library(knitr)
knitr::opts_chunk$set(echo = TRUE)
opts_chunk$set(tidy.opts=list(width.cutoff=100),tidy=TRUE, warning = FALSE, message = FALSE,comment = "#>", cache=TRUE, class.source=c("test"), class.output=c("test2"))
options(width = 100)
rgl::setupKnitr()
colorize <- function(x, color) {sprintf("<span style='color: %s;'>%s</span>", color, x) }klippy::klippy(position = c('top', 'right'))#klippy::klippy(color = 'darkred')
#klippy::klippy(tooltip_message = 'Click to copy', tooltip_success = 'Done')Last compiled on oktober, 2022
### Author: NINA BRANTEN### Lastmod: 28-09-2022###
# cleanup workspace
rm(list = ls())# install packages
library(RSiena)# density: observed relations divided by possible relations
fdensity <- function(x) {
# x is your nomination network make sure diagonal cells are NA
diag(x) <- NA
# take care of RSiena structural zeros, set as missing.
x[x == 10] <- NA
sum(x == 1, na.rm = T)/(sum(x == 1 | x == 0, na.rm = T))
}
# calculate intragroup density
fdensityintra <- function(x, A) {
# A is matrix indicating whether nodes in dyad have same node attributes
diag(x) <- NA
x[x == 10] <- NA
diag(A) <- NA
sum(x == 1 & A == 1, na.rm = T)/(sum((x == 1 | x == 0) & A == 1, na.rm = T))
}
# calculate intragroup density
fdensityinter <- function(x, A) {
# A is matrix indicating whether nodes in dyad have same node attributes
diag(x) <- NA
x[x == 10] <- NA
diag(A) <- NA
sum(x == 1 & A != 1, na.rm = T)/(sum((x == 1 | x == 0) & A != 1, na.rm = T))
}
# construct dyadcharacteristic whether nodes are similar/homogenous
fhomomat <- function(x) {
# x is a vector of node-covariate
xmat <- matrix(x, nrow = length(x), ncol = length(x))
xmatt <- t(xmat)
xhomo <- xmat == xmatt
return(xhomo)
}
# a function to calculate all valid dyads.
fndyads <- function(x) {
diag(x) <- NA
x[x == 10] <- NA
(sum((x == 1 | x == 0), na.rm = T))
}
# a function to calculate all valid intragroupdyads.
fndyads2 <- function(x, A) {
diag(x) <- NA
x[x == 10] <- NA
diag(A) <- NA
(sum((x == 1 | x == 0) & A == 1, na.rm = T))
}
fscolnet <- function(network, ccovar) {
# Calculate coleman on network level:
# https://reader.elsevier.com/reader/sd/pii/S0378873314000239?token=A42F99FF6E2B750436DD2CB0DB7B1F41BDEC16052A45683C02644DAF88215A3379636B2AA197B65941D6373E9E2EE413
fhomomat <- function(x) {
xmat <- matrix(x, nrow = length(x), ncol = length(x))
xmatt <- t(xmat)
xhomo <- xmat == xmatt
return(xhomo)
}
fsumintra <- function(x, A) {
# A is matrix indicating whether nodes constituting dyad have same characteristics
diag(x) <- NA
x[x == 10] <- NA
diag(A) <- NA
sum(x == 1 & A == 1, na.rm = T)
}
# expecation w*=sum_g sum_i (ni((ng-1)/(N-1)))
network[network == 10] <- NA
ni <- rowSums(network, na.rm = T)
ng <- NA
for (i in 1:length(ccovar)) {
ng[i] <- table(ccovar)[rownames(table(ccovar)) == ccovar[i]]
}
N <- length(ccovar)
wexp <- sum(ni * ((ng - 1)/(N - 1)), na.rm = T)
# wgg1 how many intragroup ties
w <- fsumintra(network, fhomomat(ccovar))
Scol_net <- ifelse(w >= wexp, (w - wexp)/(sum(ni, na.rm = T) - wexp), (w - wexp)/wexp)
return(Scol_net)
}getwd()#> [1] "C:/Users/ninab/OneDrive/Documenten/GitHub/labjournal"load("twitter_20190919.RData") #change to your working directory
str(twitter_20190919, 1)#> List of 3
#> $ keyf :'data.frame': 147 obs. of 41 variables:
#> $ mydata:List of 8
#> ..- attr(*, "higher")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "disjoint")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "atLeastOne")= Named logi [1:9] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> .. ..- attr(*, "names")= chr [1:9] "fnet,fnet" "atmnet,fnet" "rtnet,fnet" "fnet,atmnet" ...
#> ..- attr(*, "class")= chr "siena"
#> $ seats :'data.frame': 150 obs. of 5 variables:keyf <- twitter_20190919[[1]]
#keyf is dataframe on 147 Dutch MPs
mydata <- twitter_20190919[[2]]
# mydata is object ready to analyze in RSiena. Nodes are the same as in keyf and seats. Contains twitter data at three timepounts. Three layers: fnet (who follows whom), atmnet (who amentions whom) and rnter (who retweets whom). Also contains timevariant information on nodes.
seats <- twitter_20190919[[3]]
#seats is dataset which contains coordinates of seats in House of Parliament in Netherlands# retrieve nominationdata from rsiena object
fnet <- mydata$depvars$fnet
atmnet <- mydata$depvars$atmnet
rtnet <- mydata$depvars$rtnet
# retrieve node-attributes from rsiena object
vrouw <- mydata$cCovars$vrouw
partij <- mydata$cCovars$partij
ethminz <- mydata$cCovars$ethminz
lft <- mydata$cCovars$lft
# de-mean-center node attributes
ethminz <- ethminz + attributes(ethminz)$mean
partij <- partij + attributes(partij)$mean
vrouw <- vrouw + attributes(vrouw)$mean
lft <- lft + attributes(lft)$mean
# construct matrices for similarity for each dimension (dyad characteristics)
vrouwm <- fhomomat(vrouw)
partijm <- fhomomat(partij)
ethminzm <- fhomomat(ethminz)
# just for fun, make dyad characteristic indicating whether both nodes are ethnic minorities
xmat <- matrix(ethminz, nrow = length(ethminz), ncol = length(ethminz))
xmatt <- t(xmat)
minoritym <- xmat == 1 & xmatt == 1
# for age max 5 year difference / for descriptives
xmat <- matrix(lft, nrow = length(lft), ncol = length(lft))
xmatt <- t(xmat)
lftm <- (abs(xmat - xmatt) < 6)
# calculate all possible similar dyads, not the focus of this exercise. fndyads2(fnet[,,1], vrouwm)
# fndyads2(fnet[,,3], vrouwm) fndyads2(fnet[,,1], partijm) fndyads2(fnet[,,3], partijm)
# fndyads2(fnet[,,1], ethminzm) fndyads2(fnet[,,3], ethminzm)
# make a big object to store all results
desmat <- matrix(NA, nrow = 10, ncol = 9)
# lets start using our functions
desmat[1, 1] <- fdensity(fnet[, , 1])
desmat[1, 2] <- fdensity(fnet[, , 2])
desmat[1, 3] <- fdensity(fnet[, , 3])
desmat[2, 1] <- fdensityintra(fnet[, , 1], vrouwm)
desmat[2, 2] <- fdensityintra(fnet[, , 2], vrouwm)
desmat[2, 3] <- fdensityintra(fnet[, , 3], vrouwm)
desmat[3, 1] <- fdensityinter(fnet[, , 1], vrouwm)
desmat[3, 2] <- fdensityinter(fnet[, , 2], vrouwm)
desmat[3, 3] <- fdensityinter(fnet[, , 3], vrouwm)
desmat[4, 1] <- fdensityintra(fnet[, , 1], partijm)
desmat[4, 2] <- fdensityintra(fnet[, , 2], partijm)
desmat[4, 3] <- fdensityintra(fnet[, , 3], partijm)
desmat[5, 1] <- fdensityinter(fnet[, , 1], partijm)
desmat[5, 2] <- fdensityinter(fnet[, , 2], partijm)
desmat[5, 3] <- fdensityinter(fnet[, , 3], partijm)
desmat[6, 1] <- fdensityintra(fnet[, , 1], ethminzm)
desmat[6, 2] <- fdensityintra(fnet[, , 2], ethminzm)
desmat[6, 3] <- fdensityintra(fnet[, , 3], ethminzm)
desmat[7, 1] <- fdensityinter(fnet[, , 1], ethminzm)
desmat[7, 2] <- fdensityinter(fnet[, , 2], ethminzm)
desmat[7, 3] <- fdensityinter(fnet[, , 3], ethminzm)
desmat[8, 1] <- fdensityinter(fnet[, , 1], minoritym)
desmat[8, 2] <- fdensityinter(fnet[, , 2], minoritym)
desmat[8, 3] <- fdensityinter(fnet[, , 3], minoritym)
desmat[9, 1] <- fdensityintra(fnet[, , 1], lftm)
desmat[9, 2] <- fdensityintra(fnet[, , 2], lftm)
desmat[9, 3] <- fdensityintra(fnet[, , 3], lftm)
desmat[10, 1] <- fdensityinter(fnet[, , 1], lftm)
desmat[10, 2] <- fdensityinter(fnet[, , 2], lftm)
desmat[10, 3] <- fdensityinter(fnet[, , 3], lftm)
desmat[1, 1 + 3] <- fdensity(atmnet[, , 1])
desmat[1, 2 + 3] <- fdensity(atmnet[, , 2])
desmat[1, 3 + 3] <- fdensity(atmnet[, , 3])
desmat[2, 1 + 3] <- fdensityintra(atmnet[, , 1], vrouwm)
desmat[2, 2 + 3] <- fdensityintra(atmnet[, , 2], vrouwm)
desmat[2, 3 + 3] <- fdensityintra(atmnet[, , 3], vrouwm)
desmat[3, 1 + 3] <- fdensityinter(atmnet[, , 1], vrouwm)
desmat[3, 2 + 3] <- fdensityinter(atmnet[, , 2], vrouwm)
desmat[3, 3 + 3] <- fdensityinter(atmnet[, , 3], vrouwm)
desmat[4, 1 + 3] <- fdensityintra(atmnet[, , 1], partijm)
desmat[4, 2 + 3] <- fdensityintra(atmnet[, , 2], partijm)
desmat[4, 3 + 3] <- fdensityintra(atmnet[, , 3], partijm)
desmat[5, 1 + 3] <- fdensityinter(atmnet[, , 1], partijm)
desmat[5, 2 + 3] <- fdensityinter(atmnet[, , 2], partijm)
desmat[5, 3 + 3] <- fdensityinter(atmnet[, , 3], partijm)
desmat[6, 1 + 3] <- fdensityintra(atmnet[, , 1], ethminzm)
desmat[6, 2 + 3] <- fdensityintra(atmnet[, , 2], ethminzm)
desmat[6, 3 + 3] <- fdensityintra(atmnet[, , 3], ethminzm)
desmat[7, 1 + 3] <- fdensityinter(atmnet[, , 1], ethminzm)
desmat[7, 2 + 3] <- fdensityinter(atmnet[, , 2], ethminzm)
desmat[7, 3 + 3] <- fdensityinter(atmnet[, , 3], ethminzm)
desmat[8, 1 + 3] <- fdensityinter(atmnet[, , 1], minoritym)
desmat[8, 2 + 3] <- fdensityinter(atmnet[, , 2], minoritym)
desmat[8, 3 + 3] <- fdensityinter(atmnet[, , 3], minoritym)
desmat[9, 1 + 3] <- fdensityintra(atmnet[, , 1], lftm)
desmat[9, 2 + 3] <- fdensityintra(atmnet[, , 2], lftm)
desmat[9, 3 + 3] <- fdensityintra(atmnet[, , 3], lftm)
desmat[10, 1 + 3] <- fdensityinter(atmnet[, , 1], lftm)
desmat[10, 2 + 3] <- fdensityinter(atmnet[, , 2], lftm)
desmat[10, 3 + 3] <- fdensityinter(atmnet[, , 3], lftm)
desmat[1, 1 + 6] <- fdensity(rtnet[, , 1])
desmat[1, 2 + 6] <- fdensity(rtnet[, , 2])
desmat[1, 3 + 6] <- fdensity(rtnet[, , 3])
desmat[2, 1 + 6] <- fdensityintra(rtnet[, , 1], vrouwm)
desmat[2, 2 + 6] <- fdensityintra(rtnet[, , 2], vrouwm)
desmat[2, 3 + 6] <- fdensityintra(rtnet[, , 3], vrouwm)
desmat[3, 1 + 6] <- fdensityinter(rtnet[, , 1], vrouwm)
desmat[3, 2 + 6] <- fdensityinter(rtnet[, , 2], vrouwm)
desmat[3, 3 + 6] <- fdensityinter(rtnet[, , 3], vrouwm)
desmat[4, 1 + 6] <- fdensityintra(rtnet[, , 1], partijm)
desmat[4, 2 + 6] <- fdensityintra(rtnet[, , 2], partijm)
desmat[4, 3 + 6] <- fdensityintra(rtnet[, , 3], partijm)
desmat[5, 1 + 6] <- fdensityinter(rtnet[, , 1], partijm)
desmat[5, 2 + 6] <- fdensityinter(rtnet[, , 2], partijm)
desmat[5, 3 + 6] <- fdensityinter(rtnet[, , 3], partijm)
desmat[6, 1 + 6] <- fdensityintra(rtnet[, , 1], ethminzm)
desmat[6, 2 + 6] <- fdensityintra(rtnet[, , 2], ethminzm)
desmat[6, 3 + 6] <- fdensityintra(rtnet[, , 3], ethminzm)
desmat[7, 1 + 6] <- fdensityinter(rtnet[, , 1], ethminzm)
desmat[7, 2 + 6] <- fdensityinter(rtnet[, , 2], ethminzm)
desmat[7, 3 + 6] <- fdensityinter(rtnet[, , 3], ethminzm)
desmat[8, 1 + 6] <- fdensityinter(rtnet[, , 1], minoritym)
desmat[8, 2 + 6] <- fdensityinter(rtnet[, , 2], minoritym)
desmat[8, 3 + 6] <- fdensityinter(rtnet[, , 3], minoritym)
desmat[9, 1 + 6] <- fdensityintra(rtnet[, , 1], lftm)
desmat[9, 2 + 6] <- fdensityintra(rtnet[, , 2], lftm)
desmat[9, 3 + 6] <- fdensityintra(rtnet[, , 3], lftm)
desmat[10, 1 + 6] <- fdensityinter(rtnet[, , 1], lftm)
desmat[10, 2 + 6] <- fdensityinter(rtnet[, , 2], lftm)
desmat[10, 3 + 6] <- fdensityinter(rtnet[, , 3], lftm)
colnames(desmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3",
"retweets w1", "retweets w2", "retweets w3")
rownames(desmat) <- c("total", "same sex", "different sex", "same party", "different party", "same ethnicity",
"different ethnicity", "both minority", "same age (<6)", "different age (>5)")
desmat#> friends w1 friends w2 friends w3 atmentions w1 atmentions w2 atmentions w3
#> total 0.2545583 0.2794521 0.2797969 0.04912612 0.03500236 0.013465660
#> same sex 0.2630408 0.2887662 0.2883167 0.05278618 0.03569521 0.013750219
#> different sex 0.2449678 0.2689211 0.2701115 0.04498792 0.03421900 0.013142174
#> same party 0.7091278 0.7334686 0.7415459 0.19918864 0.14239351 0.063607085
#> different party 0.1946538 0.2196204 0.2193593 0.02935044 0.02085004 0.006902729
#> same ethnicity 0.2655497 0.2885362 0.2885929 0.04926514 0.03574368 0.013491604
#> different ethnicity 0.2096154 0.2423077 0.2435592 0.04855769 0.03197115 0.013358779
#> both minority 0.2537506 0.2786431 0.2790029 0.04859054 0.03497372 0.013570823
#> same age (<6) 0.2933009 0.3137704 0.3131586 0.05868881 0.03693100 0.014668186
#> different age (>5) 0.2354766 0.2625494 0.2635734 0.04441624 0.03405245 0.012880886
#> retweets w1 retweets w2 retweets w3
#> total 0.046008503 0.03401361 0.03373404
#> same sex 0.045753961 0.03363111 0.03284288
#> different sex 0.046296296 0.03444843 0.03474711
#> same party 0.335496957 0.25040258 0.24798712
#> different party 0.007858861 0.00569080 0.00569080
#> same ethnicity 0.047971781 0.03491604 0.03381587
#> different ethnicity 0.037980769 0.03029580 0.03339695
#> both minority 0.045723841 0.03402130 0.03355009
#> same age (<6) 0.052247352 0.03716890 0.03702649
#> different age (>5) 0.042935702 0.03247922 0.03213296# we observe a lot of homophily. Mainly big difference in density between and whithin political parties. Homophily is not that strong across social dimensions.# Because size of different subgroups vary and number of out-degrees differs between MPs, whitin party densities might be higher when MPs randomly select partner/alter. Segregation will partly be structully induced by differences in relative groups sizes and activity on twitter. Coleman's homophily index: takes relative group sizes and differences into account. 0 -> observed number of within-group ties is the same as would be expected under random choice. 1 -> maximum segregation. -1 -> MPs maximally avoid within group relations.colmat <- matrix(NA, nrow = 3, ncol = 9)
colmat[1, 1] <- fscolnet(fnet[, , 1], partij)
colmat[1, 2] <- fscolnet(fnet[, , 2], partij)
colmat[1, 3] <- fscolnet(fnet[, , 3], partij)
colmat[1, 4] <- fscolnet(atmnet[, , 1], partij)
colmat[1, 5] <- fscolnet(atmnet[, , 2], partij)
colmat[1, 6] <- fscolnet(atmnet[, , 3], partij)
colmat[1, 7] <- fscolnet(rtnet[, , 1], partij)
colmat[1, 8] <- fscolnet(rtnet[, , 2], partij)
colmat[1, 9] <- fscolnet(rtnet[, , 3], partij)
colmat[2, 1] <- fscolnet(fnet[, , 1], vrouw)
colmat[2, 2] <- fscolnet(fnet[, , 2], vrouw)
colmat[2, 3] <- fscolnet(fnet[, , 3], vrouw)
colmat[2, 4] <- fscolnet(atmnet[, , 1], vrouw)
colmat[2, 5] <- fscolnet(atmnet[, , 2], vrouw)
colmat[2, 6] <- fscolnet(atmnet[, , 3], vrouw)
colmat[2, 7] <- fscolnet(rtnet[, , 1], vrouw)
colmat[2, 8] <- fscolnet(rtnet[, , 2], vrouw)
colmat[2, 9] <- fscolnet(rtnet[, , 3], vrouw)
colmat[3, 1] <- fscolnet(fnet[, , 1], ethminz)
colmat[3, 2] <- fscolnet(fnet[, , 2], ethminz)
colmat[3, 3] <- fscolnet(fnet[, , 3], ethminz)
colmat[3, 4] <- fscolnet(atmnet[, , 1], ethminz)
colmat[3, 5] <- fscolnet(atmnet[, , 2], ethminz)
colmat[3, 6] <- fscolnet(atmnet[, , 3], ethminz)
colmat[3, 7] <- fscolnet(rtnet[, , 1], ethminz)
colmat[3, 8] <- fscolnet(rtnet[, , 2], ethminz)
colmat[3, 9] <- fscolnet(rtnet[, , 3], ethminz)
colnames(colmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3",
"retweets w1", "retweets w2", "retweets w3")
rownames(colmat) <- c("party", "sex", "ethnicity")
colmat#> friends w1 friends w2 friends w3 atmentions w1 atmentions w2 atmentions w3 retweets w1
#> party 0.23290292 0.21197422 0.21310665 0.39147672 0.399713437 0.48111606 0.804211725
#> sex 0.04624336 0.04080333 0.04272129 0.07421140 0.032149289 0.04699739 -0.005381047
#> ethnicity 0.12122258 0.09821319 0.09889074 -0.03010028 -0.004564059 -0.02078296 0.051678853
#> retweets w2 retweets w3
#> party 0.803175327 0.802304023
#> sex -0.029967472 -0.011929039
#> ethnicity -0.007183535 -0.006536557# Mostly segregation within retweet network and along parties# defining myeff object
library(RSiena)
myeff <- getEffects(mydata)
myeff#> name effectName include fix test initialValue parm
#> 1 fnet constant fnet rate (period 1) TRUE FALSE FALSE 7.22021 0
#> 2 fnet constant fnet rate (period 2) TRUE FALSE FALSE 3.79571 0
#> 3 fnet fnet: outdegree (density) TRUE FALSE FALSE 0.00000 0
#> 4 fnet fnet: reciprocity TRUE FALSE FALSE 0.00000 0
#> 5 atmnet constant atmnet rate (period 1) TRUE FALSE FALSE 16.26089 0
#> 6 atmnet constant atmnet rate (period 2) TRUE FALSE FALSE 9.17902 0
#> 7 atmnet atmnet: outdegree (density) TRUE FALSE FALSE -1.76137 0
#> 8 atmnet atmnet: reciprocity TRUE FALSE FALSE 0.00000 0
#> 9 rtnet constant rtnet rate (period 1) TRUE FALSE FALSE 10.98716 0
#> 10 rtnet constant rtnet rate (period 2) TRUE FALSE FALSE 9.39819 0
#> 11 rtnet rtnet: outdegree (density) TRUE FALSE FALSE -1.61392 0
#> 12 rtnet rtnet: reciprocity TRUE FALSE FALSE 0.00000 0myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "partij", name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: same partij TRUE FALSE FALSE 0 0# I used a seed so you will probably see the same results
myalgorithm <- sienaAlgorithmCreate(projname = "test", seed = 345654)#> If you use this algorithm object, siena07 will create/use an output file test.txt .# to speed things up a bit, I am using more cores.
ansM1 <- siena07(myalgorithm, data = mydata, effects = myeff_m1, useCluster = TRUE, nbrNodes = 4, initC = TRUE,
batch = TRUE)
ansM1b <- siena07(myalgorithm, data = mydata, prevAns = ansM1, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 4, initC = TRUE, batch = TRUE)
ansM1c <- siena07(myalgorithm, data = mydata, prevAns = ansM1b, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 4, initC = TRUE, batch = TRUE)
save(ansM1, file = "ansM1a.RData")
save(ansM1b, file = "ansM1b.RData")
save(ansM1c, file = "ansM1c.RData")load("ansM1a.RData")
load("ansM1b.RData")
load("ansM1c.RData")
ansM1#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7091 ( 0.1678 ) 0.0802
#> 2. rate constant fnet rate (period 2) 1.9690 ( 0.1206 ) -0.0329
#> 3. eval fnet: outdegree (density) -0.6588 ( 0.0876 ) 0.0250
#> 4. eval fnet: reciprocity 0.8762 ( 0.0907 ) -0.0133
#> 5. rate constant atmnet rate (period 1) 25.7496 ( 1.8713 ) 0.0220
#> 6. rate constant atmnet rate (period 2) 9.6467 ( 0.6387 ) 0.0098
#> 7. eval atmnet: outdegree (density) -2.3452 ( 0.0321 ) -0.0936
#> 8. eval atmnet: reciprocity 1.7004 ( 0.0727 ) -0.0577
#> 9. rate constant rtnet rate (period 1) 13.4141 ( 0.8603 ) 0.0133
#> 10. rate constant rtnet rate (period 2) 12.1080 ( 0.9050 ) 0.0024
#> 11. eval rtnet: outdegree (density) -2.8349 ( 0.0465 ) -0.0047
#> 12. eval rtnet: reciprocity 0.8781 ( 0.0643 ) 0.0307
#> 13. eval rtnet: same partij 1.8582 ( 0.0583 ) 0.0427
#>
#> Overall maximum convergence ratio: 0.1963
#>
#>
#> Total of 2158 iteration steps.ansM1b#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7037 ( 0.1701 ) 0.0505
#> 2. rate constant fnet rate (period 2) 1.9687 ( 0.1205 ) -0.0350
#> 3. eval fnet: outdegree (density) -0.6541 ( 0.0844 ) -0.0026
#> 4. eval fnet: reciprocity 0.8756 ( 0.0841 ) 0.0061
#> 5. rate constant atmnet rate (period 1) 25.7211 ( 1.8487 ) 0.0565
#> 6. rate constant atmnet rate (period 2) 9.6449 ( 0.5197 ) -0.0190
#> 7. eval atmnet: outdegree (density) -2.3452 ( 0.0297 ) -0.0472
#> 8. eval atmnet: reciprocity 1.7000 ( 0.0703 ) -0.0012
#> 9. rate constant rtnet rate (period 1) 13.3847 ( 0.8180 ) 0.0404
#> 10. rate constant rtnet rate (period 2) 12.0998 ( 0.7632 ) 0.0072
#> 11. eval rtnet: outdegree (density) -2.8343 ( 0.0503 ) -0.0190
#> 12. eval rtnet: reciprocity 0.8758 ( 0.0614 ) -0.0285
#> 13. eval rtnet: same partij 1.8582 ( 0.0601 ) 0.0178
#>
#> Overall maximum convergence ratio: 0.1857
#>
#>
#> Total of 2104 iteration steps.ansM1c#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7053 ( 0.1674 ) -0.0270
#> 2. rate constant fnet rate (period 2) 1.9683 ( 0.1254 ) 0.0579
#> 3. eval fnet: outdegree (density) -0.6570 ( 0.0852 ) -0.0352
#> 4. eval fnet: reciprocity 0.8753 ( 0.0933 ) -0.0508
#> 5. rate constant atmnet rate (period 1) 25.7437 ( 1.4522 ) -0.0013
#> 6. rate constant atmnet rate (period 2) 9.6502 ( 0.5618 ) 0.0395
#> 7. eval atmnet: outdegree (density) -2.3453 ( 0.0304 ) -0.0235
#> 8. eval atmnet: reciprocity 1.7011 ( 0.0645 ) 0.0050
#> 9. rate constant rtnet rate (period 1) 13.3964 ( 0.8279 ) 0.0523
#> 10. rate constant rtnet rate (period 2) 12.0945 ( 0.8603 ) -0.0005
#> 11. eval rtnet: outdegree (density) -2.8322 ( 0.0500 ) -0.0197
#> 12. eval rtnet: reciprocity 0.8779 ( 0.0625 ) 0.0133
#> 13. eval rtnet: same partij 1.8551 ( 0.0569 ) 0.0097
#>
#> Overall maximum convergence ratio: 0.1471
#>
#>
#> Total of 2078 iteration steps.#To what extent do we observe segregation along party affiliation in the retweet network among Dutch MPs?
# Answer: the eval same partij is 1.8551. This means that we observe strong segregation along pary affiliation: people are more likely to have ties with people from the same party.#research question 1
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "partij", name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: same partij TRUE FALSE FALSE 0 0#research question 2
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "vrouw", name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: same vrouw TRUE FALSE FALSE 0 0myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "lft", name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: same lft TRUE FALSE FALSE 0 0#research question 3: propinquity -> not sure if I am doing this the right way?
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, X, interaction1 = "afstand", name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: afstand TRUE FALSE FALSE 0 0# research question 4: structural effects are reciprocity and transitivity. Effects depending on network only.
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, recip, name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: reciprocity TRUE FALSE FALSE 0 0myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, transTrip, name = "rtnet")#> effectName include fix test initialValue parm
#> 1 rtnet: transitive triplets TRUE FALSE FALSE 0 0# I used a seed so you will probably see the same results
myalgorithm <- sienaAlgorithmCreate(projname = "test", seed = 345654)#> If you use this algorithm object, siena07 will create/use an output file test.txt .load("ansM2a.RData")
load("ansM2b.RData")
load("ansM2c.RData")
ansM2#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7085 ( 0.1714 ) 0.0372
#> 2. rate constant fnet rate (period 2) 1.9696 ( 0.1230 ) 0.0724
#> 3. eval fnet: outdegree (density) -0.6582 ( 0.0816 ) -0.0148
#> 4. eval fnet: reciprocity 0.8765 ( 0.0913 ) -0.0204
#> 5. rate constant atmnet rate (period 1) 25.8514 ( 2.1960 ) 0.1187
#> 6. rate constant atmnet rate (period 2) 9.6315 ( 0.5393 ) -0.0152
#> 7. eval atmnet: outdegree (density) -2.3436 ( 0.0295 ) -0.0701
#> 8. eval atmnet: reciprocity 1.7015 ( 0.0702 ) -0.0815
#> 9. rate constant rtnet rate (period 1) 12.8580 ( 0.8342 ) -0.0031
#> 10. rate constant rtnet rate (period 2) 11.3612 ( 0.9477 ) -0.0006
#> 11. eval rtnet: outdegree (density) -2.3176 ( 0.0269 ) 0.0051
#> 12. eval rtnet: reciprocity 0.9706 ( 0.0814 ) 0.0236
#> 13. eval rtnet: transitive triplets 0.1672 ( 0.0079 ) 0.0284
#>
#> Overall maximum convergence ratio: 0.1962
#>
#>
#> Total of 2258 iteration steps.summary(ansM2)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7085 ( 0.1714 ) 0.0372
#> 2. rate constant fnet rate (period 2) 1.9696 ( 0.1230 ) 0.0724
#> 3. eval fnet: outdegree (density) -0.6582 ( 0.0816 ) -0.0148
#> 4. eval fnet: reciprocity 0.8765 ( 0.0913 ) -0.0204
#> 5. rate constant atmnet rate (period 1) 25.8514 ( 2.1960 ) 0.1187
#> 6. rate constant atmnet rate (period 2) 9.6315 ( 0.5393 ) -0.0152
#> 7. eval atmnet: outdegree (density) -2.3436 ( 0.0295 ) -0.0701
#> 8. eval atmnet: reciprocity 1.7015 ( 0.0702 ) -0.0815
#> 9. rate constant rtnet rate (period 1) 12.8580 ( 0.8342 ) -0.0031
#> 10. rate constant rtnet rate (period 2) 11.3612 ( 0.9477 ) -0.0006
#> 11. eval rtnet: outdegree (density) -2.3176 ( 0.0269 ) 0.0051
#> 12. eval rtnet: reciprocity 0.9706 ( 0.0814 ) 0.0236
#> 13. eval rtnet: transitive triplets 0.1672 ( 0.0079 ) 0.0284
#>
#> Overall maximum convergence ratio: 0.1962
#>
#>
#> Total of 2258 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.029 0.001 0.000 0.000 -0.044 0.004 0.000 0.000 0.002 0.001 0.000 0.001 0.000
#> 0.032 0.015 0.000 0.000 0.003 0.010 0.000 0.000 -0.001 -0.010 0.000 0.000 0.000
#> -0.020 -0.028 0.007 -0.002 0.001 -0.004 0.000 0.000 0.002 -0.005 0.000 0.000 0.000
#> -0.018 -0.030 -0.334 0.008 -0.022 -0.002 0.000 0.000 0.002 0.007 0.000 0.000 0.000
#> -0.117 0.012 0.003 -0.108 4.822 -0.218 0.016 0.010 0.043 -0.065 0.008 0.006 -0.002
#> 0.043 0.155 -0.081 -0.037 -0.184 0.291 0.002 0.000 -0.015 0.032 0.000 -0.002 0.000
#> -0.044 0.021 -0.019 -0.032 0.246 0.098 0.001 -0.001 0.000 0.003 0.000 0.000 0.000
#> -0.037 0.010 -0.033 0.013 0.063 0.006 -0.503 0.005 0.006 -0.009 0.000 0.000 0.000
#> 0.013 -0.008 0.031 0.030 0.023 -0.033 0.001 0.101 0.696 -0.094 0.003 -0.006 0.001
#> 0.006 -0.090 -0.065 0.080 -0.031 0.063 0.096 -0.134 -0.118 0.898 0.002 -0.001 0.001
#> 0.034 0.023 -0.057 0.033 0.131 0.011 0.016 0.036 0.135 0.076 0.001 0.000 0.000
#> 0.043 0.031 -0.011 0.000 0.031 -0.036 0.028 -0.010 -0.085 -0.017 -0.183 0.007 0.000
#> -0.045 -0.049 0.051 0.056 -0.107 -0.015 -0.028 -0.015 0.117 0.132 -0.273 -0.590 0.000
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 128.526 0.000 128.526 79.417 0.771 0.000 0.903 1.418 -8.237 0.000 -1.305 4.061 71.700
#> 0.000 136.933 25.571 9.374 0.000 -10.480 0.984 1.342 0.000 -0.999 -2.402 4.962 55.345
#> 6.860 6.533 216.498 142.640 3.748 17.440 -14.008 -0.899 -1.969 6.017 8.885 4.009 -60.461
#> 9.702 1.068 74.618 328.887 -0.507 8.232 -8.793 -10.173 -3.871 1.331 -23.701 -19.827 -312.993
#> 1.409 0.000 1.409 2.492 8.184 0.000 -7.990 -4.026 -0.264 0.000 -1.084 0.481 15.640
#> 0.000 -4.716 0.928 1.635 0.000 39.729 -21.548 -11.125 0.000 -1.696 4.048 5.126 54.897
#> 16.269 0.090 18.652 -9.026 317.445 123.500 1312.693 597.160 -23.900 -12.684 98.144 13.643 647.445
#> 17.517 1.608 20.846 -5.928 14.491 -7.402 318.589 399.385 -6.070 -3.638 13.546 -2.805 -3.067
#> -0.609 0.000 -0.609 -1.188 -1.317 0.000 -1.530 -2.475 27.643 0.000 -15.207 -6.516 -39.421
#> 0.000 1.790 0.380 -1.176 0.000 -0.959 0.013 1.345 0.000 21.210 -13.597 -7.663 -59.768
#> -41.340 -8.811 -24.646 -83.592 -16.243 40.849 3.948 10.778 33.946 166.028 1789.982 853.548 7874.136
#> -1.235 4.262 10.291 -9.092 0.641 17.783 -15.141 -8.385 -59.466 -15.642 484.615 612.355 4992.638
#> 33.511 84.710 76.193 -157.316 26.113 75.523 -128.954 -2.728 -927.912 -456.791 6178.390 6374.010 85199.442
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 475.173 10.433 475.436 293.147 6.688 5.887 -18.736 -0.951 -32.431 -0.030 0.596 33.001 235.554
#> 0.029 273.831 63.018 18.980 11.561 -17.447 -1.491 1.260 -8.541 -1.994 18.751 40.483 377.441
#> 0.802 0.140 740.467 467.588 -3.261 10.606 -42.778 -10.862 -26.648 -7.528 40.267 89.046 741.055
#> 0.440 0.038 0.562 933.831 0.354 -11.716 -38.631 -35.566 -30.390 4.351 -18.637 31.195 108.255
#> 0.014 0.032 -0.005 0.001 484.576 -1.106 129.741 -3.350 -17.403 18.322 20.507 12.364 338.723
#> 0.012 -0.048 0.018 -0.018 -0.002 475.215 -11.696 -53.909 -7.009 6.698 62.855 38.371 274.767
#> -0.027 -0.003 -0.049 -0.040 0.185 -0.017 1017.535 496.515 6.810 4.082 30.662 -18.737 63.310
#> -0.002 0.003 -0.017 -0.050 -0.006 -0.105 0.662 552.029 9.782 -16.411 30.278 -10.367 -20.366
#> -0.063 -0.022 -0.042 -0.042 -0.034 -0.014 0.009 0.018 555.151 -31.120 -116.888 -174.349 -1485.992
#> 0.000 -0.006 -0.013 0.007 0.040 0.015 0.006 -0.033 -0.063 436.871 35.945 -52.564 -549.689
#> 0.001 0.027 0.035 -0.014 0.022 0.068 0.023 0.030 -0.117 0.040 1807.012 1205.748 12783.390
#> 0.040 0.065 0.087 0.027 0.015 0.047 -0.016 -0.012 -0.196 -0.067 0.750 1429.426 14884.154
#> 0.023 0.049 0.059 0.008 0.033 0.027 0.004 -0.002 -0.137 -0.057 0.652 0.853 213035.965ansM2b#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7045 ( 0.1685 ) 0.0021
#> 2. rate constant fnet rate (period 2) 1.9699 ( 0.1174 ) 0.0444
#> 3. eval fnet: outdegree (density) -0.6560 ( 0.0859 ) -0.0133
#> 4. eval fnet: reciprocity 0.8762 ( 0.0873 ) -0.0010
#> 5. rate constant atmnet rate (period 1) 25.7332 ( 1.8144 ) 0.0375
#> 6. rate constant atmnet rate (period 2) 9.6330 ( 0.5367 ) 0.0383
#> 7. eval atmnet: outdegree (density) -2.3455 ( 0.0307 ) -0.0167
#> 8. eval atmnet: reciprocity 1.7010 ( 0.0697 ) -0.0210
#> 9. rate constant rtnet rate (period 1) 12.8752 ( 0.7444 ) -0.0419
#> 10. rate constant rtnet rate (period 2) 11.3581 ( 0.7501 ) 0.0830
#> 11. eval rtnet: outdegree (density) -2.3177 ( 0.0258 ) -0.0406
#> 12. eval rtnet: reciprocity 0.9709 ( 0.0785 ) -0.0114
#> 13. eval rtnet: transitive triplets 0.1674 ( 0.0077 ) -0.0234
#>
#> Overall maximum convergence ratio: 0.1393
#>
#>
#> Total of 2106 iteration steps.summary(ansM2b)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7045 ( 0.1685 ) 0.0021
#> 2. rate constant fnet rate (period 2) 1.9699 ( 0.1174 ) 0.0444
#> 3. eval fnet: outdegree (density) -0.6560 ( 0.0859 ) -0.0133
#> 4. eval fnet: reciprocity 0.8762 ( 0.0873 ) -0.0010
#> 5. rate constant atmnet rate (period 1) 25.7332 ( 1.8144 ) 0.0375
#> 6. rate constant atmnet rate (period 2) 9.6330 ( 0.5367 ) 0.0383
#> 7. eval atmnet: outdegree (density) -2.3455 ( 0.0307 ) -0.0167
#> 8. eval atmnet: reciprocity 1.7010 ( 0.0697 ) -0.0210
#> 9. rate constant rtnet rate (period 1) 12.8752 ( 0.7444 ) -0.0419
#> 10. rate constant rtnet rate (period 2) 11.3581 ( 0.7501 ) 0.0830
#> 11. eval rtnet: outdegree (density) -2.3177 ( 0.0258 ) -0.0406
#> 12. eval rtnet: reciprocity 0.9709 ( 0.0785 ) -0.0114
#> 13. eval rtnet: transitive triplets 0.1674 ( 0.0077 ) -0.0234
#>
#> Overall maximum convergence ratio: 0.1393
#>
#>
#> Total of 2106 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.028 0.000 0.000 0.000 -0.005 0.003 0.000 0.000 0.002 -0.001 0.000 -0.001 0.000
#> -0.008 0.014 0.000 0.000 0.004 0.002 0.000 0.000 -0.001 -0.003 0.000 0.000 0.000
#> -0.006 0.026 0.007 -0.003 0.002 -0.001 0.000 0.000 0.004 0.002 0.000 0.000 0.000
#> 0.028 -0.012 -0.349 0.008 0.005 0.002 0.000 0.000 -0.001 0.005 0.000 0.000 0.000
#> -0.015 0.020 0.010 0.033 3.292 -0.105 0.005 0.015 -0.070 -0.046 0.002 -0.007 0.001
#> 0.029 0.038 -0.020 0.040 -0.107 0.288 0.002 -0.002 0.002 -0.013 0.000 0.001 0.000
#> -0.036 0.024 0.030 -0.009 0.095 0.101 0.001 -0.001 -0.003 0.000 0.000 0.000 0.000
#> 0.009 -0.046 -0.044 0.051 0.115 -0.046 -0.566 0.005 0.003 0.002 0.000 0.000 0.000
#> 0.015 -0.012 0.056 -0.018 -0.052 0.005 -0.120 0.060 0.554 -0.014 -0.002 0.000 0.001
#> -0.008 -0.031 0.033 0.082 -0.034 -0.031 -0.015 0.031 -0.026 0.563 0.002 0.002 0.000
#> 0.088 0.028 0.030 -0.010 0.050 -0.024 0.003 0.023 -0.119 0.087 0.001 0.000 0.000
#> -0.042 -0.053 -0.042 0.035 -0.052 0.034 -0.104 0.039 0.008 0.036 -0.150 0.006 0.000
#> 0.031 0.054 0.044 0.002 0.084 -0.024 -0.016 0.015 0.144 -0.019 -0.316 -0.554 0.000
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 132.001 0.000 132.001 78.205 -0.419 0.000 0.560 -0.578 -5.624 0.000 -9.433 -3.588 -63.363
#> 0.000 143.336 24.312 13.868 0.000 -0.988 11.436 8.494 0.000 -7.624 5.001 8.654 70.425
#> 9.438 -3.822 201.911 132.867 1.188 -12.446 -5.682 4.620 -2.073 -16.442 -19.819 -11.035 -265.084
#> 2.207 0.834 68.064 340.935 -2.288 -4.661 4.824 -6.466 -3.449 -12.837 -1.627 -13.191 -170.777
#> 0.120 0.000 0.120 -0.704 10.466 0.000 -7.654 -4.442 0.826 0.000 -2.414 -1.520 -24.818
#> 0.000 -1.465 0.648 -2.979 0.000 36.535 -18.254 -8.021 0.000 1.537 0.475 -0.197 3.198
#> 44.234 16.597 45.186 51.372 308.854 170.580 1285.889 599.944 23.361 14.580 141.005 165.064 1982.835
#> 2.527 5.228 11.673 -8.134 37.355 0.214 359.140 422.776 3.847 9.716 8.437 12.457 310.101
#> -0.503 0.000 -0.503 -0.226 1.316 0.000 0.422 -0.339 32.618 0.000 -5.896 -5.867 -26.454
#> 0.000 0.828 -0.785 -1.300 0.000 0.708 -0.980 -0.542 0.000 27.420 -9.793 -5.010 -10.257
#> -76.865 -5.255 -97.882 -29.379 -13.612 -5.313 -6.050 12.425 105.152 138.410 1792.920 828.454 7755.612
#> -2.782 9.654 -2.815 -0.662 14.960 8.059 9.636 10.521 -48.117 -32.742 448.229 573.386 4535.969
#> -124.877 5.079 -193.397 -147.635 43.954 76.330 115.137 150.319 -697.006 -360.832 5974.511 6157.446 84409.823
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 489.098 -4.965 500.211 311.469 0.082 17.648 6.032 -8.461 -22.243 -5.477 -9.174 -2.913 -25.911
#> -0.013 281.345 43.506 26.596 11.703 -0.915 22.848 6.135 -6.707 -12.531 42.876 43.643 499.696
#> 0.821 0.094 759.369 478.845 12.199 10.450 3.728 0.311 -5.621 -22.478 -17.695 -1.563 -66.404
#> 0.453 0.051 0.559 966.493 1.830 -0.227 51.104 -5.494 -22.969 5.158 21.673 1.973 -124.580
#> 0.000 0.032 0.020 0.003 469.194 26.148 98.612 6.617 12.258 -14.610 11.759 35.318 364.504
#> 0.038 -0.003 0.018 0.000 0.058 432.763 12.927 -54.485 -12.887 9.209 8.718 27.098 277.994
#> 0.008 0.042 0.004 0.050 0.139 0.019 1076.000 536.826 -31.760 16.589 4.462 -2.975 422.848
#> -0.016 0.015 0.000 -0.007 0.013 -0.109 0.681 577.934 0.092 24.325 26.711 19.619 563.570
#> -0.042 -0.017 -0.009 -0.031 0.024 -0.026 -0.041 0.000 570.745 0.793 -74.531 -138.124 -877.109
#> -0.012 -0.035 -0.038 0.008 -0.031 0.021 0.024 0.047 0.002 461.019 -3.672 -106.269 -991.484
#> -0.010 0.060 -0.015 0.016 0.013 0.010 0.003 0.026 -0.073 -0.004 1803.940 1192.547 13034.998
#> -0.004 0.070 -0.002 0.002 0.044 0.035 -0.002 0.022 -0.156 -0.134 0.760 1366.422 14767.366
#> -0.003 0.064 -0.005 -0.009 0.036 0.029 0.028 0.050 -0.078 -0.099 0.655 0.853 219594.082ansM2c#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7037 ( 0.1684 ) 0.0450
#> 2. rate constant fnet rate (period 2) 1.9693 ( 0.1238 ) 0.0014
#> 3. eval fnet: outdegree (density) -0.6518 ( 0.0835 ) 0.0549
#> 4. eval fnet: reciprocity 0.8754 ( 0.0939 ) 0.0205
#> 5. rate constant atmnet rate (period 1) 25.7350 ( 1.3232 ) 0.0001
#> 6. rate constant atmnet rate (period 2) 9.6373 ( 0.5570 ) -0.0062
#> 7. eval atmnet: outdegree (density) -2.3441 ( 0.0327 ) -0.0171
#> 8. eval atmnet: reciprocity 1.6996 ( 0.0679 ) -0.0428
#> 9. rate constant rtnet rate (period 1) 12.9186 ( 0.6778 ) -0.0010
#> 10. rate constant rtnet rate (period 2) 11.3854 ( 0.7948 ) 0.0633
#> 11. eval rtnet: outdegree (density) -2.3177 ( 0.0272 ) -0.0316
#> 12. eval rtnet: reciprocity 0.9705 ( 0.0762 ) -0.0090
#> 13. eval rtnet: transitive triplets 0.1674 ( 0.0076 ) -0.0353
#>
#> Overall maximum convergence ratio: 0.1232
#>
#>
#> Total of 2014 iteration steps.summary(ansM2c)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7037 ( 0.1684 ) 0.0450
#> 2. rate constant fnet rate (period 2) 1.9693 ( 0.1238 ) 0.0014
#> 3. eval fnet: outdegree (density) -0.6518 ( 0.0835 ) 0.0549
#> 4. eval fnet: reciprocity 0.8754 ( 0.0939 ) 0.0205
#> 5. rate constant atmnet rate (period 1) 25.7350 ( 1.3232 ) 0.0001
#> 6. rate constant atmnet rate (period 2) 9.6373 ( 0.5570 ) -0.0062
#> 7. eval atmnet: outdegree (density) -2.3441 ( 0.0327 ) -0.0171
#> 8. eval atmnet: reciprocity 1.6996 ( 0.0679 ) -0.0428
#> 9. rate constant rtnet rate (period 1) 12.9186 ( 0.6778 ) -0.0010
#> 10. rate constant rtnet rate (period 2) 11.3854 ( 0.7948 ) 0.0633
#> 11. eval rtnet: outdegree (density) -2.3177 ( 0.0272 ) -0.0316
#> 12. eval rtnet: reciprocity 0.9705 ( 0.0762 ) -0.0090
#> 13. eval rtnet: transitive triplets 0.1674 ( 0.0076 ) -0.0353
#>
#> Overall maximum convergence ratio: 0.1232
#>
#>
#> Total of 2014 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.028 0.000 -0.001 0.000 -0.010 0.007 0.000 0.000 0.003 0.000 0.000 0.000 0.000
#> 0.014 0.015 0.000 0.000 0.000 0.000 0.000 0.000 0.001 -0.009 0.000 0.000 0.000
#> -0.086 0.005 0.007 -0.004 0.003 0.001 0.000 0.000 -0.002 -0.001 0.000 0.000 0.000
#> 0.014 -0.018 -0.481 0.009 -0.013 0.002 0.000 0.000 0.006 -0.003 0.000 0.000 0.000
#> -0.044 -0.002 0.031 -0.101 1.751 -0.079 0.002 0.020 -0.102 -0.014 0.002 -0.005 0.001
#> 0.071 0.000 0.019 0.045 -0.107 0.310 0.004 -0.002 0.008 0.006 0.001 -0.001 0.000
#> 0.011 0.033 -0.002 -0.011 0.053 0.194 0.001 -0.001 -0.001 0.002 0.000 0.000 0.000
#> 0.003 -0.029 -0.014 -0.030 0.224 -0.058 -0.549 0.005 0.001 -0.002 0.000 0.000 0.000
#> 0.025 0.007 -0.032 0.095 -0.114 0.022 -0.050 0.019 0.459 -0.033 0.001 -0.002 0.000
#> -0.002 -0.090 -0.013 -0.041 -0.013 0.012 0.074 -0.033 -0.062 0.632 0.004 0.004 0.000
#> 0.016 -0.015 -0.043 0.039 0.053 0.047 0.068 -0.032 0.063 0.163 0.001 0.000 0.000
#> -0.039 -0.045 -0.007 -0.044 -0.054 -0.021 -0.038 -0.015 -0.041 0.069 -0.204 0.006 0.000
#> 0.008 0.012 0.032 -0.023 0.080 -0.005 -0.019 0.074 0.066 -0.078 -0.307 -0.510 0.000
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 132.941 0.000 132.941 74.533 8.556 0.000 1.202 0.112 1.988 0.000 6.913 6.781 30.527
#> 0.000 131.107 24.343 5.964 0.000 -4.994 -4.798 1.158 0.000 4.241 7.737 2.612 -0.629
#> 13.273 1.010 239.217 179.315 2.385 0.642 12.087 13.805 2.966 1.527 -3.053 -9.861 -190.410
#> -2.673 -3.952 78.722 324.716 0.159 -0.581 6.893 12.416 -3.480 -7.993 -1.468 2.382 -13.218
#> 0.833 0.000 0.833 1.522 15.093 0.000 -10.838 -7.113 0.583 0.000 -4.681 -2.521 -38.144
#> 0.000 0.020 -0.730 -3.100 0.000 35.546 -22.704 -11.606 0.000 1.040 -0.219 1.126 11.227
#> -16.014 -12.629 -18.632 31.995 233.982 111.217 1198.180 585.864 30.629 -5.641 -23.434 3.218 -257.176
#> -6.814 -0.760 0.818 24.243 -17.422 -10.640 337.481 427.452 7.243 3.119 -39.031 -22.970 -395.754
#> -0.566 0.000 -0.566 -4.057 1.927 0.000 1.630 -0.902 35.176 0.000 -15.960 -7.706 -28.232
#> 0.000 1.516 0.494 1.300 0.000 -2.423 -1.553 -0.547 0.000 24.568 -10.358 -3.517 32.539
#> 30.438 7.969 29.723 7.063 -15.764 12.843 23.465 28.321 72.829 129.622 1825.646 893.247 8291.916
#> 19.415 6.735 24.296 31.853 4.112 21.385 15.481 9.718 -34.339 -32.576 458.377 589.002 4563.053
#> 54.716 -20.418 -61.003 17.543 38.025 141.614 39.641 37.866 -544.479 -288.619 5969.852 6118.327 83996.091
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 497.015 4.557 479.707 247.205 33.372 27.958 -10.932 -6.326 10.358 6.643 51.561 37.327 154.597
#> 0.013 261.195 53.199 -4.938 0.942 -10.017 -6.143 -2.699 2.250 6.459 2.683 -12.052 -239.677
#> 0.788 0.121 745.986 409.270 28.333 30.891 -18.142 -1.051 15.429 3.343 17.686 1.186 -448.007
#> 0.386 -0.011 0.522 825.249 6.730 9.724 16.526 34.661 -10.065 -16.674 -4.076 -7.664 -505.453
#> 0.069 0.003 0.048 0.011 467.748 24.730 60.917 -32.060 -8.961 6.235 -21.400 -4.606 -99.846
#> 0.060 -0.030 0.054 0.016 0.055 439.810 -11.807 -72.686 13.563 -14.382 65.693 62.558 522.842
#> -0.015 -0.012 -0.021 0.018 0.088 -0.018 1029.845 500.205 46.258 16.642 18.497 9.530 -6.607
#> -0.012 -0.007 -0.002 0.052 -0.063 -0.148 0.667 546.274 6.958 3.525 -11.461 3.445 7.978
#> 0.019 0.006 0.023 -0.014 -0.017 0.027 0.060 0.012 584.649 8.055 -117.792 -151.288 -952.159
#> 0.014 0.019 0.006 -0.028 0.014 -0.033 0.025 0.007 0.016 429.464 52.034 -53.334 -151.620
#> 0.052 0.004 0.015 -0.003 -0.022 0.071 0.013 -0.011 -0.110 0.057 1960.422 1292.018 14059.103
#> 0.044 -0.019 0.001 -0.007 -0.006 0.078 0.008 0.004 -0.163 -0.067 0.762 1465.197 15656.572
#> 0.015 -0.031 -0.034 -0.037 -0.010 0.052 0.000 0.001 -0.083 -0.015 0.666 0.857 227629.972# Research question 2: To what extent is the presumed segregation along party affiliation in the retweet network among Dutch MPs the byproduct of segregation along other social dimensions such as sex, age?
# Answer:# Research question 3: To what extent is the presumed segregation along party affiliation in the retweet network among Dutch MPs the result of propinquity?
# Answer:
# Research question 4: To what extent is the presumed segregation along party affiliation in the retweet network among Dutch MPs the result of structural (network) effects?
# Answer: