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2.4_BERGM_analysis_null.R
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2.4_BERGM_analysis_null.R
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########################################################################################################################
## GOSSIP IN HUNGARIAN HIGH SCHOOLS
## BERGM analysis (2.4)
## R script written by Jose Luis Estevez (Linkoping University)
## Date: September 1st, 2021
########################################################################################################################
# R PACKAGES REQUIRED
library(Bergm)
########################################################################################################################
# DATA LOADING
rm(list=ls())
load('tidieddata.RData')
# PRELIMINARY
# Removal of the diagonal in all matrices but gossip
for(wave in seq_along(networks)){
for(item in c('friend','dislike','otherslookup','othersscorn')){
for(room in seq_along(networks[[wave]][[item]])){
diag(networks[[wave]][[item]][[room]]) <- NA
}
}
}
# NAs to zeroes in predictors
for(wave in seq_along(networks)){
for(item in c('friend','dislike','otherslookup','othersscorn')){
for(room in seq_along(networks[[wave]][[item]])){
networks[[wave]][[item]][[room]][is.na(networks[[wave]][[item]][[room]])] <- 0
}
}
}
# 2nd-degree dislike and shared dislike
for(wave in seq_along(networks)){
networks[[wave]]$shared_dislike <- networks[[wave]]$dislike_2 <- networks[[wave]]$dislike
for(room in seq_along(networks[[wave]]$gossip)){
# being friends with somebody who dislikes the target
networks[[wave]]$dislike_2[[room]] <- 1*(networks[[wave]]$friend[[room]] %*% networks[[wave]]$dislike[[room]]!=0)
# shared dislike with somebody else
networks[[wave]]$shared_dislike[[room]] <- 1*(networks[[wave]]$dislike[[room]]+networks[[wave]]$dislike_2[[room]]==2)
}
}
# Students with unknown ethnicity sent to non-Roma
for(wave in seq_along(students)){
for(i in 1:nrow(students[[wave]])){
if(is.na(students[[wave]]$roma[i])){
students[[wave]]$roma[i] <- 0
}
}
}
# Creation of popularity, and popularity square
for(wave in seq_along(networks)){
networks[[wave]]$popular2 <- networks[[wave]]$popular <- networks[[wave]]$friend
for(room in seq_along(networks[[wave]]$gossip)){
# popularity
networks[[wave]]$popular[[room]] <- as.numeric(scale(colSums(networks[[wave]]$popular[[room]],na.rm=TRUE),
center=TRUE))
# popularity^2
networks[[wave]]$popular2[[room]] <- as.numeric(scale(networks[[wave]]$popular[[room]]^2,center=TRUE))
}
}
# Gossip into a network object, and addition of nodes' attributes (gender, roma, popular, sb_disliked, and sh_dislike):
for(wave in seq_along(networks)){
for(room in names(networks[[wave]]$gossip)){
networks[[wave]]$gossip[[room]] <- network(networks[[wave]]$gossip[[room]],directed=TRUE)
network::set.vertex.attribute(networks[[wave]]$gossip[[room]],'gender',
students[[wave]][students[[wave]]$class == room,]$female)
network::set.vertex.attribute(networks[[wave]]$gossip[[room]],'roma',
students[[wave]][students[[wave]]$class == room,]$roma)
network::set.vertex.attribute(networks[[wave]]$gossip[[room]],'popular',networks[[wave]]$popular[[room]])
network::set.vertex.attribute(networks[[wave]]$gossip[[room]],'popular2',networks[[wave]]$popular2[[room]])
}
}
########################################################################################################################
# Possible specifications: with/without gender, with/without ethnicity
specifications <- vector('list',length(networks))
names(specifications) <- names(networks)
for(wave in seq_along(specifications)){
specifications[[wave]] <- data.frame(row.names=names(networks[[wave]]$gossip))
}
for(wave in seq_along(specifications)){
for(room in rownames(specifications[[wave]])){
specifications[[wave]][room,'roma'] <- sum(students[[wave]][students[[wave]]$class==room,]$roma==1)
specifications[[wave]][room,'no_roma'] <- sum(students[[wave]][students[[wave]]$class==room,]$roma==0)
specifications[[wave]][room,'females'] <- sum(students[[wave]][students[[wave]]$class==room,]$female==1)
specifications[[wave]][room,'males'] <- sum(students[[wave]][students[[wave]]$class==room,]$female==0)
specifications[[wave]][room,'ethnicity'] <- ifelse(specifications[[wave]][[room,'roma']] >= 3 &
specifications[[wave]][[room,'no_roma']] >= 3,TRUE,FALSE)
specifications[[wave]][room,'gender'] <- ifelse(specifications[[wave]][[room,'females']] >= 3 &
specifications[[wave]][[room,'males']] >= 3,TRUE,FALSE)
}
}
# ERGM descriptive
bergm_descript <- vector('list',length(networks))
names(bergm_descript) <- names(networks)
for(wave in seq_along(bergm_descript)){
bergm_descript[[wave]] <- vector('list',length(networks[[wave]]$gossip))
names(bergm_descript[[wave]]) <- names(networks[[wave]]$gossip)
}
for(wave in seq_along(bergm_descript)){
for(room in seq_along(bergm_descript[[wave]])){
if(specifications[[wave]][[room,'ethnicity']] == TRUE & specifications[[wave]][[room,'gender']] == TRUE){
bergm_descript[[wave]][[room]] <- summary(networks[[wave]]$gossip[[room]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
nodeofactor('gender')+nodeifactor('gender')+nodematch('gender',diff=FALSE)+
nodeifactor('roma')+
nodeocov('popular')+nodeocov('popular2')+
edgecov(networks[[wave]]$dislike[[room]])+
edgecov(networks[[wave]]$dislike_2[[room]])+
edgecov(networks[[wave]]$shared_dislike[[room]])+
edgecov(networks[[wave]]$otherslookup[[room]])+
edgecov(networks[[wave]]$othersscorn[[room]]))
}else if(specifications[[wave]][[room,'roma']] == FALSE){
bergm_descript[[wave]][[room]] <- summary(networks[[wave]]$gossip[[room]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
nodeofactor('gender')+nodeifactor('gender')+nodematch('gender',diff=FALSE)+
nodeocov('popular')+nodeocov('popular2')+
edgecov(networks[[wave]]$dislike[[room]])+
edgecov(networks[[wave]]$dislike_2[[room]])+
edgecov(networks[[wave]]$shared_dislike[[room]])+
edgecov(networks[[wave]]$otherslookup[[room]])+
edgecov(networks[[wave]]$othersscorn[[room]]))
}else if(specifications[[wave]][[room,'gender']] == FALSE){
bergm_descript[[wave]][[room]] <- summary(networks[[wave]]$gossip[[room]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
nodeifactor('roma')+
nodeocov('popular')+nodeocov('popular2')+
edgecov(networks[[wave]]$dislike[[room]])+
edgecov(networks[[wave]]$dislike_2[[room]])+
edgecov(networks[[wave]]$shared_dislike[[room]])+
edgecov(networks[[wave]]$otherslookup[[room]])+
edgecov(networks[[wave]]$othersscorn[[room]]))
}
}
bergm_descript[[wave]] <- lapply(bergm_descript[[wave]],data.frame)
}
########################################################################################################################
# BERGM ANALYSIS
# Object for results
bergm_results <- vector('list',length(networks))
names(bergm_results) <- names(networks)
for(wave in seq_along(bergm_results)){
bergm_results[[wave]] <- vector('list',length(networks[[wave]]$gossip))
names(bergm_results[[wave]]) <- names(networks[[wave]]$gossip)
}
### TIME 1 ###
# classroom 2200
set.seed(0708)
bergm_results[[1]][[1]] <- bergmM(networks[[1]]$gossip[[1]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[1]])+
edgecov(networks[[1]]$dislike_2[[1]])+
edgecov(networks[[1]]$shared_dislike[[1]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 3400 (no gender)
set.seed(0708)
bergm_results[[1]][[2]] <- bergmM(networks[[1]]$gossip[[2]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[2]])+
edgecov(networks[[1]]$dislike_2[[2]])+
edgecov(networks[[1]]$shared_dislike[[2]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6100
set.seed(0708)
bergm_results[[1]][[3]] <- bergmM(networks[[1]]$gossip[[3]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[3]])+
edgecov(networks[[1]]$dislike_2[[3]])+
edgecov(networks[[1]]$shared_dislike[[3]]),
burn.in=1000,main.iters=5000,gamma=0.3,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6300 (no ethnicity)
set.seed(0708)
bergm_results[[1]][[4]] <- bergmM(networks[[1]]$gossip[[4]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[4]])+
edgecov(networks[[1]]$dislike_2[[4]])+
edgecov(networks[[1]]$shared_dislike[[4]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 7100 (no mutual ties and no gender)
set.seed(0708)
bergm_results[[1]][[5]] <- bergmM(networks[[1]]$gossip[[5]]~
edges+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[5]])+
edgecov(networks[[1]]$dislike_2[[5]])+
edgecov(networks[[1]]$shared_dislike[[5]]),
burn.in=1000,main.iters=5000,gamma=0.30,
prior.mean=c(rep(0,9)),prior.sigma=diag(5,9))
# classroom 7800
set.seed(0708)
bergm_results[[1]][[6]] <- bergmM(networks[[1]]$gossip[[6]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[1]]$dislike[[6]])+
edgecov(networks[[1]]$dislike_2[[6]])+
edgecov(networks[[1]]$shared_dislike[[6]]),
burn.in=1000,main.iters=5000,gamma=0.25,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
### TIME 2 ###
# classroom 1100 (no ethnicity)
set.seed(0708)
bergm_results[[2]][[1]] <- bergmM(networks[[2]]$gossip[[1]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[1]])+
edgecov(networks[[2]]$dislike_2[[1]])+
edgecov(networks[[2]]$shared_dislike[[1]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 5400 (no mutual ties)
set.seed(0708)
bergm_results[[2]][[2]] <- bergmM(networks[[2]]$gossip[[2]]~
edges+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[2]])+
edgecov(networks[[2]]$dislike_2[[2]])+
edgecov(networks[[2]]$shared_dislike[[2]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,9)),prior.sigma=diag(5,9))
# classroom 6200 (no ethnicity)
set.seed(0708)
bergm_results[[2]][[3]] <- bergmM(networks[[2]]$gossip[[3]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+
gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[3]])+
edgecov(networks[[2]]$dislike_2[[3]])+
edgecov(networks[[2]]$shared_dislike[[3]]),
burn.in=1000,main.iters=5000,gamma=0.25,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6300 (no ethnicity and no mutual ties)
set.seed(0708)
bergm_results[[2]][[4]] <- bergmM(networks[[2]]$gossip[[4]]~
edges+
gwodegree(decay=log(2),fixed=TRUE)+
gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[4]])+
edgecov(networks[[2]]$dislike_2[[4]])+
edgecov(networks[[2]]$shared_dislike[[4]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,9)),prior.sigma=diag(5,9))
# classroom 6400 (no ethnicity)
set.seed(0708)
bergm_results[[2]][[5]] <- bergmM(networks[[2]]$gossip[[5]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+
gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[5]])+
edgecov(networks[[2]]$dislike_2[[5]])+
edgecov(networks[[2]]$shared_dislike[[5]]),
burn.in=1000,main.iters=5000,gamma=0.3,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 7600 (no gender and no sinks)
set.seed(0708)
bergm_results[[2]][[6]] <- bergmM(networks[[2]]$gossip[[6]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[2]]$dislike[[6]])+
edgecov(networks[[2]]$dislike_2[[6]])+
edgecov(networks[[2]]$shared_dislike[[6]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,9)),prior.sigma=diag(5,9))
### TIME 3 ###
# classroom 2100 (no gender & no sinks)
set.seed(0708)
bergm_results[[3]][[1]] <- bergmM(networks[[3]]$gossip[[1]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[3]]$dislike[[1]])+
edgecov(networks[[3]]$dislike_2[[1]])+
edgecov(networks[[3]]$shared_dislike[[1]]),
burn.in=1000,main.iters=5000,gamma=0.33,
prior.mean=c(rep(0,9)),prior.sigma=diag(5,9))
# classroom 5100 (no gender)
set.seed(0708)
bergm_results[[3]][[2]] <- bergmM(networks[[3]]$gossip[[2]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[3]]$dislike[[2]])+
edgecov(networks[[3]]$dislike_2[[2]])+
edgecov(networks[[3]]$shared_dislike[[2]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6100 (no ethnicity)
set.seed(0708)
bergm_results[[3]][[3]] <- bergmM(networks[[3]]$gossip[[3]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[3]]$dislike[[3]])+
edgecov(networks[[3]]$dislike_2[[3]])+
edgecov(networks[[3]]$shared_dislike[[3]]),
burn.in=1000,main.iters=5000,gamma=0.4,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6200 (no ethnicity)
set.seed(0708)
bergm_results[[3]][[4]] <- bergmM(networks[[3]]$gossip[[4]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[3]]$dislike[[4]])+
edgecov(networks[[3]]$dislike_2[[4]])+
edgecov(networks[[3]]$shared_dislike[[4]]),
burn.in=1000,main.iters=5000,gamma=0.3,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
# classroom 6400 (no ethnicity)
set.seed(0708)
bergm_results[[3]][[5]] <- bergmM(networks[[3]]$gossip[[5]]~
edges+mutual+
gwodegree(decay=log(2),fixed=TRUE)+gwidegree(decay=log(2),fixed=TRUE)+
odegree(d=0)+
dgwdsp(decay=log(2),fixed=TRUE,type='OTP')+
dgwesp(decay=log(2),fixed=TRUE,type='OTP')+
edgecov(networks[[3]]$dislike[[5]])+
edgecov(networks[[3]]$dislike_2[[5]])+
edgecov(networks[[3]]$shared_dislike[[5]]),
burn.in=1000,main.iters=5000,gamma=0.3,
prior.mean=c(rep(0,10)),prior.sigma=diag(5,10))
########################################################################################################################
# Acceptance rate
for(wave in seq_along(bergm_results)){
for(room in seq_along(bergm_results[[wave]])){
print(bergm_results[[wave]][[room]]$AR)
}
}
# Save image
save.image('bergm_results_null.RData')
########################################################################################################################
# Bayesian GOF
bayesian_gof <- vector('list',length(networks))
names(bayesian_gof) <- names(networks)
for(room in c(1:6)){
jpeg(filename=paste('gof_t1_',room,'.jpeg'),width=7,height=5,units='in',res=500)
bayesian_gof[[1]][[room]] <- bgof(bergm_results[[1]][[room]])
dev.off()
}
for(room in c(1:6)){
jpeg(filename=paste('gof_t2_',room,'.jpeg'),width=7,height=5,units='in',res=500)
bayesian_gof[[2]][[room]] <- bgof(bergm_results[[2]][[room]])
dev.off()
}
for(room in c(1:5)){
jpeg(filename=paste('gof_t3_',room,'.jpeg'),width=7,height=5,units='in',res=500)
bayesian_gof[[3]][[room]] <- bgof(bergm_results[[3]][[room]])
dev.off()
}