library("latentnet")

help("sampson")
help("samplike")

data("sampson")
network.vertex.names(samplike)

samplike %v% "group"

samplike.fit<-ergmm(samplike~latent(d=2),tofit=c("mle"))

samplike.fit$mle$Z

oneL <- samplike %v% "group"
oneL[oneL == "Turks"] <- "T"
oneL[oneL == "outcasts"] <- "O"
oneL[oneL == "loyal"] <- "L"
oneL[c(1, 7, 15)] <- "W"
oneL

oneLcolors <- c("red", "blue", "black", "green")[match(oneL,c("T", "O", "L", "W"))]

plot(samplike.fit, label = oneL, vertex.col = oneLcolors, what="mle", main = "MLE positions",
print.formula=FALSE,labels=TRUE)
title(sub = "Color represents the estimated groups; Labels the Sampson's groups")

set.seed(3141)

samplike.fit <- ergmm(samplike ~ latent(d = 2, G = 3), verbose = TRUE)

summary(samplike.fit)

data("tribes")
tribes.fit<-ergmm(tribes~latent(d=2,G=3),
                family="binomial",fam.par=list(trials=2),response="sign.012",
                verbose=1)

attr(samplike.fit$sample,"Q")

plot(samplike.fit)

plot(samplike.fit,pie=TRUE,vertex.cex=2.5)

plot(samplike.fit,what="pmean")

#plot(samplike.fit,what="pmode")

plot(samplike.fit,what=4)

#for(i in 1:samplike.fit$control$sample.size){
#  plot(samplike.fit,what=i)
#  Sys.sleep(0.1)
#}

plot(tribes.fit,
     edge.col=as.matrix(tribes,"gama",m="a")*3+as.matrix(tribes,"rova",m="a")*2,pie=TRUE)

mcmc.diagnostics(samplike.fit)

mcmc.diagnostics(tribes.fit)

likemonks<-simulate(samplike.fit)
likemonks.par<-attr(likemonks,"ergmm.par")
plot(likemonks,coord=likemonks.par$Z,edge.col=8,
     vertex.col=c("red","green","blue")[likemonks.par$Z.K])

likemonks1<-with(samplike.fit,simulate(model,par=sample[[1]],prior=prior))
plot(likemonks1)

samplike.fit$mkl$Z.K

samplike.fit$mkl$Z

plot(samplike.fit,what="density")

number.to.plot <- 24
interval.to.plot <- round(dim(samplike.fit$sample$Z)[1]/number.to.plot)
for (i in 1:number.to.plot) {
  isamp <- 1 + (i - 1) * interval.to.plot
  isamp.plot <- plot(samplike, label = "",
                     vertex.col = samplike.fit$sample$Z.K[isamp, ],
                     arrowhead.cex = 0.3, vertex.cex = 2, 
                     coord = samplike.fit$sample$Z[isamp, , ],
                     main = paste("Draw number", isamp))
}


samplike.fit.gof <- gof(samplike.fit)

summary(samplike.fit.gof)

plot(samplike.fit.gof)