May 11, 2018
Extended family names. Pajek description as multi-relational network.
Different ordering of original vector data.
> library(sna)
> test <- read.paj("http://vlado.fmf.uni-lj.si/pub/networks/data/GD/gd98/A98.net")
> plot(test,main=test%n%'title')
>
> netM <- read.paj("C:/Users/batagelj/Documents/papers/2018/moskva/NetR/nets/padgett/padgettM.net")
> plot(netM,main=netM%n%'title')
> setwd("C:/Users/batagelj/Documents/papers/2018/moskva/NetR/nets/padgett")
> netB <- read.paj("padgettB.net")
> plot(netB,main=netB%n%'title')
> wealth <- read.csv("wealth.vec",skip=2,header=FALSE)
> plot(netB,main=netB%n%'title',label=netB%v%"vertex.names",vertex.cex=0.05*wealth$V1)
>
> plot(netM,main=netM%n%'title',label=netM%v%"vertex.names",vertex.cex=0.05*wealth$V1)
>
> nacf(netM,wealth$V1,type="moran",mode="graph")[2]
1
-0.3107353
> nacf(netM,wealth$V1,type="geary",mode="graph")[2]
1
1.683607
> cugBetSize <- cug.test(netB,
+ centralization,
+ FUN.arg=list(FUN=betweenness),
+ mode="graph",
+ cmode="size")
> cugBetEdges <- cug.test(netB,
+ centralization,
+ FUN.arg=list(FUN=betweenness),
+ mode="graph",
+ cmode="edges")
> cugBetDyad <- cug.test(netB,
+ centralization,
+ FUN.arg=list(FUN=betweenness),
+ mode="graph",
+ cmode="dyad.census")
> # Aggregate the findings...if you prefer.
> Betweenness <- c(cugBetSize$obs.stat, cugBetEdges$obs.stat, cugBetDyad$obs.stat)
> PctGreater <- c(cugBetSize$pgteobs, cugBetEdges$pgteobs, cugBetDyad$pgteobs)
> PctLess <- c(cugBetSize$plteobs, cugBetEdges$plteobs, cugBetDyad$plteobs)
> Betweenness <- cbind(Betweenness, PctGreater, PctLess)
> rownames(Betweenness) <- c("Size", "Edges", "Dyads")
> Betweenness
Betweenness PctGreater PctLess
Size 0.2057143 0.000 1.000
Edges 0.2057143 0.727 0.274
Dyads 0.2057143 0.728 0.273
>
>
> par(mfrow=c(1,3))
> plot(cugBetSize, main="Betweenness \nConditioned on Size" )
> plot(cugBetEdges, main="Betweenness \nConditioned on Edges" )
> plot(cugBetDyad, main="Betweenness \nConditioned on Dyads" )
> par(mfrow=c(1,1))
>
>
> # Get the Correlation value
> gcor(netB,netM)
[1] 0.3718679
> # Is it significant?
> pCor <- qaptest(list(netB,netM),gcor,g1=1,g2=2,reps=1000)
> pCor
QAP Test Results
Estimated p-values:
p(f(perm) >= f(d)): 0
p(f(perm) <= f(d)): 1
> plot(pCor, xlim=c(-0.25, 0.4))
>
> party <- read.csv("party.clu",skip=2,header=FALSE)$V1
> party
[1] 1 3 3 2 2 1 2 2 1 1 2 1 1 3 2 1