Naor Dalal & Koren Levenbrown 12/26/2017
library(igraph)##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
ga.data <- read.csv('part1/ga_edgelist.csv', header = T)
g <- graph.data.frame(ga.data,directed = F)
plot(g)
gDerived <- delete.vertices(g , c('adele' , 'chief' , 'susan grey' , 'thatch grey' , 'ellis grey' , 'tucker' , 'bailey' , 'ben'))
plot(gDerived)
between <- betweenness(gDerived)
between[which.max(between)]## sloan
## 115.3667
close <- closeness(gDerived)
close[which.max(close)]## torres
## 0.01754386
eig <- eigen_centrality(gDerived)
eig$vector[which.max(eig$vector)]## karev
## 1
gc <- edge.betweenness.community(g)
gc## IGRAPH clustering edge betweenness, groups: 7, mod: 0.58
## + groups:
## $`1`
## [1] "lexi" "sloan" "karev" "kepner"
## [5] "addison" "nancy" "mrs. seabury" "avery"
##
## $`2`
## [1] "owen" "yang" "altman" "colin" "preston"
##
## $`3`
## [1] "torres" "o'malley" "arizona" "olivia"
##
## + ... omitted several groups/vertices
memb <- membership(gc)
memb## lexi owen sloan torres derek
## 1 2 1 3 4
## karev o'malley yang grey chief
## 1 3 2 4 5
## ellis grey susan grey bailey izzie altman
## 5 5 6 7 2
## arizona colin preston kepner addison
## 3 2 2 1 1
## nancy olivia mrs. seabury adele thatch grey
## 1 3 1 5 5
## tucker hank denny finn steve
## 6 7 7 4 4
## ben avery
## 6 1
Plot the graph with unique color for each community accordingly
plot(g, vertex.size=7, #vertex.label=NA,
vertex.color=memb, asp=FALSE)
There is 7 communities
length(unique(memb))## [1] 7
The size of each community
t <- as.data.frame(table(memb))
colnames(t) <- c('ID' , 'Size')
t## ID Size
## 1 1 8
## 2 2 5
## 3 3 4
## 4 4 4
## 5 5 5
## 6 6 3
## 7 7 3
The modularity
gc$modularity## [1] -0.04584775 -0.01773356 0.01081315 0.03849481 0.06617647
## [6] 0.09472318 0.12326990 0.14965398 0.17560554 0.20285467
## [11] 0.23096886 0.25865052 0.28633218 0.31358131 0.34083045
## [16] 0.36894464 0.39576125 0.41479239 0.44247405 0.46712803
## [21] 0.49134948 0.50778547 0.52681661 0.54974048 0.57050173
## [26] 0.57742215 0.56098616 0.53416955 0.45804498 0.30449827
gc1 <- walktrap.community(g)
gc1## IGRAPH clustering walktrap, groups: 7, mod: 0.51
## + groups:
## $`1`
## [1] "owen" "yang" "altman" "colin" "preston"
##
## $`2`
## [1] "lexi" "sloan" "torres" "derek"
## [5] "karev" "o'malley" "arizona" "kepner"
## [9] "addison" "nancy" "olivia" "mrs. seabury"
## [13] "avery"
##
## $`3`
## + ... omitted several groups/vertices
memb1 <- membership(gc1)
memb1## lexi owen sloan torres derek
## 2 1 2 2 2
## karev o'malley yang grey chief
## 2 2 1 6 3
## ellis grey susan grey bailey izzie altman
## 3 5 7 4 1
## arizona colin preston kepner addison
## 2 1 1 2 2
## nancy olivia mrs. seabury adele thatch grey
## 2 2 2 3 5
## tucker hank denny finn steve
## 7 4 4 6 6
## ben avery
## 7 2
Plot the graph with unique color for each community accordingly
plot(g, vertex.size=7, #vertex.label=NA,
vertex.color=memb1, asp=FALSE)
There is 7 communities
length(unique(memb1))## [1] 7
The size of each community
t1 <- as.data.frame(table(memb1))
colnames(t1) <- c('ID' , 'Size')
t1## ID Size
## 1 1 5
## 2 2 13
## 3 3 3
## 4 4 3
## 5 5 2
## 6 6 3
## 7 7 3
The modularity
gc1$modularity## [1] 0.00000000 -0.01730106 0.01081313 0.03676469 0.06487888
## [6] 0.09256054 0.12024221 0.14749134 0.17387544 0.19982699
## [11] 0.22837371 0.25692043 0.28460205 0.31185120 0.33910033
## [16] 0.36678201 0.39489621 0.42171276 0.44939446 0.45544982
## [21] 0.48226649 0.47923881 0.49567476 0.48875433 0.49394464
## [26] 0.51470590 0.48269898 0.50562286 0.45804498 0.30449831
## [31] 0.00000000 0.00000000
library(igraph)
library(twitteR)
library(tm)## Warning: package 'tm' was built under R version 3.4.3
## Loading required package: NLP
library(httr)##
## Attaching package: 'httr'
## The following object is masked from 'package:NLP':
##
## content
Set twitter keys
consumer_key <- "Y0NniYiJCKL7qqbrreh6p9P4F"
consumer_secret <- "yZlDnWZEB20LrrdVPXwTIY6skmuj9N3iljcO3cGvugrwSJlhYu"
access_token <- "945458627669786624-LsmOz4oCzo0lT6UHwSkPv6hT0inP47x"
access_secret <- "2jwReQYUJHFTDAjQhs0y3Yt1v4MAO2zyAddddBXMVJF4N"Set up the OAuth credentials for a twitteR session
sig <- setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)## [1] "Using direct authentication"
## Warning in strptime(x, fmt, tz = "GMT"): unknown timezone 'zone/tz/2017c.
## 1.0/zoneinfo/Asia/Jerusalem'
tweets <- searchTwitter("#bitcoin", n=200 , lang = "en" , since = "2017-12-01")convert the tweets to dataFrame
tweetsDf <- twListToDF(tweets)
summary(tweetsDf)## text favorited favoriteCount replyToSN
## Length:200 Mode :logical Min. :0.000 Length:200
## Class :character FALSE:200 1st Qu.:0.000 Class :character
## Mode :character Median :0.000 Mode :character
## Mean :0.085
## 3rd Qu.:0.000
## Max. :4.000
## created truncated replyToSID
## Min. :2017-12-26 01:52:20 Mode :logical Length:200
## 1st Qu.:2017-12-26 01:55:00 FALSE:163 Class :character
## Median :2017-12-26 01:57:01 TRUE :37 Mode :character
## Mean :2017-12-26 01:57:10
## 3rd Qu.:2017-12-26 01:59:48
## Max. :2017-12-26 02:01:14
## id replyToUID statusSource
## Length:200 Length:200 Length:200
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## screenName retweetCount isRetweet retweeted
## Length:200 Min. : 0.00 Mode :logical Mode :logical
## Class :character 1st Qu.: 0.00 FALSE:107 FALSE:200
## Mode :character Median : 0.00 TRUE :93
## Mean : 69.61
## 3rd Qu.: 30.25
## Max. :1957.00
## longitude latitude
## Mode:logical Mode:logical
## NA's:200 NA's:200
##
##
##
##
Get users that publish the tweets
users <- twListToDF(lookupUsers(tweetsDf$screenName))Our vertexes are user's names and the edge between two users means that their account created on the same month
library(lubridate)##
## Attaching package: 'lubridate'
## The following object is masked from 'package:igraph':
##
## %--%
## The following object is masked from 'package:base':
##
## date
users1Edge <- c()
users2Edge <- c()
for(i in 1:nrow(users))
{
for(j in 1:nrow(users))
{
user1 <- users[i,]
user2 <- users[j,]
user1Month <- month(as.POSIXlt(user1$created, format="%d/%m/%Y"))
user2Month <- month(as.POSIXlt(user2$created, format="%d/%m/%Y"))
if((user1$screenName != user2$screenName) && user1Month == user2Month)
{
users1Edge <- c(users1Edge , user1$screenName)
users2Edge <- c(users2Edge , user2$screenName)
}
}
}Create file from users1Edge and users2Edge and read the file to graph
res <- cbind(from = users1Edge , to = users2Edge)
write.csv(res , file = "part2/tweets.csv" , row.names = FALSE)
ga.data <- read.csv('part2/tweets.csv', header = T)
g <- graph.data.frame(ga.data,directed = F)
plot(g, vertex.size=7, vertex.label=NA, asp=FALSE)
between <- betweenness(g)
between[which.max(between)]## topfashionideas
## 0
close <- closeness(g)
close[which.max(close)]## abidoank12
## 3.555682e-05
eig <- eigen_centrality(g)
eig$vector[which.max(eig$vector)]## Ragnarly
## 1
gc <- edge.betweenness.community(g)
gc## IGRAPH clustering edge betweenness, groups: 12, mod: 0.89
## + groups:
## $`1`
## [1] "topfashionideas" "CryptoJauregui" "Polite_Jerk"
## [4] "BitcoinOps" "khichariya1" "darren_yan"
## [7] "dodadopp" "LudwigsenAngila" "sydni519"
## [10] "socialprnews" "RandallGoulding" "hitjo"
## [13] "CryptoW0rld"
##
## $`2`
## [1] "EvaBlaisdell" "eiyeorch" "Torosernjacks" "mvasey"
## [5] "CogitoErgoCode" "TAHARARALAN" "BTCticker" "coinstats"
## + ... omitted several groups/vertices
memb <- membership(gc)
memb## topfashionideas EvaBlaisdell CryptoJauregui eiyeorch
## 1 2 1 2
## CryptoPrices_ cryptananda Coinnnnn_ reubeng0
## 3 4 3 5
## StreamIn_ Polite_Jerk Torosernjacks FeesBitcoin
## 6 1 2 5
## Crypto_Newz CryptoRidiculo wavesprice block_bit
## 4 6 7 8
## abidoank12 vkeyxyz CityofInvestmnt sabbir1133
## 9 10 9 11
## whaleclubco HarrietteFarkas mvasey rottenwheel
## 6 8 2 4
## strange10change CogitoErgoCode BtcPulse TommyeKirkendal
## 9 2 5 8
## coinradar_io TAHARARALAN dc77ae00b817435 chrisleu2
## 9 2 7 10
## BitcoinOps Cryptonic17 BitcoinSpreads BTCticker
## 1 8 6 2
## btcreports betbtc crypto_rush TheeJimmyCox
## 8 5 8 8
## CryptoSeven BitcoinKacDolar coinstats UnconfirmedTxns
## 9 11 2 8
## BTCdominance digitaljournal Anon_Emy TboneMacdonald
## 6 7 10 5
## Chey999 bitstein antoni161273 Bitmoeda
## 9 10 5 2
## steveouttrim Ragnarly khichariya1 CHP_the_RIPPER
## 10 9 1 5
## latigo661 GlencieR Crypto_Monkeyo Nationalacrobat
## 6 12 8 9
## blkchdemcy btcbeehive Bitcoin_UAE BitcoinUkNews
## 7 5 8 3
## miamipeoria CryptoStacking tommyalvarez81 jmvillegast
## 5 9 7 2
## CarlaCoinsNLegs tigzorr adam3us bitcoin_miner_
## 5 4 5 9
## ibrahimsilence ESellhamer MIParentalRlght henrynburton
## 3 6 5 7
## mktm9871 darren_yan bitcoinnewsweb juliarahma1995
## 12 1 8 12
## blockchainbot maestrejoseg BitcoinInvest24 Soc_Currency
## 5 5 6 8
## socialstocksnow iam_rahul555 PatPrivilege dodadopp
## 7 11 11 1
## s_H4k4 LudwigsenAngila eprocentteam bitcointonic
## 11 1 10 8
## socmrktng socialstartnow nowsocialinc socialnewstweet
## 6 9 12 8
## getitoutnow socmediaimpact Just_JUNO FTS_Billing
## 11 8 8 12
## Kandy_LOVE_94 politicalHEDGE jeffvillalobos3 RTrobby
## 9 12 6 9
## steemero BitcoinWrld BrutallyDoke paleodead
## 11 10 9 3
## sydni519 bitcoinfirehose CryptNotBlood socialprnews
## 1 9 6 1
## socialirnews RandallGoulding ssn3media socialstartnews
## 9 1 4 3
## ssn1tweet ssn4marketing GetOvarIt 1JustinMcCollum
## 7 11 9 9
## hotload2000 MzCh11KiETHaNg9 KovshBeats roni20731
## 9 4 4 12
## Kayy_Kayy_44 ondhro1 UndersHead TheSideHusband
## 5 8 2 8
## goldseek btcmrkts realSatoshiN egaconsulting
## 12 12 5 6
## findingreview davidar12 g0t3nk5 NeilOKeefe
## 6 12 3 10
## 37angelsny King_Tuesday hexagram_power xbtnews
## 11 4 6 4
## leola_joergen KalEl_1987 Crystal0182 cryptomiao
## 12 9 6 6
## MdKayumUddin2 BASSLINE_BOP2 CryptoNewswire midousujikun
## 3 2 3 3
## jekiedugn1 btc_update matslats TheBlockchain
## 4 9 2 8
## OmegaLuther VeryVeriViral eStream_Studios eBargainsToday
## 12 7 6 12
## TheBitForum NazzyN21 MikeAlden2012 DMVLife1
## 11 11 9 11
## mempool_stats Buddhamangler hitjo djprincealby
## 5 2 1 10
## fakent_ TugBoatTrader CryptoW0rld CA_Minho25
## 10 10 1 6
## shushmashri21 markuspdee JacekSalaj tell_taylor
## 6 12 5 11
## Priya_upala MattLeft99 theonevortex CryptoKid77
## 6 2 4 5
## BitcoinCashApp michael55038689 McclamNeva
## 5 6 4
Plot the graph with unique color for each community accordingly
plot(g, vertex.size=7, #vertex.label=NA,
vertex.color=memb, asp=FALSE)
There is 12 communities
length(unique(memb))## [1] 12
The size of each community
t <- as.data.frame(table(memb))
colnames(t) <- c('ID' , 'Size')
t## ID Size
## 1 1 13
## 2 2 15
## 3 3 10
## 4 4 12
## 5 5 20
## 6 6 21
## 7 7 9
## 8 8 19
## 9 9 22
## 10 10 11
## 11 11 13
## 12 12 14
The modularity
gc$modularity## [1] -0.006045729 -0.005406431 -0.004701871 -0.004073179 -0.002794583
## [6] -0.001537197 0.000348881 0.001059152 0.002479693 0.003177998
## [11] 0.003862164 0.005780058 0.006478363 0.007874975 0.009969892
## [16] 0.010661400 0.013176171 0.014559186 0.015927518 0.016545061
## [21] 0.017954182 0.018669619 0.020722116 0.022152990 0.025296454
## [26] 0.029068610 0.030303698 0.032378221 0.034491901 0.038892750
## [31] 0.041710991 0.044504214 0.046635025 0.048781337 0.051547367
## [36] 0.056576909 0.062235144 0.064792336 0.068249874 0.072398920
## [41] 0.073048279 0.075784941 0.077083660 0.080280149 0.085120702
## [46] 0.088643503 0.092870863 0.094723495 0.097193670 0.100281389
## [51] 0.103772918 0.104492978 0.107354726 0.112286647 0.113683258
## [56] 0.117388521 0.119483439 0.125770366 0.130093173 0.132886396
## [61] 0.136463582 0.141403932 0.144245014 0.148434849 0.153992743
## [66] 0.160908363 0.166440424 0.172615862 0.178839431 0.180787508
## [71] 0.183384945 0.188273085 0.191519881 0.198434957 0.205227939
## [76] 0.212772252 0.216668407 0.220219760 0.223711289 0.229297735
## [81] 0.233487570 0.239772322 0.246755380 0.248195500 0.252741013
## [86] 0.260914019 0.264749806 0.269224891 0.276831476 0.282026349
## [91] 0.286914489 0.295212581 0.297372760 0.302487143 0.311288842
## [96] 0.315581465 0.322991990 0.328745671 0.334382152 0.340775130
## [101] 0.344195959 0.347076198 0.355104267 0.362136544 0.367144604
## [106] 0.372988836 0.380660410 0.384260710 0.392906323 0.399399914
## [111] 0.408389513 0.417819905 0.426130777 0.431717223 0.440980380
## [116] 0.445242003 0.449346997 0.458297167 0.463269061 0.473149762
## [121] 0.482739230 0.488421394 0.492741753 0.502800837 0.509943787
## [126] 0.514732947 0.522414311 0.532912555 0.540704865 0.549146533
## [131] 0.559375299 0.568466327 0.573939652 0.584627429 0.594367816
## [136] 0.600652568 0.607045003 0.612085422 0.618242913 0.629358701
## [141] 0.639748447 0.650616510 0.657458167 0.669191499 0.675532540
## [146] 0.683058362 0.694097467 0.705413937 0.713793606 0.726144482
## [151] 0.733247187 0.746215607 0.757904071 0.763664550 0.775609712
## [156] 0.782655313 0.794162675 0.802372663 0.809355720 0.821929576
## [161] 0.834076235 0.839799732 0.847549893 0.853988827 0.862882980
## [166] 0.870564344 0.880142663 0.888522333
gc1 <- walktrap.community(g)
gc1## IGRAPH clustering walktrap, groups: 12, mod: 0.89
## + groups:
## $`1`
## [1] "wavesprice" "dc77ae00b817435" "digitaljournal"
## [4] "blkchdemcy" "tommyalvarez81" "henrynburton"
## [7] "socialstocksnow" "ssn1tweet" "VeryVeriViral"
##
## $`2`
## [1] "CryptoPrices_" "Coinnnnn_" "BitcoinUkNews"
## [4] "ibrahimsilence" "paleodead" "socialstartnews"
## [7] "g0t3nk5" "MdKayumUddin2" "CryptoNewswire"
## [10] "midousujikun"
## + ... omitted several groups/vertices
memb1 <- membership(gc1)
memb1## topfashionideas EvaBlaisdell CryptoJauregui eiyeorch
## 5 8 5 8
## CryptoPrices_ cryptananda Coinnnnn_ reubeng0
## 2 4 2 10
## StreamIn_ Polite_Jerk Torosernjacks FeesBitcoin
## 11 5 8 10
## Crypto_Newz CryptoRidiculo wavesprice block_bit
## 4 11 1 9
## abidoank12 vkeyxyz CityofInvestmnt sabbir1133
## 12 3 12 6
## whaleclubco HarrietteFarkas mvasey rottenwheel
## 11 9 8 4
## strange10change CogitoErgoCode BtcPulse TommyeKirkendal
## 12 8 10 9
## coinradar_io TAHARARALAN dc77ae00b817435 chrisleu2
## 12 8 1 3
## BitcoinOps Cryptonic17 BitcoinSpreads BTCticker
## 5 9 11 8
## btcreports betbtc crypto_rush TheeJimmyCox
## 9 10 9 9
## CryptoSeven BitcoinKacDolar coinstats UnconfirmedTxns
## 12 6 8 9
## BTCdominance digitaljournal Anon_Emy TboneMacdonald
## 11 1 3 10
## Chey999 bitstein antoni161273 Bitmoeda
## 12 3 10 8
## steveouttrim Ragnarly khichariya1 CHP_the_RIPPER
## 3 12 5 10
## latigo661 GlencieR Crypto_Monkeyo Nationalacrobat
## 11 7 9 12
## blkchdemcy btcbeehive Bitcoin_UAE BitcoinUkNews
## 1 10 9 2
## miamipeoria CryptoStacking tommyalvarez81 jmvillegast
## 10 12 1 8
## CarlaCoinsNLegs tigzorr adam3us bitcoin_miner_
## 10 4 10 12
## ibrahimsilence ESellhamer MIParentalRlght henrynburton
## 2 11 10 1
## mktm9871 darren_yan bitcoinnewsweb juliarahma1995
## 7 5 9 7
## blockchainbot maestrejoseg BitcoinInvest24 Soc_Currency
## 10 10 11 9
## socialstocksnow iam_rahul555 PatPrivilege dodadopp
## 1 6 6 5
## s_H4k4 LudwigsenAngila eprocentteam bitcointonic
## 6 5 3 9
## socmrktng socialstartnow nowsocialinc socialnewstweet
## 11 12 7 9
## getitoutnow socmediaimpact Just_JUNO FTS_Billing
## 6 9 9 7
## Kandy_LOVE_94 politicalHEDGE jeffvillalobos3 RTrobby
## 12 7 11 12
## steemero BitcoinWrld BrutallyDoke paleodead
## 6 3 12 2
## sydni519 bitcoinfirehose CryptNotBlood socialprnews
## 5 12 11 5
## socialirnews RandallGoulding ssn3media socialstartnews
## 12 5 4 2
## ssn1tweet ssn4marketing GetOvarIt 1JustinMcCollum
## 1 6 12 12
## hotload2000 MzCh11KiETHaNg9 KovshBeats roni20731
## 12 4 4 7
## Kayy_Kayy_44 ondhro1 UndersHead TheSideHusband
## 10 9 8 9
## goldseek btcmrkts realSatoshiN egaconsulting
## 7 7 10 11
## findingreview davidar12 g0t3nk5 NeilOKeefe
## 11 7 2 3
## 37angelsny King_Tuesday hexagram_power xbtnews
## 6 4 11 4
## leola_joergen KalEl_1987 Crystal0182 cryptomiao
## 7 12 11 11
## MdKayumUddin2 BASSLINE_BOP2 CryptoNewswire midousujikun
## 2 8 2 2
## jekiedugn1 btc_update matslats TheBlockchain
## 4 12 8 9
## OmegaLuther VeryVeriViral eStream_Studios eBargainsToday
## 7 1 11 7
## TheBitForum NazzyN21 MikeAlden2012 DMVLife1
## 6 6 12 6
## mempool_stats Buddhamangler hitjo djprincealby
## 10 8 5 3
## fakent_ TugBoatTrader CryptoW0rld CA_Minho25
## 3 3 5 11
## shushmashri21 markuspdee JacekSalaj tell_taylor
## 11 7 10 6
## Priya_upala MattLeft99 theonevortex CryptoKid77
## 11 8 4 10
## BitcoinCashApp michael55038689 McclamNeva
## 10 11 4
Plot the graph with unique color for each community accordingly
plot(g, vertex.size=7, #vertex.label=NA,
vertex.color=memb1, asp=FALSE)
There is 12 communities
length(unique(memb1))## [1] 12
The size of each community
t1 <- as.data.frame(table(memb1))
colnames(t1) <- c('ID' , 'Size')
t1## ID Size
## 1 1 9
## 2 2 10
## 3 3 11
## 4 4 12
## 5 5 13
## 6 6 13
## 7 7 14
## 8 8 15
## 9 9 19
## 10 10 20
## 11 11 21
## 12 12 22
The modularity
gc1$modularity## [1] 0.000000000 -0.005428182 -0.004810639 -0.004193096 -0.002958009
## [6] -0.002340465 -0.001722922 0.001982341 0.003217428 0.005687603
## [11] 0.008775322 0.016185848 0.042122684 0.042740226 0.043357771
## [16] 0.043975312 0.045210402 0.046445489 0.048298120 0.052003384
## [21] 0.064354256 0.136606887 0.137235567 0.137864262 0.138492942
## [26] 0.139750332 0.142265111 0.142893806 0.150438130 0.151695505
## [31] 0.156096354 0.156725034 0.157353729 0.157982409 0.159239799
## [36] 0.160497189 0.162383273 0.164898038 0.179986671 0.193817914
## [41] 0.218336940 0.268632352 0.269271642 0.269910932 0.270550221
## [46] 0.271189511 0.271828800 0.272468090 0.275025308 0.276303917
## [51] 0.281418324 0.285254121 0.292925686 0.318497598 0.326808482
## [56] 0.335758656 0.345348120 0.355576873 0.366444945 0.377952278
## [61] 0.390098989 0.390748352 0.391397715 0.392047077 0.392696440
## [66] 0.393995166 0.394644529 0.395943224 0.396592617 0.397241980
## [71] 0.401138127 0.402436823 0.403735518 0.413475901 0.417372048
## [76] 0.427761793 0.437502176 0.489450932 0.501139402 0.501823545
## [81] 0.503191888 0.505244374 0.507981002 0.511401892 0.512086034
## [86] 0.512770176 0.513454318 0.514138460 0.515506804 0.518243492
## [91] 0.522348464 0.536031783 0.572976768 0.573668301 0.574359834
## [96] 0.575051367 0.575742900 0.577125907 0.579891860 0.581274807
## [101] 0.587498426 0.604094625 0.611009717 0.618616283 0.626914382
## [106] 0.635903955 0.636602283 0.637300611 0.637998939 0.638697267
## [111] 0.639395595 0.640093923 0.641490519 0.642887056 0.647076905
## [116] 0.651266754 0.658249795 0.665232837 0.665931165 0.666629493
## [121] 0.667327821 0.668026149 0.669422686 0.672215879 0.676405728
## [126] 0.679198980 0.703639686 0.728080392 0.736460030 0.744839728
## [131] 0.745544314 0.746953428 0.747658014 0.748362601 0.749067187
## [136] 0.751180828 0.752589941 0.758226395 0.760340035 0.768794775
## [141] 0.791340709 0.792050958 0.792761207 0.793471515 0.794892073
## [146] 0.797733188 0.800574243 0.811228275 0.811938524 0.813359082
## [151] 0.830405593 0.831121087 0.832551897 0.833267391 0.835413694
## [156] 0.836129129 0.837559998 0.840421796 0.851153314 0.862600267
## [161] 0.863320351 0.864040434 0.864760518 0.867640734 0.869080842
## [166] 0.871961057 0.875561357 0.888522446 0.000000000 0.000000000
## [171] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## [176] 0.000000000 0.000000000 0.000000000 0.000000000