rm(list=ls())#install.packages("RSiena")
library(RSiena)
friend.data.w1 <- s501
friend.data.w2 <- s502
friend.data.w3 <- s503
drink <- s50a
smoke <- s50s
friendship <- sienaDependent(array(c(friend.data.w1, friend.data.w2, friend.data.w3), dim = c(50, 50,
3)))
smoke1 <- coCovar(smoke[, 1])
alcohol <- varCovar(drink)
mydata <- sienaDataCreate(friendship, smoke1, alcohol)
# and request
mydata
# to see what you have produced.myalgorithm <- sienaAlgorithmCreate(projname = NULL)myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip, cycle3)
myeff <- includeEffects(myeff, egoX, altX, egoXaltX, interaction1 = "alcohol")
myeff <- includeEffects(myeff, simX, interaction1 = "smoke1")
myeffans <- siena07(myalgorithm, data = mydata, effects = myeff)
summary(ans)myeff <- setEffect(myeff, inPopSqrt, fix = TRUE, test = TRUE, initialValue = 0)
ans <- siena07(myalgorithm, data = mydata, effects = myeff, prevAns=ans)
summary(ans)# 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()
load("/Users/anuschka/Documents/labjournal/data/twitter_20190919.RData") #change to your working directorystr(twitter_20190919, 1)
keyf <- twitter_20190919[[1]]
mydata <- twitter_20190919[[2]]
seats <- twitter_20190919[[3]]# 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)")
desmatmyeff <- getEffects(mydata)
myeff#Name is de afhankelijke variabele
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "partij", name = "rtnet")
#To answer the second research question, I would like to include different effects. To study the segregation of sex, I think I can still use the sameX effect (to see whether individuals of the same sex are more inclined to retweet one another).
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "vrouw", name = "rtnet")
#To answer the second subquestion of the second RQ, we have to see if there is segregation based on age. I believe that during the class we talked about that you just want the normal variable of age (lft), but I don't totally understand why it's not possible to take the variable same age. Let's try lft first.
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "lft", name = "rtnet")
#Does afstand just work like this?
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "afstand", name = "rtnet")
#No it doesn't so I checked the effect in the effectsdocumentation. Here I see that sameX cannot be used for the variable afstand. I'm not sure which of the other effects is useful, I checked them in the Rsiena manual. Maybe incoming shared WWX, but on the other hand I'm not sure if that really tells something about segregation. Let's try reciprocity and then if there's no reciprocity that's segregation (not really but ok)
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, XRecip, interaction1 = "afstand", name = "rtnet")
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, transTrip, transTies, name = "rtnet")# I used a seed so you will probably see the same results
myalgorithm <- sienaAlgorithmCreate(projname = "test", seed = 345654)# to speed things up a bit, I am using more cores.
ansM1 <- siena07(myalgorithm, data = mydata, effects = myeff_m1, useCluster = TRUE, nbrNodes = 2, initC = TRUE,
batch = TRUE)
ansM1b <- siena07(myalgorithm, data = mydata, prevAns = ansM1, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 2, initC = TRUE, batch = TRUE)
ansM1c <- siena07(myalgorithm, data = mydata, prevAns = ansM1b, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 2, initC = TRUE, batch = TRUE)
ansM1d <- siena07(myalgorithm, data = mydata, prevAns = ansM1c, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 2, initC = TRUE, batch = TRUE)save(ansM1, file = "ansM1a.RData")
save(ansM1b, file = "ansM1b.RData")
save(ansM1c, file = "ansM1c.RData")
save(ansM1d, file="ansM1d.RData")summary(ansM1)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7025 ( 0.1597 ) 0.0357
#> 2. rate constant fnet rate (period 2) 1.9617 ( 0.1209 ) -0.0394
#> 3. eval fnet: outdegree (density) -0.6469 ( 0.0832 ) 0.0611
#> 4. eval fnet: reciprocity 0.8738 ( 0.0933 ) -0.0087
#> 5. rate constant atmnet rate (period 1) 25.7940 ( 1.9433 ) 0.0479
#> 6. rate constant atmnet rate (period 2) 9.6375 ( 0.5609 ) -0.0363
#> 7. eval atmnet: outdegree (density) -2.3469 ( 0.0306 ) -0.0745
#> 8. eval atmnet: reciprocity 1.7050 ( 0.0679 ) -0.0068
#> 9. rate constant rtnet rate (period 1) 9.8803 ( 0.4642 ) 0.0368
#> 10. rate constant rtnet rate (period 2) 8.8679 ( 0.4907 ) 0.0069
#> 11. eval rtnet: outdegree (density) -2.1481 ( 0.0383 ) 0.0565
#> 12. eval rtnet: reciprocity 1.7617 ( 0.0677 ) 0.0693
#> 13. eval rtnet: same vrouw -0.0053 ( 0.0479 ) 0.0758
#>
#> Overall maximum convergence ratio: 0.1886
#>
#>
#> Total of 2494 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.026 0.001 0.000 0.001 0.014 -0.002 0.000 0.000 0.005 -0.002 0.000 0.000 0.000
#> 0.072 0.015 0.000 0.000 0.004 -0.003 0.000 -0.001 0.005 -0.002 0.000 0.000 0.000
#> -0.011 -0.018 0.007 -0.003 -0.009 0.007 0.000 0.000 0.002 -0.002 0.000 0.000 0.000
#> 0.043 0.012 -0.398 0.009 0.021 0.002 0.000 0.000 -0.003 0.003 0.000 0.000 0.000
#> 0.046 0.018 -0.057 0.114 3.777 -0.134 0.019 0.020 -0.090 0.069 -0.011 0.018 0.010
#> -0.018 -0.038 0.147 0.036 -0.123 0.315 0.000 0.000 -0.006 0.008 0.000 0.001 0.000
#> 0.061 0.060 0.038 0.124 0.314 0.005 0.001 -0.001 0.000 -0.001 0.000 0.000 0.000
#> -0.045 -0.074 -0.029 -0.043 0.153 0.001 -0.474 0.005 -0.002 0.001 0.000 0.000 0.000
#> 0.071 0.084 0.046 -0.070 -0.100 -0.022 -0.018 -0.068 0.215 -0.023 0.000 0.001 0.001
#> -0.024 -0.029 -0.042 0.069 0.072 0.028 -0.038 0.039 -0.099 0.241 -0.001 0.002 0.000
#> 0.034 0.016 0.038 -0.008 -0.148 -0.002 0.051 -0.103 -0.024 -0.039 0.001 -0.001 -0.001
#> -0.011 -0.005 -0.023 -0.029 0.137 0.028 -0.047 0.002 0.033 0.060 -0.362 0.005 0.000
#> 0.025 0.027 -0.007 0.006 0.105 -0.009 -0.028 0.052 0.066 0.007 -0.686 0.111 0.002
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 147.068 0.000 147.068 80.339 -9.614 0.000 2.660 4.027 5.224 0.000 -7.700 -0.720 -4.456
#> 0.000 136.075 26.814 9.317 0.000 1.654 -3.338 -0.203 0.000 1.329 -8.634 -3.628 -8.557
#> 7.944 2.444 223.672 157.729 1.575 -5.525 -6.637 3.345 -1.090 10.867 -1.099 -2.692 -4.298
#> -9.111 -4.532 69.433 324.649 4.971 -10.379 -8.389 5.973 10.768 -2.642 -7.224 -4.728 -5.551
#> -0.774 0.000 -0.774 -2.111 9.675 0.000 -10.690 -6.356 0.485 0.000 0.322 -1.527 -0.797
#> 0.000 0.744 -4.899 -3.694 0.000 37.463 -14.015 -6.784 0.000 -0.023 -0.275 0.189 0.168
#> 1.904 -30.789 -36.032 -79.930 217.567 163.212 1293.928 582.230 -15.236 9.605 28.318 55.585 35.254
#> 8.951 4.859 7.445 -5.530 -4.936 -0.014 335.429 414.904 22.024 2.639 28.854 17.507 6.540
#> -3.888 0.000 -3.888 0.123 4.701 0.000 3.261 2.967 50.878 0.000 -12.638 -8.288 -8.170
#> 0.000 0.983 0.661 -2.082 0.000 1.110 3.678 0.981 0.000 43.015 -9.656 -6.170 -4.157
#> -54.242 -19.149 -54.952 -45.825 12.993 -18.686 -15.470 17.704 97.337 178.564 1305.721 504.070 709.266
#> 1.970 -5.476 -3.632 -11.686 3.487 -4.020 13.956 12.646 -36.347 -10.687 289.674 419.988 146.919
#> -35.639 -15.114 -38.982 -28.483 8.240 -4.596 29.300 20.538 34.695 100.331 692.127 245.758 727.670
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 543.039 21.571 557.499 301.214 -33.152 0.794 26.020 15.467 18.185 -8.478 -17.680 6.594 -9.906
#> 0.057 268.069 73.485 21.783 -0.754 -1.092 -16.214 -10.072 32.020 4.967 -19.569 -14.018 -15.080
#> 0.824 0.155 842.106 498.782 -29.602 -1.591 32.188 16.713 35.880 9.622 -8.467 -13.465 -11.349
#> 0.422 0.043 0.561 939.412 -25.220 7.817 15.788 2.032 41.081 -1.388 -51.635 -59.587 -30.041
#> -0.065 -0.002 -0.047 -0.038 473.637 14.358 79.919 -36.650 6.324 13.342 8.625 0.983 4.440
#> 0.002 -0.003 -0.003 0.012 0.031 463.176 0.591 -40.726 -21.149 19.981 -14.604 13.900 0.428
#> 0.035 -0.031 0.035 0.016 0.116 0.001 1001.633 482.314 36.089 1.539 -19.287 -38.926 8.611
#> 0.029 -0.027 0.025 0.003 -0.073 -0.082 0.657 538.782 39.987 -0.475 -13.830 -30.417 -6.263
#> 0.033 0.082 0.052 0.056 0.012 -0.041 0.048 0.072 569.093 -27.751 -35.811 -100.069 -21.007
#> -0.017 0.014 0.015 -0.002 0.029 0.043 0.002 -0.001 -0.054 461.029 42.505 -39.025 37.047
#> -0.022 -0.035 -0.009 -0.049 0.012 -0.020 -0.018 -0.017 -0.044 0.058 1171.588 564.844 645.549
#> 0.011 -0.033 -0.018 -0.075 0.002 0.025 -0.047 -0.050 -0.161 -0.070 0.633 680.652 310.823
#> -0.017 -0.037 -0.016 -0.039 0.008 0.001 0.011 -0.011 -0.035 0.069 0.750 0.474 633.061summary(ansM1b)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7029 ( 0.1585 ) 0.0540
#> 2. rate constant fnet rate (period 2) 1.9681 ( 0.1212 ) 0.0116
#> 3. eval fnet: outdegree (density) -0.6500 ( 0.0813 ) 0.0518
#> 4. eval fnet: reciprocity 0.8738 ( 0.0876 ) -0.0051
#> 5. rate constant atmnet rate (period 1) 25.7601 ( 1.6459 ) 0.0126
#> 6. rate constant atmnet rate (period 2) 9.6437 ( 0.5321 ) 0.0203
#> 7. eval atmnet: outdegree (density) -2.3447 ( 0.0299 ) -0.0553
#> 8. eval atmnet: reciprocity 1.7003 ( 0.0701 ) -0.0063
#> 9. rate constant rtnet rate (period 1) 9.8955 ( 0.4964 ) 0.0516
#> 10. rate constant rtnet rate (period 2) 8.8459 ( 0.4346 ) 0.0499
#> 11. eval rtnet: outdegree (density) -2.1509 ( 0.0282 ) 0.0390
#> 12. eval rtnet: reciprocity 1.7617 ( 0.0695 ) -0.0035
#> 13. eval rtnet: same lft -0.0085 ( 0.1328 ) -0.0104
#>
#> Overall maximum convergence ratio: 0.1374
#>
#>
#> Total of 2317 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.025 0.000 -0.001 0.000 -0.013 0.000 0.000 -0.001 -0.003 0.002 0.000 0.000 0.001
#> -0.003 0.015 0.001 0.000 0.003 0.004 0.000 0.000 0.000 -0.003 0.000 0.000 0.000
#> -0.075 0.052 0.007 -0.003 0.010 -0.005 0.000 0.000 0.000 -0.001 0.000 0.000 0.000
#> -0.005 -0.016 -0.357 0.008 -0.017 0.000 0.000 0.000 0.003 0.002 0.000 0.000 -0.001
#> -0.050 0.013 0.071 -0.117 2.709 -0.153 0.006 0.028 -0.020 -0.013 0.001 -0.002 -0.022
#> 0.000 0.058 -0.105 0.010 -0.175 0.283 0.000 -0.001 -0.004 -0.004 -0.001 0.001 0.007
#> 0.037 -0.015 -0.005 -0.076 0.130 0.000 0.001 -0.001 0.000 0.000 0.000 0.000 0.000
#> -0.098 0.019 0.016 0.045 0.246 -0.039 -0.498 0.005 0.001 0.000 0.000 0.000 -0.001
#> -0.042 0.000 -0.010 0.059 -0.024 -0.015 0.033 0.016 0.246 -0.006 0.001 0.003 0.002
#> 0.027 -0.058 -0.032 0.040 -0.018 -0.016 -0.013 0.012 -0.030 0.189 -0.001 0.001 0.002
#> -0.022 -0.022 -0.016 -0.002 0.030 -0.051 -0.050 -0.035 0.045 -0.068 0.001 -0.001 0.000
#> 0.044 0.008 0.028 0.039 -0.013 0.017 0.059 0.034 0.087 0.035 -0.453 0.005 0.000
#> 0.032 -0.031 -0.035 -0.044 -0.102 0.093 0.014 -0.068 0.027 0.042 -0.069 -0.035 0.018
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 150.116 0.000 150.116 91.979 -1.053 0.000 5.713 4.942 4.915 0.000 -5.025 -7.543 -0.389
#> 0.000 135.597 17.896 11.251 0.000 1.815 7.116 1.617 0.000 8.528 -1.616 -0.846 0.896
#> 10.124 -9.224 225.543 153.271 -3.358 5.632 -5.974 -2.904 0.498 -5.866 -4.452 -12.147 1.960
#> 6.339 1.619 79.299 342.095 1.342 1.783 2.013 -2.384 4.347 -8.948 7.250 -6.697 3.485
#> 0.217 0.000 0.217 2.348 12.892 0.000 -10.445 -6.741 0.048 0.000 -0.113 0.379 0.325
#> 0.000 -2.149 3.217 1.926 0.000 40.360 -12.757 -6.244 0.000 -2.642 4.594 2.378 -0.694
#> 14.360 -0.095 13.143 63.176 234.860 153.303 1293.019 608.863 -68.841 -9.777 49.364 -3.789 3.087
#> 27.763 -0.908 23.747 4.911 -18.209 -0.110 341.843 416.070 -10.391 9.170 22.488 -1.038 2.493
#> 2.437 0.000 2.437 -0.736 2.161 0.000 -1.869 -2.470 46.319 0.000 -17.428 -10.949 -0.685
#> 0.000 1.979 1.623 0.310 0.000 5.981 -0.451 -2.486 0.000 50.634 -5.413 -3.758 -0.522
#> -14.421 4.352 -24.191 -6.354 -30.492 2.480 3.670 38.038 107.701 140.822 1372.864 604.811 34.686
#> -11.525 1.230 -21.914 -13.324 -7.629 -11.031 -14.407 -5.819 -39.579 -12.265 334.461 450.751 8.939
#> -2.567 2.933 2.226 7.220 -1.194 0.828 0.735 4.304 -1.718 6.548 39.485 16.641 45.684
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 555.845 1.950 537.333 332.788 -6.804 -3.500 31.165 14.942 18.248 11.058 -27.062 -17.675 -0.199
#> 0.005 266.998 40.151 29.633 3.949 0.542 7.070 3.674 -0.313 14.991 -12.061 -2.638 0.788
#> 0.811 0.087 790.350 507.460 -12.670 4.795 7.792 5.658 19.086 10.748 -47.046 -34.156 1.751
#> 0.458 0.059 0.586 949.029 15.371 -2.705 30.698 14.219 26.619 4.990 5.935 -4.091 3.786
#> -0.012 0.010 -0.019 0.022 535.198 -31.328 63.140 -27.218 -6.246 -8.924 -21.592 -0.855 0.816
#> -0.007 0.002 0.008 -0.004 -0.061 485.227 11.137 -45.416 -18.008 13.765 20.476 9.244 2.308
#> 0.040 0.013 0.008 0.030 0.083 0.015 1075.542 530.346 -37.125 -11.314 -27.476 6.911 0.377
#> 0.027 0.009 0.008 0.019 -0.050 -0.087 0.683 561.055 -19.040 -7.207 16.898 15.625 1.070
#> 0.033 -0.001 0.029 0.037 -0.012 -0.035 -0.048 -0.034 549.119 3.117 -34.853 -99.089 -1.642
#> 0.021 0.041 0.017 0.007 -0.017 0.028 -0.016 -0.014 0.006 495.080 21.291 -44.192 5.892
#> -0.033 -0.021 -0.048 0.005 -0.027 0.027 -0.024 0.020 -0.042 0.027 1229.548 654.334 41.096
#> -0.027 -0.006 -0.044 -0.005 -0.001 0.015 0.008 0.024 -0.152 -0.071 0.671 773.468 16.495
#> -0.001 0.008 0.010 0.021 0.006 0.018 0.002 0.008 -0.012 0.044 0.196 0.099 35.594summary(ansM1c)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7056 ( 0.1618 ) 0.0704
#> 2. rate constant fnet rate (period 2) 1.9693 ( 0.1169 ) 0.0015
#> 3. eval fnet: outdegree (density) -0.6498 ( 0.0875 ) 0.1001
#> 4. eval fnet: reciprocity 0.8761 ( 0.0952 ) 0.0577
#> 5. rate constant atmnet rate (period 1) 25.7303 ( 2.1120 ) 0.0595
#> 6. rate constant atmnet rate (period 2) 9.6375 ( 0.6349 ) -0.0284
#> 7. eval atmnet: outdegree (density) -2.3448 ( 0.0300 ) 0.0472
#> 8. eval atmnet: reciprocity 1.7007 ( 0.0739 ) 0.0538
#> 9. rate constant rtnet rate (period 1) 10.3726 ( 0.5379 ) 0.0871
#> 10. rate constant rtnet rate (period 2) 9.2336 ( 0.5150 ) 0.0279
#> 11. eval rtnet: outdegree (density) -2.1440 ( 0.0281 ) 0.0235
#> 12. eval rtnet: reciprocity 1.0249 ( 0.1500 ) -0.0119
#> 13. eval rtnet: afstand x reciprocity -0.0999 ( 0.0163 ) 0.0223
#>
#> Overall maximum convergence ratio: 0.1644
#>
#>
#> Total of 2494 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.026 0.001 0.000 0.000 -0.036 0.000 0.000 -0.001 -0.001 -0.001 0.000 0.001 0.000
#> 0.066 0.014 -0.001 0.000 -0.005 -0.002 0.000 0.000 0.002 0.001 0.000 0.001 0.000
#> -0.016 -0.054 0.008 -0.003 -0.015 -0.008 0.000 0.000 -0.002 -0.005 0.000 0.000 0.000
#> 0.003 -0.026 -0.304 0.009 0.065 -0.006 0.000 0.001 0.006 0.000 0.000 0.000 0.000
#> -0.104 -0.021 -0.080 0.322 4.460 -0.282 0.013 0.046 0.169 0.043 -0.001 -0.015 -0.003
#> 0.001 -0.021 -0.149 -0.097 -0.210 0.403 0.001 -0.003 -0.026 0.006 0.001 -0.001 0.000
#> -0.086 -0.057 0.012 0.072 0.206 0.042 0.001 -0.001 -0.001 0.000 0.000 0.000 0.000
#> -0.059 0.023 -0.049 0.112 0.295 -0.070 -0.494 0.005 0.007 0.000 0.000 0.000 0.000
#> -0.012 0.033 -0.045 0.122 0.149 -0.075 -0.040 0.164 0.289 -0.019 0.001 0.001 0.000
#> -0.015 0.012 -0.106 0.005 0.039 0.019 -0.020 -0.008 -0.068 0.265 0.001 0.003 0.000
#> 0.014 -0.032 -0.011 0.027 -0.013 0.079 0.008 -0.070 0.067 0.091 0.001 -0.001 0.000
#> 0.034 0.031 0.036 0.006 -0.046 -0.006 0.015 -0.013 0.010 0.033 -0.222 0.022 0.002
#> 0.047 0.010 0.045 0.004 -0.101 0.041 -0.018 -0.037 -0.036 -0.024 -0.045 0.880 0.000
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 144.470 0.000 144.470 83.286 2.341 0.000 7.377 7.069 2.028 0.000 1.827 1.609 -25.495
#> 0.000 145.145 32.526 15.128 0.000 8.427 5.418 1.267 0.000 9.474 -0.349 -5.563 48.416
#> 8.048 13.588 204.612 132.600 1.380 16.022 -9.298 -4.354 0.955 0.352 10.568 7.198 -102.057
#> -4.539 5.009 63.105 316.504 -2.476 6.827 0.195 1.972 -14.451 -0.061 -3.740 5.697 -94.590
#> 0.930 0.000 0.930 -3.104 9.422 0.000 -11.059 -7.451 -0.911 0.000 -0.445 -0.311 5.975
#> 0.000 0.977 5.228 4.830 0.000 31.487 -16.166 -5.929 0.000 -0.569 -1.997 1.276 -16.881
#> 60.585 15.293 47.030 -13.969 239.030 168.447 1436.591 711.460 30.587 22.931 -11.674 -93.763 760.712
#> 34.807 1.895 33.543 5.519 -23.055 -1.239 376.462 436.122 -25.714 11.595 33.029 -10.481 125.422
#> -0.139 0.000 -0.139 -2.694 1.137 0.000 -2.493 -2.640 40.009 0.000 -14.453 -9.884 89.108
#> 0.000 0.204 3.509 3.212 0.000 -1.768 6.110 3.917 0.000 39.756 -13.313 -9.677 86.780
#> -1.989 -8.585 -14.870 -61.245 16.241 -12.586 58.969 37.685 115.434 149.505 1277.164 465.523 -3710.883
#> 2.531 -8.782 5.233 -4.483 -2.043 4.189 -8.569 -6.601 -41.723 -25.786 261.957 395.219 -3245.204
#> -37.206 52.657 -88.089 -44.796 8.715 4.592 48.013 40.744 408.108 237.334 -2206.344 -3242.372 32032.864
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 534.590 23.520 543.904 320.731 9.467 -14.363 -3.505 22.158 8.268 -6.101 13.382 13.583 -104.140
#> 0.060 285.246 86.395 36.262 -10.001 15.128 0.460 3.552 7.901 17.951 -16.302 -11.752 95.641
#> 0.824 0.179 814.580 513.905 11.650 10.536 -6.936 18.401 3.523 -0.611 26.764 27.108 -230.267
#> 0.453 0.070 0.588 936.486 -3.468 10.009 -7.472 14.573 -22.456 3.949 -29.707 -5.312 -21.730
#> 0.018 -0.027 0.018 -0.005 499.326 -14.181 85.187 -39.764 17.376 19.266 6.616 -5.604 10.299
#> -0.030 0.043 0.018 0.016 -0.031 425.790 20.526 -22.231 -6.150 -15.328 -2.766 -2.972 43.979
#> -0.004 0.001 -0.007 -0.007 0.111 0.029 1184.271 597.356 -3.620 39.347 39.691 -15.946 85.779
#> 0.039 0.009 0.026 0.019 -0.072 -0.044 0.703 610.319 -15.926 27.964 19.371 -12.680 121.894
#> 0.016 0.022 0.006 -0.034 0.036 -0.014 -0.005 -0.030 470.653 -3.160 14.641 -97.120 941.082
#> -0.012 0.050 -0.001 0.006 0.041 -0.035 0.054 0.053 -0.007 452.224 83.922 -37.552 391.211
#> 0.018 -0.029 0.029 -0.030 0.009 -0.004 0.035 0.024 0.021 0.120 1080.719 482.551 -4133.295
#> 0.023 -0.027 0.037 -0.007 -0.010 -0.006 -0.018 -0.020 -0.176 -0.069 0.577 646.063 -5382.071
#> -0.020 0.025 -0.035 -0.003 0.002 0.009 0.011 0.022 0.189 0.080 -0.549 -0.924 52488.103summary(ansM1d)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7040 ( 0.1662 ) 0.0205
#> 2. rate constant fnet rate (period 2) 1.9684 ( 0.1246 ) -0.0029
#> 3. eval fnet: outdegree (density) -0.6498 ( 0.0816 ) 0.0129
#> 4. eval fnet: reciprocity 0.8748 ( 0.0918 ) -0.0070
#> 5. rate constant atmnet rate (period 1) 25.7112 ( 1.9733 ) -0.0044
#> 6. rate constant atmnet rate (period 2) 9.6607 ( 0.5419 ) 0.0394
#> 7. eval atmnet: outdegree (density) -2.3452 ( 0.0302 ) -0.0134
#> 8. eval atmnet: reciprocity 1.7019 ( 0.0706 ) 0.0083
#> 9. rate constant rtnet rate (period 1) 14.0703 ( 0.8359 ) -0.0769
#> 10. rate constant rtnet rate (period 2) 14.3665 ( 0.9926 ) -0.0006
#> 11. eval rtnet: outdegree (density) -2.8855 ( 0.0565 ) -0.0722
#> 12. eval rtnet: reciprocity 0.7335 ( 0.0802 ) -0.0364
#> 13. eval rtnet: transitive triplets 0.1258 ( 0.0080 ) -0.0180
#> 14. eval rtnet: transitive ties 1.1401 ( 0.0742 ) -0.0706
#>
#> Overall maximum convergence ratio: 0.1251
#>
#>
#> Total of 2956 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.028 0.000 -0.001 0.000 -0.098 0.005 0.000 0.000 -0.006 0.001 0.000 0.000 0.000 0.000
#> 0.005 0.016 -0.001 0.000 0.006 -0.008 0.000 0.000 0.002 -0.019 -0.001 0.001 0.000 0.002
#> -0.038 -0.068 0.007 -0.003 -0.006 0.001 0.000 0.000 0.002 0.003 0.000 0.000 0.000 0.000
#> -0.004 0.020 -0.389 0.008 -0.005 -0.003 0.000 -0.001 -0.013 0.003 0.000 0.000 0.000 0.000
#> -0.300 0.026 -0.039 -0.026 3.894 -0.186 0.011 0.030 0.070 -0.110 -0.004 -0.005 0.000 0.012
#> 0.060 -0.121 0.013 -0.059 -0.174 0.294 0.002 -0.002 0.011 0.021 0.004 -0.002 0.001 -0.006
#> -0.059 0.044 -0.099 0.036 0.183 0.132 0.001 -0.001 0.000 -0.002 0.000 0.000 0.000 0.000
#> -0.034 -0.039 0.038 -0.082 0.218 -0.041 -0.451 0.005 0.004 -0.004 0.000 0.000 0.000 0.000
#> -0.041 0.016 0.029 -0.166 0.042 0.024 -0.016 0.063 0.699 -0.150 0.001 0.001 0.000 0.003
#> 0.004 -0.153 0.033 0.038 -0.056 0.039 -0.070 -0.058 -0.181 0.985 0.014 -0.003 0.001 -0.019
#> 0.002 -0.152 0.027 -0.017 -0.031 0.119 -0.114 0.011 0.014 0.247 0.003 0.000 0.000 -0.004
#> -0.031 0.090 -0.009 0.014 -0.029 -0.053 0.042 -0.081 0.017 -0.039 0.014 0.006 0.000 0.000
#> 0.049 -0.145 0.036 -0.088 0.021 0.133 -0.063 0.074 -0.037 0.127 0.106 -0.552 0.000 0.000
#> -0.034 0.169 -0.045 0.042 0.080 -0.142 0.116 -0.007 0.051 -0.261 -0.865 -0.068 -0.348 0.006
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 147.630 0.000 147.630 88.732 -0.602 0.000 -1.461 -4.670 2.669 0.000 -8.696 -8.150 -95.961 -8.335
#> 0.000 135.002 34.368 9.812 0.000 3.628 -3.734 6.008 0.000 -2.209 -3.736 -1.738 33.991 -8.235
#> 7.411 11.548 225.935 150.124 0.933 5.738 17.627 8.148 2.537 -14.145 -5.894 -4.351 -29.486 0.430
#> 11.274 -3.115 86.316 327.207 0.305 2.679 1.718 -1.019 8.189 -4.172 -10.515 -0.817 113.055 -5.701
#> 3.886 0.000 3.886 2.715 9.800 0.000 -8.790 -5.614 -0.415 0.000 -5.610 -3.260 -43.154 -5.972
#> 0.000 2.846 0.816 1.723 0.000 36.201 -25.670 -9.126 0.000 -3.472 -3.045 -2.953 -50.172 -3.061
#> -10.618 -8.540 37.130 22.775 257.354 121.782 1286.184 551.828 25.791 32.397 82.528 33.331 568.367 61.393
#> -18.467 10.932 -11.978 -8.130 4.651 -5.834 316.761 389.963 14.855 1.042 10.827 8.209 -29.716 -2.739
#> 1.271 0.000 1.271 4.636 -1.102 0.000 0.020 -0.913 28.292 0.000 -10.993 -5.285 -8.332 -13.102
#> 0.000 2.208 0.028 -0.854 0.000 1.071 1.149 1.666 0.000 19.562 -8.378 -4.817 -43.013 -7.031
#> -1.357 1.450 -14.224 -26.659 22.771 -9.891 53.522 27.017 256.937 234.967 2552.419 1141.957 12407.941 2437.127
#> 1.474 -13.450 -2.933 -9.498 -1.536 3.405 9.040 14.711 0.420 -8.073 696.120 725.940 6359.445 773.587
#> -107.516 0.371 -120.248 -75.931 131.930 -63.260 140.307 82.588 159.547 -19.988 10140.304 8246.876 109375.937 11051.064
#> 1.304 -17.860 -9.619 -30.208 10.622 24.653 -3.484 8.001 193.146 130.794 2119.132 1072.890 11610.482 2292.185
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 544.414 5.756 531.543 331.271 -1.369 0.764 -6.408 -24.959 12.660 -11.783 -41.245 -33.563 -313.369 -35.209
#> 0.015 266.044 72.605 9.577 -0.791 4.911 -8.746 14.878 -1.176 -3.559 -33.716 -23.246 -313.467 -34.813
#> 0.808 0.158 795.623 500.758 -10.798 8.913 14.199 -13.816 -2.907 -30.724 -92.402 -61.828 -647.959 -75.304
#> 0.470 0.019 0.588 912.203 -0.335 0.830 6.876 -42.830 -1.820 -10.611 -109.280 -94.595 -951.729 -98.832
#> -0.003 -0.002 -0.017 -0.001 491.744 -1.755 116.285 -5.028 -0.721 -0.432 1.526 -12.364 -111.728 3.656
#> 0.002 0.015 0.016 0.001 -0.004 411.235 -32.766 -39.565 9.437 -15.494 36.363 15.990 312.488 42.232
#> -0.008 -0.016 0.015 0.007 0.161 -0.050 1063.442 492.310 5.118 -0.777 14.575 14.818 -25.875 18.732
#> -0.046 0.040 -0.021 -0.062 -0.010 -0.085 0.656 530.269 14.952 -5.071 6.844 12.018 58.783 -5.810
#> 0.021 -0.003 -0.004 -0.002 -0.001 0.018 0.006 0.026 645.114 -24.568 256.292 5.334 577.254 190.502
#> -0.024 -0.010 -0.051 -0.016 -0.001 -0.036 -0.001 -0.010 -0.045 453.488 146.254 -56.020 -242.391 31.827
#> -0.030 -0.035 -0.055 -0.061 0.001 0.030 0.008 0.005 0.171 0.116 3486.930 1968.728 22589.967 3493.685
#> -0.034 -0.033 -0.051 -0.074 -0.013 0.019 0.011 0.012 0.005 -0.062 0.783 1813.727 19504.121 2121.364
#> -0.026 -0.037 -0.044 -0.060 -0.010 0.030 -0.002 0.005 0.044 -0.022 0.734 0.879 271555.304 23858.852
#> -0.024 -0.035 -0.043 -0.053 0.003 0.034 0.009 -0.004 0.122 0.024 0.960 0.808 0.743 3796.365I think we can conclude that there is a negative and weak effect of same gender on retweeting, which means that individuals do not necessarily prefer to retweet someone of the same gender compared to someone of the other gender. Now we have to check how this is the case for individuals of the same age. In the second model I ran, we see that also for same age there is a negative and quite weak effect (-0.0085), so politicians do not prefer to retweet something tweeted by someone of the same age. In sum, the segregation of party affiliation in the retweet network does not seem to be the result of segregation along age and sex.
summary(ansM1b)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7029 ( 0.1585 ) 0.0540
#> 2. rate constant fnet rate (period 2) 1.9681 ( 0.1212 ) 0.0116
#> 3. eval fnet: outdegree (density) -0.6500 ( 0.0813 ) 0.0518
#> 4. eval fnet: reciprocity 0.8738 ( 0.0876 ) -0.0051
#> 5. rate constant atmnet rate (period 1) 25.7601 ( 1.6459 ) 0.0126
#> 6. rate constant atmnet rate (period 2) 9.6437 ( 0.5321 ) 0.0203
#> 7. eval atmnet: outdegree (density) -2.3447 ( 0.0299 ) -0.0553
#> 8. eval atmnet: reciprocity 1.7003 ( 0.0701 ) -0.0063
#> 9. rate constant rtnet rate (period 1) 9.8955 ( 0.4964 ) 0.0516
#> 10. rate constant rtnet rate (period 2) 8.8459 ( 0.4346 ) 0.0499
#> 11. eval rtnet: outdegree (density) -2.1509 ( 0.0282 ) 0.0390
#> 12. eval rtnet: reciprocity 1.7617 ( 0.0695 ) -0.0035
#> 13. eval rtnet: same lft -0.0085 ( 0.1328 ) -0.0104
#>
#> Overall maximum convergence ratio: 0.1374
#>
#>
#> Total of 2317 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.025 0.000 -0.001 0.000 -0.013 0.000 0.000 -0.001 -0.003 0.002 0.000 0.000 0.001
#> -0.003 0.015 0.001 0.000 0.003 0.004 0.000 0.000 0.000 -0.003 0.000 0.000 0.000
#> -0.075 0.052 0.007 -0.003 0.010 -0.005 0.000 0.000 0.000 -0.001 0.000 0.000 0.000
#> -0.005 -0.016 -0.357 0.008 -0.017 0.000 0.000 0.000 0.003 0.002 0.000 0.000 -0.001
#> -0.050 0.013 0.071 -0.117 2.709 -0.153 0.006 0.028 -0.020 -0.013 0.001 -0.002 -0.022
#> 0.000 0.058 -0.105 0.010 -0.175 0.283 0.000 -0.001 -0.004 -0.004 -0.001 0.001 0.007
#> 0.037 -0.015 -0.005 -0.076 0.130 0.000 0.001 -0.001 0.000 0.000 0.000 0.000 0.000
#> -0.098 0.019 0.016 0.045 0.246 -0.039 -0.498 0.005 0.001 0.000 0.000 0.000 -0.001
#> -0.042 0.000 -0.010 0.059 -0.024 -0.015 0.033 0.016 0.246 -0.006 0.001 0.003 0.002
#> 0.027 -0.058 -0.032 0.040 -0.018 -0.016 -0.013 0.012 -0.030 0.189 -0.001 0.001 0.002
#> -0.022 -0.022 -0.016 -0.002 0.030 -0.051 -0.050 -0.035 0.045 -0.068 0.001 -0.001 0.000
#> 0.044 0.008 0.028 0.039 -0.013 0.017 0.059 0.034 0.087 0.035 -0.453 0.005 0.000
#> 0.032 -0.031 -0.035 -0.044 -0.102 0.093 0.014 -0.068 0.027 0.042 -0.069 -0.035 0.018
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 150.116 0.000 150.116 91.979 -1.053 0.000 5.713 4.942 4.915 0.000 -5.025 -7.543 -0.389
#> 0.000 135.597 17.896 11.251 0.000 1.815 7.116 1.617 0.000 8.528 -1.616 -0.846 0.896
#> 10.124 -9.224 225.543 153.271 -3.358 5.632 -5.974 -2.904 0.498 -5.866 -4.452 -12.147 1.960
#> 6.339 1.619 79.299 342.095 1.342 1.783 2.013 -2.384 4.347 -8.948 7.250 -6.697 3.485
#> 0.217 0.000 0.217 2.348 12.892 0.000 -10.445 -6.741 0.048 0.000 -0.113 0.379 0.325
#> 0.000 -2.149 3.217 1.926 0.000 40.360 -12.757 -6.244 0.000 -2.642 4.594 2.378 -0.694
#> 14.360 -0.095 13.143 63.176 234.860 153.303 1293.019 608.863 -68.841 -9.777 49.364 -3.789 3.087
#> 27.763 -0.908 23.747 4.911 -18.209 -0.110 341.843 416.070 -10.391 9.170 22.488 -1.038 2.493
#> 2.437 0.000 2.437 -0.736 2.161 0.000 -1.869 -2.470 46.319 0.000 -17.428 -10.949 -0.685
#> 0.000 1.979 1.623 0.310 0.000 5.981 -0.451 -2.486 0.000 50.634 -5.413 -3.758 -0.522
#> -14.421 4.352 -24.191 -6.354 -30.492 2.480 3.670 38.038 107.701 140.822 1372.864 604.811 34.686
#> -11.525 1.230 -21.914 -13.324 -7.629 -11.031 -14.407 -5.819 -39.579 -12.265 334.461 450.751 8.939
#> -2.567 2.933 2.226 7.220 -1.194 0.828 0.735 4.304 -1.718 6.548 39.485 16.641 45.684
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 555.845 1.950 537.333 332.788 -6.804 -3.500 31.165 14.942 18.248 11.058 -27.062 -17.675 -0.199
#> 0.005 266.998 40.151 29.633 3.949 0.542 7.070 3.674 -0.313 14.991 -12.061 -2.638 0.788
#> 0.811 0.087 790.350 507.460 -12.670 4.795 7.792 5.658 19.086 10.748 -47.046 -34.156 1.751
#> 0.458 0.059 0.586 949.029 15.371 -2.705 30.698 14.219 26.619 4.990 5.935 -4.091 3.786
#> -0.012 0.010 -0.019 0.022 535.198 -31.328 63.140 -27.218 -6.246 -8.924 -21.592 -0.855 0.816
#> -0.007 0.002 0.008 -0.004 -0.061 485.227 11.137 -45.416 -18.008 13.765 20.476 9.244 2.308
#> 0.040 0.013 0.008 0.030 0.083 0.015 1075.542 530.346 -37.125 -11.314 -27.476 6.911 0.377
#> 0.027 0.009 0.008 0.019 -0.050 -0.087 0.683 561.055 -19.040 -7.207 16.898 15.625 1.070
#> 0.033 -0.001 0.029 0.037 -0.012 -0.035 -0.048 -0.034 549.119 3.117 -34.853 -99.089 -1.642
#> 0.021 0.041 0.017 0.007 -0.017 0.028 -0.016 -0.014 0.006 495.080 21.291 -44.192 5.892
#> -0.033 -0.021 -0.048 0.005 -0.027 0.027 -0.024 0.020 -0.042 0.027 1229.548 654.334 41.096
#> -0.027 -0.006 -0.044 -0.005 -0.001 0.015 0.008 0.024 -0.152 -0.071 0.671 773.468 16.495
#> -0.001 0.008 0.010 0.021 0.006 0.018 0.002 0.008 -0.012 0.044 0.196 0.099 35.594Let’s continue to test the third RQ. I think that to test propinquity, we have to see how close the party members are seated next to each other. This has to do with the variable seats. In the keyf we see the coordinates, but when we study the mydata list, we see that the distance between members is already calculated as a dynamic covariate (afstand). This is what we want to use to test the propinquity.
summary(seats)
summary(mydata$dycCovars)In the output of afstand x reciprocity we see that there is a negative effect (-0.0999) of afstand x reciprocity. I think this means that individuals do not prefer to have a reciprocal relation with someone who is close to them, while the general effect of reciprocity is positive. However, I’m not sure how afstand is now analyzed and whether this effect regards individuals of a lower distance as more of the same. But I also don’t know which variable could give a better answer to the research question.
summary(ansM1c)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7056 ( 0.1618 ) 0.0704
#> 2. rate constant fnet rate (period 2) 1.9693 ( 0.1169 ) 0.0015
#> 3. eval fnet: outdegree (density) -0.6498 ( 0.0875 ) 0.1001
#> 4. eval fnet: reciprocity 0.8761 ( 0.0952 ) 0.0577
#> 5. rate constant atmnet rate (period 1) 25.7303 ( 2.1120 ) 0.0595
#> 6. rate constant atmnet rate (period 2) 9.6375 ( 0.6349 ) -0.0284
#> 7. eval atmnet: outdegree (density) -2.3448 ( 0.0300 ) 0.0472
#> 8. eval atmnet: reciprocity 1.7007 ( 0.0739 ) 0.0538
#> 9. rate constant rtnet rate (period 1) 10.3726 ( 0.5379 ) 0.0871
#> 10. rate constant rtnet rate (period 2) 9.2336 ( 0.5150 ) 0.0279
#> 11. eval rtnet: outdegree (density) -2.1440 ( 0.0281 ) 0.0235
#> 12. eval rtnet: reciprocity 1.0249 ( 0.1500 ) -0.0119
#> 13. eval rtnet: afstand x reciprocity -0.0999 ( 0.0163 ) 0.0223
#>
#> Overall maximum convergence ratio: 0.1644
#>
#>
#> Total of 2494 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.026 0.001 0.000 0.000 -0.036 0.000 0.000 -0.001 -0.001 -0.001 0.000 0.001 0.000
#> 0.066 0.014 -0.001 0.000 -0.005 -0.002 0.000 0.000 0.002 0.001 0.000 0.001 0.000
#> -0.016 -0.054 0.008 -0.003 -0.015 -0.008 0.000 0.000 -0.002 -0.005 0.000 0.000 0.000
#> 0.003 -0.026 -0.304 0.009 0.065 -0.006 0.000 0.001 0.006 0.000 0.000 0.000 0.000
#> -0.104 -0.021 -0.080 0.322 4.460 -0.282 0.013 0.046 0.169 0.043 -0.001 -0.015 -0.003
#> 0.001 -0.021 -0.149 -0.097 -0.210 0.403 0.001 -0.003 -0.026 0.006 0.001 -0.001 0.000
#> -0.086 -0.057 0.012 0.072 0.206 0.042 0.001 -0.001 -0.001 0.000 0.000 0.000 0.000
#> -0.059 0.023 -0.049 0.112 0.295 -0.070 -0.494 0.005 0.007 0.000 0.000 0.000 0.000
#> -0.012 0.033 -0.045 0.122 0.149 -0.075 -0.040 0.164 0.289 -0.019 0.001 0.001 0.000
#> -0.015 0.012 -0.106 0.005 0.039 0.019 -0.020 -0.008 -0.068 0.265 0.001 0.003 0.000
#> 0.014 -0.032 -0.011 0.027 -0.013 0.079 0.008 -0.070 0.067 0.091 0.001 -0.001 0.000
#> 0.034 0.031 0.036 0.006 -0.046 -0.006 0.015 -0.013 0.010 0.033 -0.222 0.022 0.002
#> 0.047 0.010 0.045 0.004 -0.101 0.041 -0.018 -0.037 -0.036 -0.024 -0.045 0.880 0.000
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 144.470 0.000 144.470 83.286 2.341 0.000 7.377 7.069 2.028 0.000 1.827 1.609 -25.495
#> 0.000 145.145 32.526 15.128 0.000 8.427 5.418 1.267 0.000 9.474 -0.349 -5.563 48.416
#> 8.048 13.588 204.612 132.600 1.380 16.022 -9.298 -4.354 0.955 0.352 10.568 7.198 -102.057
#> -4.539 5.009 63.105 316.504 -2.476 6.827 0.195 1.972 -14.451 -0.061 -3.740 5.697 -94.590
#> 0.930 0.000 0.930 -3.104 9.422 0.000 -11.059 -7.451 -0.911 0.000 -0.445 -0.311 5.975
#> 0.000 0.977 5.228 4.830 0.000 31.487 -16.166 -5.929 0.000 -0.569 -1.997 1.276 -16.881
#> 60.585 15.293 47.030 -13.969 239.030 168.447 1436.591 711.460 30.587 22.931 -11.674 -93.763 760.712
#> 34.807 1.895 33.543 5.519 -23.055 -1.239 376.462 436.122 -25.714 11.595 33.029 -10.481 125.422
#> -0.139 0.000 -0.139 -2.694 1.137 0.000 -2.493 -2.640 40.009 0.000 -14.453 -9.884 89.108
#> 0.000 0.204 3.509 3.212 0.000 -1.768 6.110 3.917 0.000 39.756 -13.313 -9.677 86.780
#> -1.989 -8.585 -14.870 -61.245 16.241 -12.586 58.969 37.685 115.434 149.505 1277.164 465.523 -3710.883
#> 2.531 -8.782 5.233 -4.483 -2.043 4.189 -8.569 -6.601 -41.723 -25.786 261.957 395.219 -3245.204
#> -37.206 52.657 -88.089 -44.796 8.715 4.592 48.013 40.744 408.108 237.334 -2206.344 -3242.372 32032.864
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 534.590 23.520 543.904 320.731 9.467 -14.363 -3.505 22.158 8.268 -6.101 13.382 13.583 -104.140
#> 0.060 285.246 86.395 36.262 -10.001 15.128 0.460 3.552 7.901 17.951 -16.302 -11.752 95.641
#> 0.824 0.179 814.580 513.905 11.650 10.536 -6.936 18.401 3.523 -0.611 26.764 27.108 -230.267
#> 0.453 0.070 0.588 936.486 -3.468 10.009 -7.472 14.573 -22.456 3.949 -29.707 -5.312 -21.730
#> 0.018 -0.027 0.018 -0.005 499.326 -14.181 85.187 -39.764 17.376 19.266 6.616 -5.604 10.299
#> -0.030 0.043 0.018 0.016 -0.031 425.790 20.526 -22.231 -6.150 -15.328 -2.766 -2.972 43.979
#> -0.004 0.001 -0.007 -0.007 0.111 0.029 1184.271 597.356 -3.620 39.347 39.691 -15.946 85.779
#> 0.039 0.009 0.026 0.019 -0.072 -0.044 0.703 610.319 -15.926 27.964 19.371 -12.680 121.894
#> 0.016 0.022 0.006 -0.034 0.036 -0.014 -0.005 -0.030 470.653 -3.160 14.641 -97.120 941.082
#> -0.012 0.050 -0.001 0.006 0.041 -0.035 0.054 0.053 -0.007 452.224 83.922 -37.552 391.211
#> 0.018 -0.029 0.029 -0.030 0.009 -0.004 0.035 0.024 0.021 0.120 1080.719 482.551 -4133.295
#> 0.023 -0.027 0.037 -0.007 -0.010 -0.006 -0.018 -0.020 -0.176 -0.069 0.577 646.063 -5382.071
#> -0.020 0.025 -0.035 -0.003 0.002 0.009 0.011 0.022 0.189 0.080 -0.549 -0.924 52488.103summary(ansM1d)#> Estimates, standard errors and convergence t-ratios
#>
#> Estimate Standard Convergence
#> Error t-ratio
#> 1. rate constant fnet rate (period 1) 3.7040 ( 0.1662 ) 0.0205
#> 2. rate constant fnet rate (period 2) 1.9684 ( 0.1246 ) -0.0029
#> 3. eval fnet: outdegree (density) -0.6498 ( 0.0816 ) 0.0129
#> 4. eval fnet: reciprocity 0.8748 ( 0.0918 ) -0.0070
#> 5. rate constant atmnet rate (period 1) 25.7112 ( 1.9733 ) -0.0044
#> 6. rate constant atmnet rate (period 2) 9.6607 ( 0.5419 ) 0.0394
#> 7. eval atmnet: outdegree (density) -2.3452 ( 0.0302 ) -0.0134
#> 8. eval atmnet: reciprocity 1.7019 ( 0.0706 ) 0.0083
#> 9. rate constant rtnet rate (period 1) 14.0703 ( 0.8359 ) -0.0769
#> 10. rate constant rtnet rate (period 2) 14.3665 ( 0.9926 ) -0.0006
#> 11. eval rtnet: outdegree (density) -2.8855 ( 0.0565 ) -0.0722
#> 12. eval rtnet: reciprocity 0.7335 ( 0.0802 ) -0.0364
#> 13. eval rtnet: transitive triplets 0.1258 ( 0.0080 ) -0.0180
#> 14. eval rtnet: transitive ties 1.1401 ( 0.0742 ) -0.0706
#>
#> Overall maximum convergence ratio: 0.1251
#>
#>
#> Total of 2956 iteration steps.
#>
#> Covariance matrix of estimates (correlations below diagonal)
#>
#> 0.028 0.000 -0.001 0.000 -0.098 0.005 0.000 0.000 -0.006 0.001 0.000 0.000 0.000 0.000
#> 0.005 0.016 -0.001 0.000 0.006 -0.008 0.000 0.000 0.002 -0.019 -0.001 0.001 0.000 0.002
#> -0.038 -0.068 0.007 -0.003 -0.006 0.001 0.000 0.000 0.002 0.003 0.000 0.000 0.000 0.000
#> -0.004 0.020 -0.389 0.008 -0.005 -0.003 0.000 -0.001 -0.013 0.003 0.000 0.000 0.000 0.000
#> -0.300 0.026 -0.039 -0.026 3.894 -0.186 0.011 0.030 0.070 -0.110 -0.004 -0.005 0.000 0.012
#> 0.060 -0.121 0.013 -0.059 -0.174 0.294 0.002 -0.002 0.011 0.021 0.004 -0.002 0.001 -0.006
#> -0.059 0.044 -0.099 0.036 0.183 0.132 0.001 -0.001 0.000 -0.002 0.000 0.000 0.000 0.000
#> -0.034 -0.039 0.038 -0.082 0.218 -0.041 -0.451 0.005 0.004 -0.004 0.000 0.000 0.000 0.000
#> -0.041 0.016 0.029 -0.166 0.042 0.024 -0.016 0.063 0.699 -0.150 0.001 0.001 0.000 0.003
#> 0.004 -0.153 0.033 0.038 -0.056 0.039 -0.070 -0.058 -0.181 0.985 0.014 -0.003 0.001 -0.019
#> 0.002 -0.152 0.027 -0.017 -0.031 0.119 -0.114 0.011 0.014 0.247 0.003 0.000 0.000 -0.004
#> -0.031 0.090 -0.009 0.014 -0.029 -0.053 0.042 -0.081 0.017 -0.039 0.014 0.006 0.000 0.000
#> 0.049 -0.145 0.036 -0.088 0.021 0.133 -0.063 0.074 -0.037 0.127 0.106 -0.552 0.000 0.000
#> -0.034 0.169 -0.045 0.042 0.080 -0.142 0.116 -0.007 0.051 -0.261 -0.865 -0.068 -0.348 0.006
#>
#> Derivative matrix of expected statistics X by parameters:
#>
#> 147.630 0.000 147.630 88.732 -0.602 0.000 -1.461 -4.670 2.669 0.000 -8.696 -8.150 -95.961 -8.335
#> 0.000 135.002 34.368 9.812 0.000 3.628 -3.734 6.008 0.000 -2.209 -3.736 -1.738 33.991 -8.235
#> 7.411 11.548 225.935 150.124 0.933 5.738 17.627 8.148 2.537 -14.145 -5.894 -4.351 -29.486 0.430
#> 11.274 -3.115 86.316 327.207 0.305 2.679 1.718 -1.019 8.189 -4.172 -10.515 -0.817 113.055 -5.701
#> 3.886 0.000 3.886 2.715 9.800 0.000 -8.790 -5.614 -0.415 0.000 -5.610 -3.260 -43.154 -5.972
#> 0.000 2.846 0.816 1.723 0.000 36.201 -25.670 -9.126 0.000 -3.472 -3.045 -2.953 -50.172 -3.061
#> -10.618 -8.540 37.130 22.775 257.354 121.782 1286.184 551.828 25.791 32.397 82.528 33.331 568.367 61.393
#> -18.467 10.932 -11.978 -8.130 4.651 -5.834 316.761 389.963 14.855 1.042 10.827 8.209 -29.716 -2.739
#> 1.271 0.000 1.271 4.636 -1.102 0.000 0.020 -0.913 28.292 0.000 -10.993 -5.285 -8.332 -13.102
#> 0.000 2.208 0.028 -0.854 0.000 1.071 1.149 1.666 0.000 19.562 -8.378 -4.817 -43.013 -7.031
#> -1.357 1.450 -14.224 -26.659 22.771 -9.891 53.522 27.017 256.937 234.967 2552.419 1141.957 12407.941 2437.127
#> 1.474 -13.450 -2.933 -9.498 -1.536 3.405 9.040 14.711 0.420 -8.073 696.120 725.940 6359.445 773.587
#> -107.516 0.371 -120.248 -75.931 131.930 -63.260 140.307 82.588 159.547 -19.988 10140.304 8246.876 109375.937 11051.064
#> 1.304 -17.860 -9.619 -30.208 10.622 24.653 -3.484 8.001 193.146 130.794 2119.132 1072.890 11610.482 2292.185
#>
#> Covariance matrix of X (correlations below diagonal):
#>
#> 544.414 5.756 531.543 331.271 -1.369 0.764 -6.408 -24.959 12.660 -11.783 -41.245 -33.563 -313.369 -35.209
#> 0.015 266.044 72.605 9.577 -0.791 4.911 -8.746 14.878 -1.176 -3.559 -33.716 -23.246 -313.467 -34.813
#> 0.808 0.158 795.623 500.758 -10.798 8.913 14.199 -13.816 -2.907 -30.724 -92.402 -61.828 -647.959 -75.304
#> 0.470 0.019 0.588 912.203 -0.335 0.830 6.876 -42.830 -1.820 -10.611 -109.280 -94.595 -951.729 -98.832
#> -0.003 -0.002 -0.017 -0.001 491.744 -1.755 116.285 -5.028 -0.721 -0.432 1.526 -12.364 -111.728 3.656
#> 0.002 0.015 0.016 0.001 -0.004 411.235 -32.766 -39.565 9.437 -15.494 36.363 15.990 312.488 42.232
#> -0.008 -0.016 0.015 0.007 0.161 -0.050 1063.442 492.310 5.118 -0.777 14.575 14.818 -25.875 18.732
#> -0.046 0.040 -0.021 -0.062 -0.010 -0.085 0.656 530.269 14.952 -5.071 6.844 12.018 58.783 -5.810
#> 0.021 -0.003 -0.004 -0.002 -0.001 0.018 0.006 0.026 645.114 -24.568 256.292 5.334 577.254 190.502
#> -0.024 -0.010 -0.051 -0.016 -0.001 -0.036 -0.001 -0.010 -0.045 453.488 146.254 -56.020 -242.391 31.827
#> -0.030 -0.035 -0.055 -0.061 0.001 0.030 0.008 0.005 0.171 0.116 3486.930 1968.728 22589.967 3493.685
#> -0.034 -0.033 -0.051 -0.074 -0.013 0.019 0.011 0.012 0.005 -0.062 0.783 1813.727 19504.121 2121.364
#> -0.026 -0.037 -0.044 -0.060 -0.010 0.030 -0.002 0.005 0.044 -0.022 0.734 0.879 271555.304 23858.852
#> -0.024 -0.035 -0.043 -0.053 0.003 0.034 0.009 -0.004 0.122 0.024 0.960 0.808 0.743 3796.365To give an answer to the last research question, I believe I do not need to estimate a new model. When we look at the outdegree, we see that this is negative. The only interpretation I wrote down from last week is that that means that individuals want outdegrees but not with everyone in the network. We also see that individuals prefer to have a reciprocal tie rather than a non-reciprocal tie. Politicians thus prefer to retweet someone who also retweets them, rather than retweeting individuals that have not retweeted them. When estimating a model with details about transitivity, we see that there is both a positive effect of transitive triplets as well as transitive ties. Does this then mean that 2 individuals that retweet me also retweet each other? When summarizing these effects, we see that politicians prefer to retweet individuals with whom they already have some kind of connection (through transitivity or to establish a reciprocal relation). Therefore, I think one could conclude that there are structural factors in the network that could link with segregation. However, it is still possible that these structural factors are mostly visible because there already is segregation based on party affiliation. (How can you distinguish what is cause and what is influence?)