.240  1.042 | -0.078  1.810  0.250 -1.064 | -0.024
##              ctr   cos2 v.test  
## N_Clon     0.885  0.023  0.350 |
## Y_Clon     0.681  0.023 -0.350 |
## N_Irrig    0.001  0.000 -0.033 |
## Y_Irrig    0.012  0.000  0.033 |
## N_Train    0.036  0.001  0.063 |
## Y_Train    0.014  0.001 -0.063 |
## N_Ferm     9.569  0.264  1.174 |
## Y_Ferm     8.038  0.264 -1.174 |
## N_Enz     36.264  0.744  1.879 |
## Y_Enz      8.821  0.744 -1.879 |
## N_Aging    2.784  0.055  0.621 |
## Y_Aging    2.141  0.055 -0.621 |
## N_Vess     0.017  0.000  0.055 |
## Y_Vess     0.022  0.000 -0.055 |
## N_Cut      3.378  0.083  0.648 |
## Y_Cut      1.980  0.083 -0.648 |
## N_Pack     0.966  0.014 -0.284 |
## Y_Pack     0.067  0.014  0.284 |
## N_Div      1.719  0.028 -0.409 |
## Y_Div      0.418  0.028  0.409 |
## N_Sal      9.228  0.149 -0.989 |
## Y_Sal      3.257  0.149  0.989 |
## N_Pric     5.412  0.102 -0.806 |
## Y_Pric     2.887  0.102  0.806 |
## N_Award    1.127  0.024  0.331 |
## Y_Award    0.274  0.024 -0.331 |
## 
## Categorical variables (eta2)
##                   Dim.1 Dim.2 Dim.3  
## Clones          | 0.271 0.017 0.024 |
## Irrigação       | 0.004 0.554 0.000 |
## Treinamento     | 0.423 0.061 0.001 |
## Fermentação     | 0.192 0.023 0.265 |
## Enzima/Levedura | 0.008 0.120 0.678 |
## Envelhecimento  | 0.409 0.159 0.074 |
## Recipiente      | 0.381 0.217 0.001 |
## Corte           | 0.172 0.045 0.081 |
## Embalagem       | 0.283 0.131 0.016 |
## Divulgação      | 0.243 0.369 0.032 |
## Vendas          | 0.405 0.003 0.188 |
## Preço           | 0.404 0.007 0.125 |
## Premiação       | 0.095 0.174 0.021 |
## 
## Supplementary categories
##              Dim.1   cos2 v.test    Dim.2   cos2 v.test    Dim.3   cos2 v.test
## S_P       |  0.047  0.043  0.177 | -0.129  0.321 -0.483 | -0.054  0.057 -0.205
## M_P       |  0.060  0.046  0.238 |  0.166  0.354  0.660 |  0.027  0.009  0.106
## B_P       | -0.054  0.210 -0.360 | -0.025  0.045 -0.167 |  0.012  0.011  0.081
## N_GI      |  0.075  0.537  0.693 |  0.013  0.016  0.120 |  0.025  0.060  0.231
## Y_GI      | -0.142  0.537 -0.693 | -0.025  0.016 -0.120 | -0.047  0.060 -0.231
## Y_T       | -0.028  0.197 -0.436 | -0.007  0.013 -0.110 | -0.040  0.421 -0.637
## NI_T      |  0.014  0.001  0.035 | -0.180  0.227 -0.467 |  0.243  0.417  0.632
## N_T       |  0.991  0.287  0.991 |  1.348  0.531  1.348 |  0.108  0.003  0.108
## Periphery |  0.015  0.016  0.090 |  0.009  0.005  0.052 |  0.084  0.501  0.496
## Center    | -0.012  0.016 -0.090 | -0.007  0.005 -0.052 | -0.065  0.501 -0.496
##            
## S_P       |
## M_P       |
## B_P       |
## N_GI      |
## Y_GI      |
## Y_T       |
## NI_T      |
## N_T       |
## Periphery |
## Center    |
## 
## Supplementary categorical variables (eta2)
##             Dim.1 Dim.2 Dim.3  
## Produção  | 0.011 0.079 0.008 |
## DO/IG     | 0.042 0.002 0.010 |
## Turismo   | 0.087 0.303 0.081 |
## Position  | 0.001 0.000 0.047 |

dimdesc(res.MCA)$`Dim 1`$quali

##                       R2      p.value
## Treinamento    0.4234564 9.785803e-07
## Envelhecimento 0.4088119 1.726609e-06
## Vendas         0.4050878 1.990645e-06
## Preço          0.4043304 2.048893e-06
## Recipiente     0.3814889 4.813458e-06
## Embalagem      0.2834665 1.397548e-04
## Clones         0.2713786 2.058163e-04
## Divulgação     0.2433178 4.955450e-04
## Fermentação    0.1915557 2.351758e-03
## Corte          0.1718386 4.180220e-03
## Premiação      0.0952447 3.690798e-02

dimdesc(res.MCA)$`Dim 2`$quali

##                        R2      p.value
## Irrigação       0.5538650 3.068959e-09
## Divulgação      0.3688706 7.620669e-06
## Turismo         0.3025613 4.318973e-04
## Recipiente      0.2168112 1.110404e-03
## Premiação       0.1738571 3.942686e-03
## Envelhecimento  0.1593405 5.994397e-03
## Embalagem       0.1306537 1.358096e-02
## Enzima/Levedura 0.1204545 1.812238e-02

dimdesc(res.MCA)$`Dim 3`$quali

##                        R2      p.value
## Enzima/Levedura 0.6780548 2.132750e-12
## Fermentação     0.2647840 2.536412e-04
## Vendas          0.1877561 2.629218e-03
## Preço           0.1248095 1.602374e-02

res.MCA$var$v.test

##              Dim 1      Dim 2       Dim 3        Dim 4      Dim 5
## N_Clon   1.7584869 -0.3360890  0.35021181 -0.260839245 -0.9080786
## Y_Clon  -1.7584869  0.3360890 -0.35021181  0.260839245  0.9080786
## N_Irrig  0.2214264 -1.8983554 -0.03265782 -0.156764963  0.1975460
## Y_Irrig -0.2214264  1.8983554  0.03265782  0.156764963 -0.1975460
## N_Train  2.1966239  0.6275699  0.06287002  0.876071045 -0.5482604
## Y_Train -2.1966239 -0.6275699 -0.06287002 -0.876071045  0.5482604
## N_Ferm   1.4774028 -0.3892665  1.17407144  0.031479032  1.0302838
## Y_Ferm  -1.4774028  0.3892665 -1.17407144 -0.031479032 -1.0302838
## N_Enz    0.2927332  0.8852931  1.87880218  0.000134767  0.1768150
## Y_Enz   -0.2927332 -0.8852931 -1.87880218 -0.000134767 -0.1768150
## N_Aging  2.1583066 -1.0182130  0.62096699  0.701508267 -0.1895100
## Y_Aging -2.1583066  1.0182130 -0.62096699 -0.701508267  0.1895100
## N_Vess   2.0849339 -1.1877263  0.05460176  0.211194044 -0.2226636
## Y_Vess  -2.0849339  1.1877263 -0.05460176 -0.211194044  0.2226636
## N_Cut    1.3993027  0.5421602  0.64773631 -1.279341965 -0.4719008
## Y_Cut   -1.3993027 -0.5421602 -0.64773631  1.279341965  0.4719008
## N_Pack   1.7972240  0.9220115 -0.28441225 -0.242188341  1.0445596
## Y_Pack  -1.7972240 -0.9220115  0.28441225  0.242188341 -1.0445596
## N_Div    1.6650922  1.5492180 -0.40911001 -0.067616519 -0.1769743
## Y_Div   -1.6650922 -1.5492180  0.40911001  0.067616519  0.1769743
## N_Sal    2.1484534  0.1330158 -0.98865698  0.385674716  0.6152267
## Y_Sal   -2.1484534 -0.1330158  0.98865698 -0.385674716 -0.6152267
## N_Pric   2.1464439  0.2094848 -0.80606936 -0.395773373 -0.2104035
## Y_Pric  -2.1464439 -0.2094848  0.80606936  0.395773373  0.2104035
## N_Award -1.0417698  1.0635839  0.33127316  1.029982119 -0.2537185
## Y_Award  1.0417698 -1.0635839 -0.33127316 -1.029982119  0.2537185

res.MCA$quali.sup

## $coord
##                 Dim 1        Dim 2       Dim 3       Dim 4        Dim 5
## S_P        0.04707117 -0.128551125 -0.05439365 -0.03922454 -0.140099795
## M_P        0.05963098  0.165661435  0.02668757 -0.09767592  0.138899748
## B_P       -0.05362412 -0.024951080  0.01209040  0.06972091 -0.005465184
## N_GI       0.07548653  0.013083872  0.02518527  0.01223494  0.031208793
## Y_GI      -0.14153724 -0.024532261 -0.04722238 -0.02294050 -0.058516487
## Y_T       -0.02750638 -0.006929217 -0.04020504  0.02848823  0.009824839
## NI_T       0.01362497 -0.179616257  0.24340843 -0.13266951 -0.138072124
## N_T        0.99099913  1.347936996  0.10754588 -0.31502398  0.445264039
## Periphery  0.01527398  0.008802058  0.08433351  0.03618467 -0.005853066
## Center    -0.01174921 -0.006770814 -0.06487193 -0.02783436  0.004502359
## 
## $cos2
##                 Dim 1       Dim 2       Dim 3      Dim 4       Dim 5
## S_P       0.043002165 0.320724447 0.057421786 0.02986043 0.380938878
## M_P       0.045843295 0.353813744 0.009182267 0.12300050 0.248733782
## B_P       0.210081758 0.045482752 0.010679470 0.35513556 0.002182115
## N_GI      0.537305249 0.016141905 0.059810212 0.01411515 0.091840901
## Y_GI      0.537305249 0.016141905 0.059810212 0.01411515 0.091840901
## Y_T       0.197053364 0.012505043 0.420995859 0.21137220 0.025140142
## NI_T      0.001306691 0.227087531 0.417035704 0.12389228 0.134188103
## N_T       0.286827484 0.530656395 0.003378017 0.02898421 0.057904119
## Periphery 0.016420869 0.005453312 0.500601652 0.09215970 0.002411342
## Center    0.016420869 0.005453312 0.500601652 0.09215970 0.002411342
## 
## $v.test
##                 Dim 1       Dim 2      Dim 3      Dim 4       Dim 5
## S_P        0.17702051 -0.48344209 -0.2045581 -0.1475117 -0.52687316
## M_P        0.23764539  0.66020505  0.1063571 -0.3892646  0.55355258
## B_P       -0.35972152 -0.16737694  0.0811049  0.4677021 -0.03666157
## N_GI       0.69338803  0.12018304  0.2313415  0.1123851  0.28667107
## Y_GI      -0.69338803 -0.12018304 -0.2313415 -0.1123851 -0.28667107
## Y_T       -0.43553497 -0.10971694 -0.6366050  0.4510815  0.15556610
## NI_T       0.03539871 -0.46665672  0.6323937 -0.3446855 -0.35872190
## N_T        0.99099913  1.34793700  0.1075459 -0.3150240  0.44526404
## Periphery  0.08986420  0.05178677  0.4961749  0.2128919 -0.03443642
## Center    -0.08986420 -0.05178677 -0.4961749 -0.2128919  0.03443642
## 
## $eta2
##                 Dim 1        Dim 2       Dim 3       Dim 4        Dim 5
## Produção 0.0114338502 0.0789973261 0.008353526 0.060526677 0.1153168934
## DO/IG    0.0421939310 0.0022199097 0.010280390 0.003213069 0.0216181877
## Turismo  0.0869426845 0.3025612592 0.080820250 0.058855454 0.0814242630
## Position 0.0007087135 0.0004121796 0.047290315 0.011529785 0.0003119519

4.3.3 Contribuição

library(factoextra) 
fviz_contrib(res.MCA, choice = "var", axes = 1, top = 15)

fviz_contrib(res.MCA, choice = "var", axes = , top = 15)

fviz_contrib(res.MCA, choice = "var", axes = 3, top = 15)

g1 <- plot.MCA(res.MCA,col.quali.sup='#006400',
               label =c('var','quali.sup'), 
               title = "1 MCA Factor Map",
               col.ind = "blue",
               cex = 0.85)

g1

g2 <- plot.MCA(res.MCA, col.var=c("#1E5AAB", "#1E5AAB", "red", "red", "#720012", "#720012", "purple", "purple", "black", "black", "#CDA4DE", "#CDA4DE", "darkgray", "darkgray", "#897D62", "#897D62", "orange", "orange", "#901F76", "#901F76",
                                  "#720012", "#720012", "#469BC3", "#469BC3", "#D05098", "#D05098"),
               label =c("var"), 
               invisible = c("ind"),
               title = "2 MCA Categories",
               cex = 0.85)

g2

g3 <- plot.MCA(res.MCA,col.quali.sup= c("#1E5AAB", "#1E5AAB", "#1E5AAB", "black", "black", "darkgreen", "darkgreen", "darkgreen", "purple", "purple"),
               label =c("quali.sup"), 
               invisible = c("ind"),
               title = "3 MCA Supplementary Categories",
               cex = 1)

g3

g4 <- plotellipses(res.MCA, keepvar = c(17),
                   title = "4 Confidence Ellipses Around the Categories of Position",
             level = 0.95)
g4

library(gridExtra)
grid.arrange(g1, g2, ncol = 2)

grid.arrange(g3, g4, ncol = 2)

4.4 Estatísticas

library(car)
library(effsize)
library(rcompanion)

4.4.1 Mann

# Mann #
aggregate(dados$grauTotal1 ~ dados$blok1, data = dados, FUN = mean)

##   dados$blok1 dados$grauTotal1
## 1   Periferia          5.95000
## 2      Centro         19.03846

wilcox.test(dados$grauTotal1 ~ dados$blok1, conf.int = T, exact = F) # W = 19.5, p-value = 1.01e-07

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  dados$grauTotal1 by dados$blok1
## W = 19.5, p-value = 1.01e-07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -15.999985  -8.000047
## sample estimates:
## difference in location 
##              -11.00007

wilcoxonR(x = dados$grauTotal1, g = dados$blok1) # -0.787  Efeito grande

##      r 
## -0.787

4.4.2 Teste T

# Teste T #

aggregate(Fator_inovação ~ blok1, data = dados, FUN = mean) # Média do centro é maior

##       blok1 Fator_inovação
## 1 Periferia     -0.1825548
## 2    Centro      0.1404268

leveneTest(dados$Fator_inovação ~ dados$blok1, data = dados, center = median) ## Teste de Brown-Forsythe para homogeneidade de variâncias

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.2721 0.6045
##       44

t.test(dados$Fator_inovação ~ dados$blok1, paired = F, conf.level = 0.95, var.eq = T) # t = -1.2391, df = 44, p-value = 0.2219

## 
##  Two Sample t-test
## 
## data:  dados$Fator_inovação by dados$blok1
## t = -1.2391, df = 44, p-value = 0.2219
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.8482843  0.2023211
## sample estimates:
## mean in group Periferia    mean in group Centro 
##              -0.1825548               0.1404268

cohen.d(dados$Fator_inovação ~ dados$blok1, data = dados, paired = F) # -0.3685528 (small)

## 
## Cohen's d
## 
## d estimate: -0.3685528 (small)
## 95 percent confidence interval:
##      lower      upper 
## -0.9729546  0.2358491

5 Machine Learning

library(foreign)
dados<- read.spss("C:/Users/user/Desktop/R/SNA/Netwine/Recebeu inf tec/net1.sav")
library(tibble)
dados <- as_tibble(dados)
dados <- dados[,c(49:66)]
dados$blok1 <- NULL
dados2 <- dados[-c(38,41,43,44,45,48,49,50,51,52),]
dados2[is.na(dados2)] <- 0
dados2 <- dados2[,-c(4,5,6,16)] # Removendo as métricas idênticas ou desnecessárias para o artigo

5.1 Descritivas e Escalonamento

# Normalidade
shapiro.test(dados2$Fator_inovação) # VD normal

## 
##  Shapiro-Wilk normality test
## 
## data:  dados2$Fator_inovação
## W = 0.97156, p-value = 0.3162

hist(dados2$Fator_inovação)

# Correlações
cor <- cor(dados2)[,1]
sort(cor, decreasing = T)

##       Fator_inovação    eigen_centrality1          autoridade1 
##          1.000000000          0.467601401          0.451670747 
##              grauIn1    power_centrality1           closeness1 
##          0.379023359          0.296152140          0.224020155 
##           grauTotal1            ego_size1 transitividadeLocal1 
##          0.182987819          0.181157803          0.132731767 
##         betweenness1                 hub1             grauOut1 
##          0.104547899         -0.008942165         -0.031683734 
##        eccentricity1 
##         -0.094521207

# Escalonamento
dados2 <- as.data.frame(scale(dados2))
boxplot(dados2$Fator_inovação) # Sem Outliers

5.2 5.2 Boruta

dadosBoruta <- dados2
colnames(dadosBoruta) <- c("Innovation Activity", "Authority", "Betweenness", "Closeness",
                           "Eccentricity", "Ego Size", "Eigen Centrality", "In Degree",
                           "Out Degree", "Total Degree", "Hub", "Power Centrality",
                           "Local Transtivity")

library(Boruta)
set.seed(1, sample.kind = "Rounding")
boruta <- Boruta(dadosBoruta$`Innovation Activity` ~., data = dadosBoruta, maxRuns = 500, 
                 pValue = 0.01, doTrace = 0, getImp = getImpRfZ)

print(boruta)

## Boruta performed 499 iterations in 7.465027 secs.
##  4 attributes confirmed important: `Eigen Centrality`, `In Degree`,
## Authority, Hub;
##  7 attributes confirmed unimportant: `Ego Size`, `Local Transtivity`,
## `Out Degree`, `Power Centrality`, `Total Degree` and 2 more;
##  1 tentative attributes left: Closeness;

par(mar = c(8,5,8,5))
plot(boruta, las = 2, cex.axis = 0.7, xlab = "", main = "")

plotImpHistory(boruta)

#dev.off()

# Concertandos os atributos que não tiveram um diagnóstico claro
attStats(boruta) # Probabilidades

##                        meanImp  medianImp     minImp     maxImp   normHits
## Authority            8.8046021  8.8276025  5.8684706 11.6761256 0.98396794
## Betweenness         -0.5873878 -0.5075578 -2.0990512  1.5942978 0.00000000
## Closeness            2.5937354  2.6264147 -1.0747861  6.1301853 0.46693387
## Eccentricity        -1.5947177 -1.7302298 -2.5175170 -0.2621905 0.00000000
## `Ego Size`           1.4788326  1.5172165 -0.9300296  3.9026244 0.03206413
## `Eigen Centrality`   7.4306129  7.3909919  4.4522463 11.0690652 0.96392786
## `In Degree`          4.4711996  4.5244641  1.1892580  7.0846117 0.72945892
## `Out Degree`         1.8120523  1.8586048 -0.5942205  4.6084299 0.04208417
## `Total Degree`       1.5428821  1.5777043 -0.2201988  3.0729108 0.01002004
## Hub                  3.4664952  3.4041872 -0.2848275  7.7696740 0.60921844
## `Power Centrality`   2.1030030  2.0643331 -0.8085407  4.6704371 0.09418838
## `Local Transtivity`  0.1545756  0.1266351 -1.1102060  1.7679986 0.00000000
##                      decision
## Authority           Confirmed
## Betweenness          Rejected
## Closeness           Tentative
## Eccentricity         Rejected
## `Ego Size`           Rejected
## `Eigen Centrality`  Confirmed
## `In Degree`         Confirmed
## `Out Degree`         Rejected
## `Total Degree`       Rejected
## Hub                 Confirmed
## `Power Centrality`   Rejected
## `Local Transtivity`  Rejected

getNonRejectedFormula(boruta) # Não rejeitados

## dadosBoruta$`Innovation Activity` ~ Authority + Closeness + `Eigen Centrality` + 
##     `In Degree` + Hub
## <environment: 0x0000000024b68e30>

getConfirmedFormula(boruta) # Confirmados

## dadosBoruta$`Innovation Activity` ~ Authority + `Eigen Centrality` + 
##     `In Degree` + Hub
## <environment: 0x0000000024aed7c0>

getSelectedAttributes(boruta)

## [1] "Authority"          "`Eigen Centrality`" "`In Degree`"       
## [4] "Hub"

bor <- TentativeRoughFix(boruta)
print(bor)

## Boruta performed 499 iterations in 7.465027 secs.
## Tentatives roughfixed over the last 499 iterations.
##  5 attributes confirmed important: `Eigen Centrality`, `In Degree`,
## Authority, Closeness, Hub;
##  7 attributes confirmed unimportant: `Ego Size`, `Local Transtivity`,
## `Out Degree`, `Power Centrality`, `Total Degree` and 2 more;

attStats(bor) # Probabilidades

##                        meanImp  medianImp     minImp     maxImp   normHits
## Authority            8.8046021  8.8276025  5.8684706 11.6761256 0.98396794
## Betweenness         -0.5873878 -0.5075578 -2.0990512  1.5942978 0.00000000
## Closeness            2.5937354  2.6264147 -1.0747861  6.1301853 0.46693387
## Eccentricity        -1.5947177 -1.7302298 -2.5175170 -0.2621905 0.00000000
## `Ego Size`           1.4788326  1.5172165 -0.9300296  3.9026244 0.03206413
## `Eigen Centrality`   7.4306129  7.3909919  4.4522463 11.0690652 0.96392786
## `In Degree`          4.4711996  4.5244641  1.1892580  7.0846117 0.72945892
## `Out Degree`         1.8120523  1.8586048 -0.5942205  4.6084299 0.04208417
## `Total Degree`       1.5428821  1.5777043 -0.2201988  3.0729108 0.01002004
## Hub                  3.4664952  3.4041872 -0.2848275  7.7696740 0.60921844
## `Power Centrality`   2.1030030  2.0643331 -0.8085407  4.6704371 0.09418838
## `Local Transtivity`  0.1545756  0.1266351 -1.1102060  1.7679986 0.00000000
##                      decision
## Authority           Confirmed
## Betweenness          Rejected
## Closeness           Confirmed
## Eccentricity         Rejected
## `Ego Size`           Rejected
## `Eigen Centrality`  Confirmed
## `In Degree`         Confirmed
## `Out Degree`         Rejected
## `Total Degree`       Rejected
## Hub                 Confirmed
## `Power Centrality`   Rejected
## `Local Transtivity`  Rejected

getNonRejectedFormula(bor) # Não rejeitados

## dadosBoruta$`Innovation Activity` ~ Authority + Closeness + `Eigen Centrality` + 
##     `In Degree` + Hub
## <environment: 0x00000000235a7e90>

getConfirmedFormula(bor) # Confirmados

## dadosBoruta$`Innovation Activity` ~ Authority + Closeness + `Eigen Centrality` + 
##     `In Degree` + Hub
## <environment: 0x0000000023526cb8>

getSelectedAttributes(bor)

## [1] "Authority"          "Closeness"          "`Eigen Centrality`"
## [4] "`In Degree`"        "Hub"

5.3 Regressão de Lasso

# Regressão de Lasso #

library(caret)
library(DMwR)

set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = .7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]

custom <- trainControl(method = "LOOCV",
                       number = 5,
                       verboseIter = F)

lambda <- 10^seq(-3, 3, length = 100)

set.seed(100, sample.kind = "Rounding")
lasso <- train(Fator_inovação ~.,
               trainData,
               method = "glmnet",
               tuneGrid = expand.grid(alpha = 1,
                                      lambda = lambda),
               trControl = custom)

coef(lasso$finalModel, lasso$bestTune$lambda)

## 13 x 1 sparse Matrix of class "dgCMatrix"
##                                1
## (Intercept)           0.10793271
## autoridade1           0.30386294
## betweenness1          .         
## closeness1            .         
## eccentricity1         .         
## ego_size1             .         
## eigen_centrality1     0.16811145
## grauIn1               .         
## grauOut1             -0.20234088
## grauTotal1            .         
## hub1                 -0.02723877
## power_centrality1     0.02507355
## transitividadeLocal1  .

varImp(lasso)

## glmnet variable importance
## 
##                      Overall
## autoridade1          100.000
## grauOut1              66.590
## eigen_centrality1     55.325
## hub1                   8.964
## power_centrality1      8.252
## ego_size1              0.000
## transitividadeLocal1   0.000
## betweenness1           0.000
## eccentricity1          0.000
## grauTotal1             0.000
## grauIn1                0.000
## closeness1             0.000

plot(lasso$finalModel, xvar = "lambda", label = T)

plot(lasso$finalModel, xvar = "dev", label = T)

varImp(lasso$finalModel)

##                         Overall
## autoridade1          0.30386294
## betweenness1         0.00000000
## closeness1           0.00000000
## eccentricity1        0.00000000
## ego_size1            0.00000000
## eigen_centrality1    0.16811145
## grauIn1              0.00000000
## grauOut1             0.20234088
## grauTotal1           0.00000000
## hub1                 0.02723877
## power_centrality1    0.02507355
## transitividadeLocal1 0.00000000

plot(varImp(lasso, scale = F))

# Erro de teste #
pred <- predict(lasso, testData)
regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.8125351 1.0233678 1.0116164 1.5651153

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y))

##       RMSE   Rsquare       MAE
## 1 1.011616 0.2269016 0.8125351

6 Regressão Elástica

# Elástica #

library(caret)
library(DMwR)

set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = .7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]

custom <- trainControl(method = "LOOCV",
                       number = 5,
                       verboseIter = F)

lambda <- 10^seq(-3, 3, length = 100)

set.seed(100, sample.kind = "Rounding")
en <- train(Fator_inovação ~.,
            trainData,
            method = "glmnet",
            tuneGrid = expand.grid(alpha = seq(0,1, length = 10),
                                   lambda = lambda),
            trControl = custom)

varImp(en)

## glmnet variable importance
## 
##                      Overall
## autoridade1           100.00
## eigen_centrality1      90.23
## hub1                   49.25
## grauOut1               39.98
## betweenness1           34.80
## power_centrality1      29.13
## closeness1             21.53
## ego_size1               0.00
## grauIn1                 0.00
## transitividadeLocal1    0.00
## grauTotal1              0.00
## eccentricity1           0.00

varImp(en$finalModel)

##                         Overall
## autoridade1          0.29819659
## betweenness1         0.10378636
## closeness1           0.06419059
## eccentricity1        0.00000000
## ego_size1            0.00000000
## eigen_centrality1    0.26907281
## grauIn1              0.00000000
## grauOut1             0.11922089
## grauTotal1           0.00000000
## hub1                 0.14687170
## power_centrality1    0.08686854
## transitividadeLocal1 0.00000000

coef(en$finalModel)

## 13 x 99 sparse Matrix of class "dgCMatrix"
##                                                                             
## (Intercept)          0.09467213 0.095676591 0.09690796 0.09811495 0.09929302
## autoridade1          .          0.007176203 0.01869378 0.02995832 0.04092475
## betweenness1         .          .           .          .          .         
## closeness1           .          .           .          .          .         
## eccentricity1        .          .           .          .          .         
## ego_size1            .          .           .          .          .         
## eigen_centrality1    .          0.013615446 0.02541840 0.03703259 0.04841939
## grauIn1              .          .           .          .          .         
## grauOut1             .          .           .          .          .         
## grauTotal1           .          .           .          .          .         
## hub1                 .          .           .          .          .         
## power_centrality1    .          .           .          .          .         
## transitividadeLocal1 .          .           .          .          .         
##                                                                    
## (Intercept)          0.10043811 0.10154660 0.1026397180 0.103737005
## autoridade1          0.05155245 0.06180529 0.0713131003 0.079964541
## betweenness1         .          .          .            .          
## closeness1           .          .          .            .          
## eccentricity1        .          .          .            .          
## ego_size1            .          .          .            .          
## eigen_centrality1    0.05954416 0.07037682 0.0807077340 0.090138385
## grauIn1              .          .          0.0007411529 0.002799246
## grauOut1             .          .          .            .          
## grauTotal1           .          .          .            .          
## hub1                 .          .          .            .          
## power_centrality1    .          .          .            .          
## transitividadeLocal1 .          .          .            .          
##                                                                       
## (Intercept)           0.104495167  0.10507594  0.10561227  0.105803146
## autoridade1           0.090790905  0.10343153  0.11611607  0.128885349
## betweenness1          .            .           .           .          
## closeness1            .            .           .           .          
## eccentricity1         .            .           .           .          
## ego_size1             .            .           .           .          
## eigen_centrality1     0.100721703  0.11230420  0.12383640  0.135032919
## grauIn1               0.005984722  0.01002302  0.01329013  0.015797596
## grauOut1             -0.012865014 -0.02515090 -0.03664259 -0.047539259
## grauTotal1            .            .           .           .          
## hub1                  .           -0.01044613 -0.02154604 -0.032304239
## power_centrality1     .            .           .           0.002692074
## transitividadeLocal1  .            .           .           .          
##                                                                      
## (Intercept)           0.10540832  0.10501765  0.10462990  0.104433136
## autoridade1           0.14165922  0.15450980  0.16739897  0.179593467
## betweenness1          .           .           .           .          
## closeness1            .           .           .           0.005329762
## eccentricity1         .           .           .           .          
## ego_size1             .           .           .           .          
## eigen_centrality1     0.14588597  0.15647440  0.16675369  0.176055414
## grauIn1               0.01746004  0.01822898  0.01809461  0.016214090
## grauOut1             -0.05751253 -0.06704745 -0.07610889 -0.085756301
## grauTotal1            .           .           .           .          
## hub1                 -0.04264783 -0.05270147 -0.06243048 -0.072671644
## power_centrality1     0.01012011  0.01720310  0.02393560  0.030362449
## transitividadeLocal1  .           .           .           .          
##                                                                      
## (Intercept)           0.104349862  0.10441632  0.10448186  0.10453682
## autoridade1           0.191345932  0.20315537  0.21521025  0.22706523
## betweenness1         -0.001885903 -0.01279702 -0.02315799 -0.03331493
## closeness1            0.012755686  0.01929226  0.02564841  0.03170448
## eccentricity1         .            .           .           .         
## ego_size1             .            .           .           .         
## eigen_centrality1     0.185372914  0.19571122  0.20571796  0.21552601
## grauIn1               0.013586399  0.01288930  0.01116889  0.00873947
## grauOut1             -0.094336323 -0.09969106 -0.10451371 -0.10872439
## grauTotal1            .            .           .           .         
## hub1                 -0.082821904 -0.09012847 -0.09731687 -0.10417618
## power_centrality1     0.036561482  0.04281919  0.04874677  0.05435907
## transitividadeLocal1  .            .           .           .         
##                                                                       
## (Intercept)           0.104580158  0.104611838  0.10472484  0.10487405
## autoridade1           0.238969328  0.250832240  0.26122525  0.27085847
## betweenness1         -0.043140347 -0.052629020 -0.06265128 -0.07268916
## closeness1            0.037443179  0.042874560  0.04777662  0.05225137
## eccentricity1         .            .            .           .         
## ego_size1             .            .            .           .         
## eigen_centrality1     0.225216938  0.234705520  0.24242666  0.24930261
## grauIn1               0.005352487  0.001150447  .           .         
## grauOut1             -0.112312465 -0.115324286 -0.11750705 -0.11901449
## grauTotal1            .            .            .           .         
## hub1                 -0.110828344 -0.117245716 -0.12330542 -0.12918293
## power_centrality1     0.059658113  0.064651745  0.06943749  0.07397825
## transitividadeLocal1  .            .            .           .         
##                                                                     
## (Intercept)           0.10501382  0.10516010  0.10530606  0.10545056
## autoridade1           0.27974125  0.28840116  0.29666722  0.30453605
## betweenness1         -0.08273041 -0.09246745 -0.10198357 -0.11125916
## closeness1            0.05634851  0.06012653  0.06357956  0.06672339
## eccentricity1         .           .           .           .         
## ego_size1             .           .           .           .         
## eigen_centrality1     0.25605336  0.26223289  0.26801920  0.27344016
## grauIn1               .           .           .           .         
## grauOut1             -0.11973572 -0.11989526 -0.11942934 -0.11835681
## grauTotal1            .           .           .           .         
## hub1                 -0.13483397 -0.14037843 -0.14582525 -0.15120936
## power_centrality1     0.07827795  0.08233478  0.08616568  0.08978200
## transitividadeLocal1  .           .           .           .         
##                                                                     
## (Intercept)           0.10559265  0.10572515  0.10585929  0.10598928
## autoridade1           0.31200906  0.31887531  0.32553124  0.33181276
## betweenness1         -0.12027925 -0.12916359 -0.13764935 -0.14584779
## closeness1            0.06957400  0.07212085  0.07443434  0.07650605
## eccentricity1         .           .           .           .         
## ego_size1             .           .           .           .         
## eigen_centrality1     0.27852178  0.28350765  0.28802404  0.29226917
## grauIn1               .           .           .           .         
## grauOut1             -0.11669375 -0.11428455 -0.11146150 -0.10809809
## grauTotal1            .           .           .           .         
## hub1                 -0.15656634 -0.16196539 -0.16738584 -0.17287952
## power_centrality1     0.09319537  0.09642888  0.09947213  0.10234629
## transitividadeLocal1  .           .           .           .         
##                                                                      
## (Intercept)           0.10610858  0.105878544  0.10539368  0.10489034
## autoridade1           0.33751231  0.344584203  0.35420879  0.36348277
## betweenness1         -0.15387674 -0.160851364 -0.16535766 -0.16948784
## closeness1            0.07832858  0.080495855  0.08342882  0.08622775
## eccentricity1         .           .            .           .         
## ego_size1             .          -0.008398046 -0.02391291 -0.03975668
## eigen_centrality1     0.29649074  0.302721422  0.30858581  0.31443031
## grauIn1               .           .            .           .         
## grauOut1             -0.10401272 -0.095592961 -0.08588734 -0.07538430
## grauTotal1            .           .            .           .         
## hub1                 -0.17854527 -0.183703053 -0.18746466 -0.19156034
## power_centrality1     0.10507481  0.107578644  0.10970669  0.11170941
## transitividadeLocal1  .           .            .           .         
##                                                                       
## (Intercept)           0.10437386  0.103771405  0.10319396  0.102569512
## autoridade1           0.37273738  0.381774116  0.39227377  0.402187334
## betweenness1         -0.17331237 -0.177050232 -0.18119796 -0.184694662
## closeness1            0.08886246  0.092317677  0.09621933  0.099980168
## eccentricity1         .           .            .           .          
## ego_size1            -0.05597687 -0.073441300 -0.09092927 -0.109234003
## eigen_centrality1     0.32005736  0.325848709  0.33019978  0.334903132
## grauIn1               .           .            .           .          
## grauOut1             -0.06406844 -0.052347740 -0.04090712 -0.028495327
## grauTotal1            .           .            .           .          
## hub1                 -0.19597562 -0.200498546 -0.20491022 -0.209667017
## power_centrality1     0.11360516  0.115347962  0.11700335  0.118548906
## transitividadeLocal1  .          -0.001913663 -0.00537373 -0.008514184
##                                                                        
## (Intercept)           1.019294e-01  0.10113363  0.100716370  0.09989807
## autoridade1           4.119926e-01  0.42374169  0.434764676  0.45680297
## betweenness1         -1.877966e-01 -0.18859834 -0.189083469 -0.18297239
## closeness1            1.035668e-01  0.10762923  0.111196845  0.11612095
## eccentricity1         .             .           .            .         
## ego_size1            -1.279258e-01 -0.14819776 -0.159387013 -0.16787593
## eigen_centrality1     3.395135e-01  0.34527383  0.347198478  0.35639844
## grauIn1               .             .          -0.002875975 -0.02921548
## grauOut1             -1.535084e-02  .           .            .         
## grauTotal1           -6.526867e-05 -0.00736735 -0.009805468 -0.01382714
## hub1                 -2.146849e-01 -0.21762414 -0.214496108 -0.21584092
## power_centrality1     1.200101e-01  0.12102445  0.121509048  0.12135595
## transitividadeLocal1 -1.140117e-02 -0.01437781 -0.017561714 -0.02164897
##                                                                      
## (Intercept)           0.09902303  0.09819161  0.097077824  0.09572472
## autoridade1           0.47775075  0.49806138  0.517747343  0.54208891
## betweenness1         -0.17611675 -0.16945026 -0.161809857 -0.14949669
## closeness1            0.12085530  0.12525360  0.132751523  0.14652009
## eccentricity1         .           .          -0.003062377 -0.01064258
## ego_size1            -0.17436159 -0.18018101 -0.187232042 -0.19553024
## eigen_centrality1     0.36912711  0.38142658  0.394955900  0.40299104
## grauIn1              -0.05958774 -0.08988831 -0.121958085 -0.15630039
## grauOut1              .           .           .            .         
## grauTotal1           -0.01767637 -0.02030930 -0.024098235 -0.03116228
## hub1                 -0.21857625 -0.22198808 -0.225616640 -0.23186046
## power_centrality1     0.12103784  0.12073825  0.120172140  0.11924903
## transitividadeLocal1 -0.02562615 -0.02935334 -0.033750950 -0.03951696
##                                                                     
## (Intercept)           0.09444520  0.09318404  0.09194366  0.09074028
## autoridade1           0.56579080  0.58936432  0.61304630  0.63659050
## betweenness1         -0.13806306 -0.12631377 -0.11416419 -0.10189568
## closeness1            0.15859427  0.17051362  0.18230911  0.19385436
## eccentricity1        -0.01727621 -0.02382514 -0.03030943 -0.03666422
## ego_size1            -0.20221115 -0.20840636 -0.21385004 -0.21868140
## eigen_centrality1     0.41261270  0.42213692  0.43159627  0.44068099
## grauIn1              -0.19158596 -0.22693008 -0.26260171 -0.29795649
## grauOut1              .           .           .           .         
## grauTotal1           -0.03767714 -0.04452224 -0.05201755 -0.05984733
## hub1                 -0.23803243 -0.24427551 -0.25063221 -0.25699622
## power_centrality1     0.11833679  0.11735830  0.11630513  0.11521668
## transitividadeLocal1 -0.04487926 -0.05011958 -0.05527471 -0.06029574
##                                                                     
## (Intercept)           0.08956951  0.08844014  0.08734302  0.08628735
## autoridade1           0.66001629  0.68315057  0.70611220  0.72872137
## betweenness1         -0.08947213 -0.07706195 -0.06451424 -0.05198691
## closeness1            0.20517761  0.21620755  0.22701763  0.23753423
## eccentricity1        -0.04290125 -0.04898282 -0.05494538 -0.06075092
## ego_size1            -0.22288162 -0.22653016 -0.22942314 -0.23156243
## eigen_centrality1     0.44949137  0.45787617  0.46611844  0.47411977
## grauIn1              -0.33305964 -0.36751279 -0.40171868 -0.43535365
## grauOut1              .           .           .           .         
## grauTotal1           -0.06816516 -0.07682358 -0.08619102 -0.09615572
## hub1                 -0.26332578 -0.26953693 -0.27565229 -0.28161482
## power_centrality1     0.11408586  0.11293483  0.11173926  0.11051898
## transitividadeLocal1 -0.06520042 -0.06996224 -0.07462561 -0.07916648
##                                                                      
## (Intercept)           0.08527969  0.08431523  0.08339528  0.082520705
## autoridade1           0.75080788  0.77242371  0.79351921  0.814046588
## betweenness1         -0.03965540 -0.02746770 -0.01545774 -0.003665395
## closeness1            0.24768914  0.25751793  0.26700947  0.276152137
## eccentricity1        -0.06636195 -0.07179536 -0.07704497 -0.082104129
## ego_size1            -0.23315554 -0.23417935 -0.23460148 -0.234434160
## eigen_centrality1     0.48169245  0.48893226  0.49586369  0.502491021
## grauIn1              -0.46797708 -0.49970823 -0.53049803 -0.560278094
## grauOut1              .           .           .           .          
## grauTotal1           -0.10645367 -0.11721907 -0.12848441 -0.140233492
## hub1                 -0.28733371 -0.29279219 -0.29796710 -0.302835473
## power_centrality1     0.10929858  0.10806965  0.10683521  0.105599944
## transitividadeLocal1 -0.08355212 -0.08780199 -0.09191714 -0.095895919
##                                                                      
## (Intercept)           0.08208724  0.08180576  0.081255531  0.08044045
## autoridade1           0.82840822  0.83700162  0.847497813  0.86433641
## betweenness1          .           .           0.004364145  0.01498772
## closeness1            0.28174217  0.28499085  0.289825600  0.29752092
## eccentricity1        -0.08538715 -0.08739116 -0.090129250 -0.09433016
## ego_size1            -0.22982165 -0.22552547 -0.225913901 -0.22890433
## eigen_centrality1     0.50895124  0.51610176  0.523114022  0.52827084
## grauIn1              -0.58356017 -0.60209493 -0.622923473 -0.64775455
## grauOut1              .           .           .            .         
## grauTotal1           -0.14707092 -0.14816611 -0.148566214 -0.15480613
## hub1                 -0.30693624 -0.31102133 -0.315987789 -0.32082545
## power_centrality1     0.10513212  0.10507854  0.104681682  0.10365942
## transitividadeLocal1 -0.09922354 -0.10165546 -0.104034190 -0.10703511
##                                                                     
## (Intercept)           0.07968457  0.07897457  0.07832334  0.07774265
## autoridade1           0.88214322  0.90024681  0.91793991  0.93430617
## betweenness1          0.02621279  0.03784035  0.04937583  0.05999558
## closeness1            0.30533955  0.31312947  0.32064293  0.32754329
## eccentricity1        -0.09861291 -0.10288962 -0.10702584 -0.11083249
## ego_size1            -0.22961599 -0.22803347 -0.22462664 -0.22103290
## eigen_centrality1     0.53277667  0.53750202  0.54232817  0.54652140
## grauIn1              -0.67208508 -0.69654641 -0.72040791 -0.74201807
## grauOut1              .           .           .           .         
## grauTotal1           -0.16556041 -0.17975602 -0.19613176 -0.21204607
## hub1                 -0.32458450 -0.32774005 -0.33038314 -0.33254672
## power_centrality1     0.10248116  0.10117796  0.09982442  0.09856136
## transitividadeLocal1 -0.11021131 -0.11349924 -0.11677437 -0.11981275
##                                                                     
## (Intercept)           0.07720156  0.07669779  0.07624024  0.07581026
## autoridade1           0.94998799  0.96507434  0.97910531  0.99272056
## betweenness1          0.07016594  0.07998498  0.08907741  0.09794246
## closeness1            0.33409281  0.34033589  0.34610818  0.35165517
## eccentricity1        -0.11444516 -0.11788906 -0.12107473 -0.12413464
## ego_size1            -0.21727106 -0.21312644 -0.20915090 -0.20482827
## eigen_centrality1     0.55038727  0.55405525  0.55726121  0.56035666
## grauIn1              -0.76237203 -0.78170560 -0.79932380 -0.81622201
## grauOut1              .           .           .           .         
## grauTotal1           -0.22796249 -0.24418662 -0.25965283 -0.27548551
## hub1                 -0.33434473 -0.33579328 -0.33692973 -0.33777743
## power_centrality1     0.09733256  0.09612170  0.09499023  0.09386445
## transitividadeLocal1 -0.12271933 -0.12552354 -0.12812829 -0.13066175
##                                                                     
## (Intercept)           0.07541938  0.07506000  0.07472968  0.07442664
## autoridade1           1.00546538  1.01749201  1.02884333  1.03954583
## betweenness1          0.10623731  0.11406177  0.12145032  0.12842257
## closeness1            0.35681502  0.36165068  0.36618474  0.37043231
## eccentricity1        -0.12698236 -0.12965092 -0.13215280 -0.13449648
## ego_size1            -0.20050022 -0.19620959 -0.19194238 -0.18769926
## eigen_centrality1     0.56314636  0.56568293  0.56800295  0.57012918
## grauIn1              -0.83178129 -0.84622721 -0.85965659 -0.87213519
## grauOut1              .           .           .           .         
## grauTotal1           -0.29086576 -0.30583688 -0.32042580 -0.33462786
## hub1                 -0.33836209 -0.33872492 -0.33889017 -0.33888061
## power_centrality1     0.09279676  0.09177709  0.09080189  0.08986982
## transitividadeLocal1 -0.13303897 -0.13528303 -0.13740361 -0.13940657
##                                                                     
## (Intercept)           0.07415096  0.07389647  0.07366377  0.07345334
## autoridade1           1.04950188  1.05893239  1.06780673  1.07601415
## betweenness1          0.13489953  0.14104394  0.14683726  0.15219043
## closeness1            0.37436391  0.37806418  0.38152607  0.38471423
## eccentricity1        -0.13666591 -0.13870703 -0.14061669 -0.14237564
## ego_size1            -0.18363764 -0.17960833 -0.17561032 -0.17181001
## eigen_centrality1     0.57202495  0.57378156  0.57540358  0.57684921
## grauIn1              -0.88356173 -0.89424471 -0.90417125 -0.91322330
## grauOut1              .           .           .           .         
## grauTotal1           -0.34812823 -0.36128755 -0.37406213 -0.38611221
## hub1                 -0.33873989 -0.33847076 -0.33808684 -0.33763302
## power_centrality1     0.08899612  0.08815733  0.08735652  0.08661039
## transitividadeLocal1 -0.14127074 -0.14303886 -0.14470792 -0.14625380
##                                                                             
## (Intercept)           7.326160e-02  7.308479e-02  7.292718e-02  7.277805e-02
## autoridade1           1.083643e+00  1.090857e+00  1.097370e+00  1.103697e+00
## betweenness1          1.571608e-01  1.618713e-01  1.661099e-01  1.702461e-01
## closeness1            3.876647e-01  3.904397e-01  3.929398e-01  3.953516e-01
## eccentricity1        -1.440032e-01 -1.455334e-01 -1.469126e-01 -1.482418e-01
## ego_size1            -1.682171e-01 -1.646886e-01 -1.614885e-01 -1.582405e-01
## eigen_centrality1     5.781448e-01  5.793543e-01  5.803990e-01  5.814183e-01
## grauIn1              -9.215264e-01 -9.292990e-01 -9.362331e-01 -9.429133e-01
## grauOut1             -1.168589e-05 -3.142869e-05 -4.704485e-05 -6.243622e-05
## grauTotal1           -3.974699e-01 -4.084579e-01 -4.184487e-01 -4.284112e-01
## hub1                 -3.371284e-01 -3.365630e-01 -3.360069e-01 -3.353873e-01
## power_centrality1     8.591252e-02  8.524424e-02  8.464098e-02  8.404556e-02
## transitividadeLocal1 -1.476906e-01 -1.490517e-01 -1.502811e-01 -1.514763e-01
##                                                                             
## (Intercept)           7.264732e-02  7.252538e-02  0.0724183156  0.0723157290
## autoridade1           1.109332e+00  1.114681e+00  1.1194445448  1.1241255064
## betweenness1          1.739195e-01  1.774106e-01  0.1805077883  0.1835801200
## closeness1            3.974967e-01  3.995230e-01  0.4013272106  0.4030961433
## eccentricity1        -1.494253e-01 -1.505424e-01 -0.1515380793 -0.1525105223
## ego_size1            -1.553065e-01 -1.524701e-01 -0.1499403130 -0.1473615392
## eigen_centrality1     5.822923e-01  5.831079e-01  0.5837879147  0.5844344395
## grauIn1              -9.488021e-01 -9.543432e-01 -0.9592355913 -0.9640204306
## grauOut1             -7.362278e-05 -8.429814e-05 -0.0001235635 -0.0003305991
## grauTotal1           -4.373749e-01 -4.459964e-01 -0.4536669064 -0.4613310115
## hub1                 -3.347970e-01 -3.341923e-01 -0.3336247636 -0.3328926325
## power_centrality1     8.351473e-02  8.300712e-02  0.0825560309  0.0820977937
## transitividadeLocal1 -1.525438e-01 -1.535560e-01 -0.1544584663 -0.1553507340
##                                                                           
## (Intercept)           0.0722284635  0.0721493046  0.072075186  0.072009489
## autoridade1           1.1282429037  1.1320355137  1.135647530  1.138895234
## betweenness1          0.1862715040  0.1887401160  0.191092862  0.193202601
## closeness1            0.4046501629  0.4060755704  0.407426225  0.408636948
## eccentricity1        -0.1533659867 -0.1541506878 -0.154893816 -0.155560380
## ego_size1            -0.1450434542 -0.1429197048 -0.140871967 -0.139012743
## eigen_centrality1     0.5849461956  0.5853811708  0.585786248  0.586139527
## grauIn1              -0.9682174998 -0.9720872186 -0.975789168 -0.979136422
## grauOut1             -0.0006229629 -0.0009624859 -0.001337666 -0.001721748
## grauTotal1           -0.4680380384 -0.4741161360 -0.479877511 -0.485015332
## hub1                 -0.3321862446 -0.3315155207 -0.330856507 -0.330251562
## power_centrality1     0.0816933571  0.0813219120  0.080966481  0.080646966
## transitividadeLocal1 -0.1561391917 -0.1568628466 -0.157550517 -0.158169291
##                                                                         
## (Intercept)           0.071948245  0.071894658  0.071841899  0.071797410
## autoridade1           1.141955242  1.144657481  1.147339824  1.149648284
## betweenness1          0.195193009  0.196942936  0.198691790  0.200187841
## closeness1            0.409774220  0.410775911  0.411768162  0.412618090
## eccentricity1        -0.156186236 -0.156737972 -0.157283666 -0.157751917
## ego_size1            -0.137243326 -0.135678054 -0.134099273 -0.132720635
## eigen_centrality1     0.586470982  0.586753721  0.587043960  0.587285151
## grauIn1              -0.982307178 -0.985122386 -0.987930744 -0.990361887
## grauOut1             -0.002116312 -0.002498415 -0.002894239 -0.003266642
## grauTotal1           -0.489838913 -0.494047545 -0.498248906 -0.501834329
## hub1                 -0.329671215 -0.329154687 -0.328631262 -0.328174416
## power_centrality1     0.080344683  0.080078771  0.079811924  0.079583136
## transitividadeLocal1 -0.158751678 -0.159266117 -0.159776032 -0.160215882
##                                               
## (Intercept)           0.071756392  0.071752473
## autoridade1           1.151778971  1.152164514
## betweenness1          0.201567939  0.201731166
## closeness1            0.413401694  0.413489282
## eccentricity1        -0.158183512 -0.158239821
## ego_size1            -0.131452958 -0.131244415
## eigen_centrality1     0.587504556  0.587453943
## grauIn1              -0.992615792 -0.993041348
## grauOut1             -0.003628273 -0.003794671
## grauTotal1           -0.505112422 -0.505387485
## hub1                 -0.327751582 -0.327693102
## power_centrality1     0.079372091  0.079351819
## transitividadeLocal1 -0.160621184 -0.160685574

plot(en)

plot(en$finalModel, xvar = "lambda", label = T)

plot(en$finalModel, xvar = "dev", label = T)

plot(varImp(en, scale = F))

# Erro de teste #
pred <- predict(en, testData)
regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.8241615 1.0486710 1.0240464 1.6979599

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y))

##       RMSE   Rsquare       MAE
## 1 1.024046 0.2252365 0.8241615

6.1 Ranger RF

library(caret)
library(DMwR)
getModelInfo()$ranger$parameters

##       parameter     class                         label
## 1          mtry   numeric #Randomly Selected Predictors
## 2     splitrule character                Splitting Rule
## 3 min.node.size   numeric             Minimal Node Size

set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = .7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]

fitControl <- trainControl(
  method = "LOOCV",
  number = 5,
  verboseIter = F)

grid_ranger <- expand.grid(mtry=c(2:12),
                           splitrule = c("variance", "extratrees"),
                           min.node.size = 5)

set.seed(100, sample.kind = "Rounding")
rrfFit <- train(Fator_inovação ~., 
                data = trainData,
                method = 'ranger',
                metric = "RMSE",
                tuneLength = 5, 
                trControl = fitControl,
                tuneGrid = grid_ranger,
                num.trees = 500,
                importance = "permutation")

plot(rrfFit)

varImp(rrfFit, scale = F)

## ranger variable importance
## 
##                        Overall
## eigen_centrality1     0.076414
## autoridade1           0.057365
## grauIn1               0.050693
## grauOut1              0.035109
## grauTotal1            0.025063
## hub1                  0.022182
## ego_size1             0.003650
## closeness1            0.002147
## power_centrality1    -0.002306
## transitividadeLocal1 -0.002871
## betweenness1         -0.011343
## eccentricity1        -0.013022

varImp(rrfFit)

## ranger variable importance
## 
##                      Overall
## eigen_centrality1    100.000
## autoridade1           78.700
## grauIn1               71.241
## grauOut1              53.817
## grauTotal1            42.584
## hub1                  39.362
## ego_size1             18.642
## closeness1            16.961
## power_centrality1     11.982
## transitividadeLocal1  11.350
## betweenness1           1.878
## eccentricity1          0.000

rfImp <- varImp(rrfFit)
plot(rfImp,top = 20)

# Erro de teste #
pred <- predict(rrfFit, testData)
regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.7397107 0.9158729 0.9570125 1.2922878

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y))

##        RMSE   Rsquare       MAE
## 1 0.9570125 0.3420608 0.7397107

6.2 BAGGING

# Bagging #

library(caret)
library(DMwR)
set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = .7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]
getModelInfo()$treebag$parameters

##   parameter     class     label
## 1 parameter character parameter

ctrl <- trainControl(method = "LOOCV", number = 5, verboseIter = F)
set.seed(100, sample.kind = "Rounding")
bag <- train(Fator_inovação ~ ., data = trainData, method = "treebag",
             trControl = ctrl,
             tuneLength=5,
             metric = "RMSE",
             keepX = T)

varImp(bag)

## treebag variable importance
## 
##                      Overall
## autoridade1           100.00
## grauIn1                87.08
## eigen_centrality1      71.70
## closeness1             62.40
## hub1                   53.69
## betweenness1           53.29
## grauTotal1             37.07
## transitividadeLocal1   31.58
## ego_size1              31.13
## power_centrality1      23.83
## grauOut1               21.51
## eccentricity1           0.00

varImp(bag, scale = F)

## treebag variable importance
## 
##                      Overall
## autoridade1          0.32861
## grauIn1              0.28614
## eigen_centrality1    0.23562
## closeness1           0.20506
## hub1                 0.17644
## betweenness1         0.17511
## grauTotal1           0.12181
## transitividadeLocal1 0.10377
## ego_size1            0.10228
## power_centrality1    0.07832
## grauOut1             0.07067
## eccentricity1        0.00000

# Erro de teste #
pred <- predict(bag, testData)
DMwR::regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.6910612 0.9332493 0.9660483 1.1255538

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y))

##        RMSE   Rsquare       MAE
## 1 0.9660483 0.4154716 0.6910612

6.3 Gradient Boosting Machine

# GBM

library(DMwR)
library(caret)

set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = .7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]
getModelInfo()$gbm$parameters

##           parameter   class                   label
## 1           n.trees numeric   # Boosting Iterations
## 2 interaction.depth numeric          Max Tree Depth
## 3         shrinkage numeric               Shrinkage
## 4    n.minobsinnode numeric Min. Terminal Node Size

gbmGrid <- expand.grid(n.trees = c(150,200,250,300,350,400,450,500),
                       shrinkage = c(0.01,0.1,0.2),
                       interaction.depth = c(1:4),
                       n.minobsinnode = c(1:5))

trainControl <- trainControl(method = "LOOCV", number = 5)

set.seed(100, sample.kind = "Rounding")
gbmFit <- train(Fator_inovação ~., data = trainData,
                method = "gbm",
                metric = "RMSE",
                trControl = trainControl,
                tuneGrid = gbmGrid,
                verbose = F)
gbmFit$bestTune

##    n.trees interaction.depth shrinkage n.minobsinnode
## 64     200                 1      0.01              4

plot(gbmFit)

dadosplot <- summary(gbmFit)

# Normalização
normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
imp2 <- normalize(summary(gbmFit)$rel.inf)*100

imp <- summary(gbmFit)
imp <- cbind(imp, imp2)

# Erro de teste 
pred <- predict(gbmFit, testData)
regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.6966799 0.9122859 0.9551366 1.1168671

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y)) # Métricas com R²

##        RMSE   Rsquare       MAE
## 1 0.9551366 0.4723856 0.6966799

6.4 XGBoost

#XGBOOSTING
citation("xgboost")

## 
## To cite package 'xgboost' in publications use:
## 
##   Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang,
##   Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou,
##   Mu Li, Junyuan Xie, Min Lin, Yifeng Geng and Yutian Li (2020).
##   xgboost: Extreme Gradient Boosting. R package version 1.0.0.2.
##   https://CRAN.R-project.org/package=xgboost
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {xgboost: Extreme Gradient Boosting},
##     author = {Tianqi Chen and Tong He and Michael Benesty and Vadim Khotilovich and Yuan Tang and Hyunsu Cho and Kailong Chen and Rory Mitchell and Ignacio Cano and Tianyi Zhou and Mu Li and Junyuan Xie and Min Lin and Yifeng Geng and Yutian Li},
##     year = {2020},
##     note = {R package version 1.0.0.2},
##     url = {https://CRAN.R-project.org/package=xgboost},
##   }

library(xgboost)
library(DiagrammeR)
library(Ckmeans.1d.dp)
library(Matrix)
library(caret)

set.seed(100, sample.kind = "Rounding")
trainRows <- createDataPartition(dados2$Fator_inovação, p = 0.7, list = F)
trainData <- dados2[trainRows,]
testData <- dados2[-trainRows,]

trainm <- sparse.model.matrix(Fator_inovação ~.,-1, data = trainData)
trainm

## 34 x 13 sparse Matrix of class "dgCMatrix"
##                                                                             
## 1  1 -0.59339249 -0.38555410 -0.12003220  1.60851264 -0.55500313 -0.59401428
## 2  1 -0.21568997 -0.30095026 -0.05162955 -0.07311421 -0.55500313  0.12575533
## 4  1  1.69560716  0.60149128  0.69040058 -0.07311421  1.04063086  1.98067078
## 6  1  0.40376859 -0.22894046  0.35593626 -0.07311421  0.02522741  0.37199747
## 7  1 -1.23337893 -0.62998740 -1.02934508  1.60851264 -1.42534894 -1.13278325
## 8  1  1.48333330  3.20892147  1.16301321 -0.07311421  2.49120721  1.49111144
## 9  1  0.21891741 -0.46462800  0.43629457 -0.07311421  0.46040032  0.32855927
## 10 1  1.04748159  3.43001768  1.21406918 -0.07311421  2.49120721  1.42695217
## 11 1 -0.31621603 -0.42553582  0.23918928 -0.07311421  0.31534268 -0.37529208
## 12 1 -0.29984925 -0.29411660  0.27761234 -0.07311421 -0.11983022 -0.52295814
## 13 1  0.37185337 -0.58566862  0.09021154 -0.07311421 -0.40994549  1.06357414
## 15 1 -0.13985379 -0.49519936  0.12676626 -0.07311421 -0.40994549 -0.31620344
## 16 1 -0.27505973 -0.17868041  0.01843135 -0.07311421  0.02522741 -0.23043134
## 17 1 -1.23337893 -0.62998740 -0.05162955 -0.07311421 -0.70006076 -1.13278325
## 18 1  0.17274335 -0.04344112  0.39585620 -0.07311421  0.60545796 -0.01843684
## 19 1  2.03320195  1.73908877  0.87156876 -1.75474106  1.33074613  2.43822587
## 21 1 -0.22198797  0.75768390  0.43629457 -0.07311421  0.31534268 -0.17982131
## 22 1  0.01677069 -0.54208329  0.05410260 -0.07311421 -0.55500313 -0.02936668
## 24 1  0.62155491  0.43032916  0.51876758 -0.07311421  0.60545796  0.23224638
## 25 1 -0.52378900 -0.38766502  0.47726156 -0.07311421  0.31534268 -0.65151657
## 27 1 -0.99056361 -0.59384781 -0.83028565  1.60851264 -1.13523367 -1.03310163
## 28 1 -1.09300799 -0.56470943 -0.08603445 -0.07311421 -0.70006076 -1.08602245
## 29 1 -0.59489632 -0.53233008 -0.08603445 -0.07311421 -0.70006076 -0.14576744
## 31 1  0.37836319 -0.43232705  0.09021154 -0.07311421 -0.11983022  0.02823540
## 32 1 -1.00069043 -0.58320597 -0.55698685 -0.07311421 -0.99017603 -1.01062389
## 33 1 -0.66550710 -0.43499903 -0.12003220 -0.07311421 -0.55500313 -0.58620113
## 34 1  1.62375694  1.58939638  0.73476830 -0.07311421  1.47580377  1.28781238
## 36 1 -0.89637392 -0.62780925 -0.49870324 -0.07311421 -1.28029130 -0.86883362
## 37 1 -0.16917744 -0.10647408  0.09021154 -0.07311421  0.17028505 -0.40193036
## 42 1  1.63504542 -0.62998740  0.31652486 -0.07311421  0.17028505  1.38285683
## 43 1 -0.49059722 -0.62998740 -0.37818458 -0.07311421 -0.84511840 -0.27288803
## 44 1 -1.06513972 -0.62998740 -1.27852147  1.60851264 -1.42534894 -1.05831329
## 45 1 -0.76536054 -0.62998740 -0.64206212 -0.07311421 -1.13523367 -0.66612425
## 46 1 -1.23337893 -0.62998740 -5.51825630 -5.11799475 -1.57040657 -1.13278325
##                                                                          
## 1  -0.61059070 -0.35840800 -0.56507694 -0.56754194 -0.7785451 -0.41759839
## 2  -0.21776312 -0.35840800 -0.35374735 -0.45424285 -0.3713107 -0.25461015
## 4   2.13920238 -0.67853942  0.70290058 -0.60659792  0.4824740 -0.15347617
## 6   0.17506447 -0.03827658  0.06891182 -0.31988986 -0.5278724 -0.44729261
## 7  -1.19983207 -0.99867084 -1.30473049 -0.92731435  0.5189346 -1.57567277
## 8   1.54996101  2.68284049  2.60486686  2.45570921 -0.2553361 -0.82998050
## 9  -0.21776312  0.76205197  0.38590620  1.42209970 -0.1104561  0.49069524
## 10  1.74637480  2.36270907  2.49920207  2.36604519 -0.2951252 -0.88592877
## 11 -0.41417691  0.76205197  0.28024141  0.42337293 -0.7716998 -0.53983958
## 12 -0.02134933 -0.19834229 -0.14241776  0.09263073 -0.6023857  0.22577626
## 13  0.37147826 -0.67853942 -0.24808256 -0.37600277  0.3457747  0.19302264
## 15 -0.21776312 -0.35840800 -0.35374735 -0.18780745 -0.6600361  0.70616267
## 16 -0.21776312 -0.19834229 -0.24808256 -0.48937953  1.2334816 -0.00349907
## 17 -1.19983207 -0.19834229 -0.77640652 -0.05268844  0.7891730  1.30664568
## 18 -0.02134933  1.08218339  0.70290058  0.82584218 -0.4982171 -0.38102762
## 19  2.53202996  0.28185484  1.54821893  0.59047450  0.2915370 -0.64635383
## 21 -0.21776312  0.76205197  0.38590620  1.10293505  2.2082175 -0.18484815
## 22 -0.41417691 -0.67853942 -0.67074173 -0.73959108  1.6874868  1.76987543
## 24  0.17506447  0.76205197  0.59723579  0.58976993 -0.1350947 -0.15347617
## 25 -0.61059070  0.60198626  0.06891182  0.70907671  0.1291405  0.68108755
## 27 -0.80700449 -0.99867084 -1.09340090 -1.09666472  0.5243203  0.22577626
## 28 -1.00341828 -0.19834229 -0.67074173 -0.42336138 -2.6287649 -0.03157360
## 29 -0.61059070 -0.35840800 -0.56507694 -0.22278105  0.1622085 -0.22458600
## 31  0.17506447 -0.35840800 -0.14241776 -0.35586003  0.5576128  0.22577626
## 32 -1.00341828 -0.51847371 -0.88207132 -0.62198418  2.6986833 -1.03523806
## 33 -0.41417691 -0.35840800 -0.45941214 -0.23858826  0.8774452 -0.37470675
## 34  1.15713342  2.04257765  1.97087810  2.09402983 -0.2467383 -0.59504527
## 36 -1.00341828 -0.99867084 -1.19906570 -0.98144546 -0.2586646  3.82867432
## 37 -0.21776312  0.60198626  0.28024141  0.45335947 -0.9483725 -0.53983958
## 42  1.15713342 -1.15873655 -0.14241776 -1.17194174  0.1378506  1.53592101
## 43 -0.21776312 -1.15873655 -0.88207132 -1.17194174  0.1378506  0.04563136
## 44 -1.00341828 -1.15873655 -1.30473049 -1.17194174  0.1378506 -1.57567277
## 45 -0.61059070 -1.15873655 -1.09340090 -1.17194174  0.1378506  0.22577626
## 46 -1.19983207 -1.15873655 -1.41039528 -1.17194174  0.1378506 -1.57567277

train_label <- trainData[, "Fator_inovação"]
train_matrix <- xgb.DMatrix(data = as.matrix(trainm), label = train_label)

testm <- sparse.model.matrix(Fator_inovação ~., -1, data = testData)
testm

## 12 x 13 sparse Matrix of class "dgCMatrix"
##                                                                            
## 3  1  0.55273637  2.45821139  0.9659803 -0.07311421  1.76591904  0.92628088
## 5  1  0.80855526 -0.20591717  0.5187676 -0.07311421  0.60545796  0.51059067
## 14 1 -0.33206797  0.61022463  0.2391893 -0.07311421  0.46040032 -0.55184502
## 20 1 -0.80964397 -0.42882771 -0.1868348 -0.07311421 -0.11983022 -0.85462561
## 23 1 -0.09667601 -0.52861712  0.1267663 -0.07311421 -0.11983022 -0.20511885
## 26 1  2.74915955  0.23747514  0.8715688 -0.07311421  1.47580377  2.19668332
## 30 1 -1.05318014 -0.53802036  0.3165249 -0.07311421  0.17028505 -0.95780130
## 35 1 -0.59945435  0.09619398  0.3165249 -0.07311421  0.02522741 -0.76800348
## 38 1 -0.78863665 -0.62998740 -0.7779153  1.60851264 -0.99017603 -0.97886785
## 39 1 -0.46972365 -0.62998740 -0.4391172  1.60851264 -0.84511840 -0.25422021
## 40 1  2.11574957  1.05038838  0.9184444 -0.07311421  1.62086140  2.21059519
## 41 1  0.23807341 -0.62998740 -0.2196535 -0.07311421 -0.70006076  0.01453132
##                                                                          
## 3   0.56789205  2.36270907  1.86521331  2.1227845 -0.20994083 -0.59940362
## 5   0.17506447  0.76205197  0.59723579  0.9056358 -0.07984237 -0.05866306
## 14 -0.02134933  0.60198626  0.38590620  0.4728472  0.48785964 -0.42327523
## 20 -0.41417691 -0.03827658 -0.24808256 -0.2148191  0.08206890 -0.39654250
## 23 -0.41417691  0.12178913 -0.14241776  0.4921252 -0.23726624  0.30766031
## 26  2.72844376 -0.19834229  1.33688934 -0.2796156  0.51568232 -0.36177327
## 30 -1.00341828  0.76205197 -0.03675297  0.6493844 -2.87624549  0.01791676
## 35 -0.61059070  0.28185484 -0.14241776  0.2003933 -2.42239668  0.22577626
## 38 -0.41417691 -1.15873655 -0.98773611 -1.1719417  0.13785059 -0.67494825
## 39 -0.21776312 -1.15873655 -0.88207132 -1.1719417  0.13785059  0.58606607
## 40  2.13920238  0.44192055  1.44255413  0.5631939  0.21940575 -0.40550075
## 41 -0.02134933 -1.15873655 -0.77640652 -1.1719417  0.13785059  2.74780491

test_label <- testData[, "Fator_inovação"]
test_matrix <- xgb.DMatrix(data = as.matrix(testm), label = test_label)

# Parâmetros #

xgb_params <- list("objective" = "reg:linear",
                   "eval_metric" = "rmse")

watchlist <- list(train = train_matrix, test = test_matrix)

# Modelo #
set.seed(100, sample.kind = "Rounding")
bst_model <- xgb.train(params = xgb_params,
                       data = train_matrix,
                       nfold = 34,
                       nrounds = 1000,
                       watchlist = watchlist,
                       eta = 0.01,
                       max.depth = 1,
                       gamma = 0,
                       subsample = 0.1,
                       colsample_bytree = 0.5,
                       booster = "gbtree",
                       verbose = 0)

bst_model

## ##### xgb.Booster
## raw: 237.1 Kb 
## call:
##   xgb.train(params = xgb_params, data = train_matrix, nrounds = 1000, 
##     watchlist = watchlist, verbose = 0, nfold = 34, eta = 0.01, 
##     max.depth = 1, gamma = 0, subsample = 0.1, colsample_bytree = 0.5, 
##     booster = "gbtree")
## params (as set within xgb.train):
##   objective = "reg:linear", eval_metric = "rmse", nfold = "34", eta = "0.01", max_depth = "1", gamma = "0", subsample = "0.1", colsample_bytree = "0.5", booster = "gbtree", silent = "1"
## xgb.attributes:
##   niter
## callbacks:
##   cb.evaluation.log()
## # of features: 13 
## niter: 1000
## nfeatures : 13 
## evaluation_log:
##     iter train_rmse test_rmse
##        1   1.032224  1.299790
##        2   1.028734  1.295287
## ---                          
##      999   0.756646  0.955193
##     1000   0.756078  0.954679

e <- data.frame(bst_model$evaluation_log)
plot(e$iter, e$train_rmse, col = "blue")

plot(e$iter, e$test_rmse, col = "red")

min(e$test_rmse) #0.949946

## [1] 0.949946

e[e$test_rmse == 0.949946,]

##     iter train_rmse test_rmse
## 992  992   0.756803  0.949946

imp <- xgb.importance(colnames(train_matrix), model = bst_model)
imp$Importance

## NULL

xgb.plot.importance(imp)

# Normalização 
normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x)))
}
imp2 <- normalize(imp$Importance)*100
imp <- cbind(imp, imp2)
imp

##                  Feature       Gain      Cover  Frequency Importance       imp2
##  1:          autoridade1 0.25072027 0.27108434 0.31208499 0.25072027 100.000000
##  2:         betweenness1 0.17491505 0.16097724 0.16733068 0.17491505  68.061750
##  3:           closeness1 0.14998415 0.14759036 0.14741036 0.14998415  57.557865
##  4:            ego_size1 0.08394535 0.08366801 0.08366534 0.08394535  29.734397
##  5:    power_centrality1 0.07206961 0.08165997 0.06374502 0.07206961  24.730911
##  6: transitividadeLocal1 0.05882443 0.05120482 0.04515272 0.05882443  19.150454
##  7:                 hub1 0.05586344 0.04886212 0.04116866 0.05586344  17.902930
##  8:    eigen_centrality1 0.05528882 0.04651941 0.04382470 0.05528882  17.660830
##  9:              grauIn1 0.03683195 0.03748327 0.03585657 0.03683195   9.884582
## 10:             grauOut1 0.03146745 0.03580991 0.03054449 0.03146745   7.624413
## 11:           grauTotal1 0.01671852 0.01740295 0.01328021 0.01671852   1.410396
## 12:        eccentricity1 0.01337096 0.01773762 0.01593625 0.01337096   0.000000

# Erro de teste #
pred <- predict(bst_model, test_matrix)
DMwR::regr.eval(testData$Fator_inovação, pred)

##       mae       mse      rmse      mape 
## 0.6884326 0.9114127 0.9546794 1.0038981

test_y <- as.matrix(testData[, "Fator_inovação"])
data.frame(
  RMSE = RMSE(pred, test_y),
  Rsquare = R2(pred, test_y),
  MAE = MAE(pred, test_y))

##        RMSE   Rsquare       MAE
## 1 0.9546794 0.5099895 0.6884326