Week 5
Kalle Stoffers
2025-10-03
1 Getting started
rm (list=ls())
fpackage.check <- function(packages) {
lapply(packages, FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, character.only = TRUE)
}
})
}
fsave <- function(x, file = NULL, location = "./data/processed/") {
ifelse(!dir.exists("data"), dir.create("data"), FALSE)
ifelse(!dir.exists("data/processed"), dir.create("data/processed"), FALSE)
if (is.null(file))
file = deparse(substitute(x))
datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
totalname <- paste(location, datename, file, ".rda", sep = "")
save(x, file = totalname) #need to fix if file is reloaded as input name, not as x.
}
fload <- function(filename) {
load(filename)
get(ls()[ls() != "filename"])
}
fshowdf <- function(x, ...) {
knitr::kable(x, digits = 2, "html", ...) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
kableExtra::scroll_box(width = "100%", height = "300px")
}
fcolnet <- function(data = scholars, university = "RU", discipline = "sociology", waves = list(c(2015,
2018), c(2019, 2023)), type = c("first")) {
# step 1
demographics <- do.call(rbind.data.frame, data$demographics)
demographics <- demographics %>%
mutate(Universiteit1.22 = replace(Universiteit1.22, is.na(Universiteit1.22), ""), Universiteit2.22 = replace(Universiteit2.22,
is.na(Universiteit2.22), ""), Universiteit1.24 = replace(Universiteit1.24, is.na(Universiteit1.24),
""), Universiteit2.24 = replace(Universiteit2.24, is.na(Universiteit2.24), ""), discipline.22 = replace(discipline.22,
is.na(discipline.22), ""), discipline.24 = replace(discipline.24, is.na(discipline.24), ""))
sample <- which((demographics$Universiteit1.22 %in% university | demographics$Universiteit2.22 %in%
university | demographics$Universiteit1.24 %in% university | demographics$Universiteit2.24 %in%
university) & (demographics$discipline.22 %in% discipline | demographics$discipline.24 %in% discipline))
demographics_soc <- demographics[sample, ]
scholars_sel <- lapply(scholars, "[", sample)
# step 2
ids <- demographics_soc$au_id
nwaves <- length(waves)
nets <- array(0, dim = c(nwaves, length(ids), length(ids)), dimnames = list(wave = 1:nwaves, ids,
ids))
dimnames(nets)
# step 3
df_works <- tibble(works_id = unlist(lapply(scholars_sel$work, function(l) l$id)), works_author = unlist(lapply(scholars_sel$work,
function(l) l$author), recursive = FALSE), works_year = unlist(lapply(scholars_sel$work, function(l) l$publication_year),
recursive = FALSE))
df_works <- df_works[!duplicated(df_works), ]
# step 4
if (type == "first") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
ego <- df_works_w$works_author[i][[1]]$au_id[1]
alters <- df_works_w$works_author[i][[1]]$au_id[-1]
if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
}
}
}
}
if (type == "last") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
ego <- rev(df_works_w$works_author[i][[1]]$au_id)[1]
alters <- rev(df_works_w$works_author[i][[1]]$au_id)[-1]
if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
}
}
}
}
if (type == "all") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
egos <- df_works_w$works_author[i][[1]]$au_id
if (sum(ids %in% egos) > 0) {
nets[j, which(ids %in% egos), which(ids %in% egos)] <- 1
}
}
}
}
output <- list()
output$data <- scholars_sel
output$nets <- nets
return(output)
}
packages = c("RSiena", "tidyverse")
fpackage.check(packages)## [[1]]
## NULL
##
## [[2]]
## NULL2 Step 1: define data
scholars <- fload("data/processed/scholars_20240924.rda")
test <- fcolnet(data = scholars,
university = c("RU", "UU"),
discipline = "sociology",
waves = list(c(2015, 2018), c(2019, 2023)),
type = c("first"))
wave1 <- test$nets[1,,]
wave2 <- test$nets[2,,]
#some checks
dim(wave1)## [1] 100 100dim(wave2)## [1] 100 100sum(is.na(wave1))## [1] 0sum(diag(wave1)==0)## [1] 99sum(wave2>1)## [1] 0sum(wave2>0)## [1] 132#make array
nets <- array(data = c(wave1, wave2), dim = c(dim(wave1), 2))
# dependent
net <- sienaDependent(nets)
#independent
df<- test$data
df_ego <- do.call(rbind.data.frame, df$demographics)
df_ego$Functie.22[is.na(df_ego$Functie.22)] <- "."
df_ego$Functie.24[is.na(df_ego$Functie.24)] <- "."
df_ego$functie <- ifelse(df_ego$Functie.22=="Hoogleraar", 1, 0)
df_ego$functie <- ifelse(df_ego$Functie.24=="Hoogleraar", 1, df_ego$functie)
df_ego$functie <- ifelse(df_ego$Functie.22=="Bijzonder hoogleraar", 1, df_ego$functie)
df_ego$functie <- ifelse(df_ego$Functie.24=="Bijzonder hoogleraar", 1, df_ego$functie)
table(df_ego$functie, useNA="always")##
## 0 1 <NA>
## 85 15 0functie <- coCovar(df_ego$functie)
mydata <- sienaDataCreate(net, functie)3 Effects
myeff <- getEffects(mydata)
myeff## effectName include fix test initialValue parm
## 1 basic rate parameter net TRUE FALSE FALSE 2.70882 0
## 2 outdegree (density) TRUE FALSE FALSE -1.62905 0
## 3 reciprocity TRUE FALSE FALSE 0.00000 0#Inital Description
ifelse(!dir.exists("results"), dir.create("results"), FALSE)## [1] FALSEprint01Report(mydata, modelname = "./results/firsttest_scholars")4 Specify model
myeff <- getEffects(mydata)
myeff <- includeEffects(
myeff,
outAct,
isolateNet
)## effectNumber effectName shortName include fix test initialValue parm
## 1 106 outdegree - activity outAct TRUE FALSE FALSE 0 0
## 2 149 network-isolate isolateNet TRUE FALSE FALSE 0 0myeff <- includeEffects(myeff, egoX, interaction1 = "functie")## effectNumber effectName shortName include fix test initialValue parm
## 1 253 functie ego egoX TRUE FALSE FALSE 0 0myeff <- includeEffects(myeff, altX, interaction1 = "functie")## effectNumber effectName shortName include fix test initialValue parm
## 1 238 functie alter altX TRUE FALSE FALSE 0 0myeff <- includeEffects(myeff, sameX, interaction1 = "functie")## effectNumber effectName shortName include fix test initialValue parm
## 1 309 same functie sameX TRUE FALSE FALSE 0 0myeff## effectName include fix test initialValue parm
## 1 basic rate parameter net TRUE FALSE FALSE 2.70882 0
## 2 outdegree (density) TRUE FALSE FALSE -1.62905 0
## 3 reciprocity TRUE FALSE FALSE 0.00000 0
## 4 outdegree - activity TRUE FALSE FALSE 0.00000 0
## 5 network-isolate TRUE FALSE FALSE 0.00000 0
## 6 functie alter TRUE FALSE FALSE 0.00000 0
## 7 functie ego TRUE FALSE FALSE 0.00000 0
## 8 same functie TRUE FALSE FALSE 0.00000 05 Estimate
myAlgorithm <- sienaAlgorithmCreate(projname = "scholars")## If you use this algorithm object, siena07 will create/use an output file scholars.txt .EstM1 <- siena07(myAlgorithm, data = mydata, effects = myeff, returnDeps = TRUE)
EstM1## Estimates, standard errors and convergence t-ratios
##
## Estimate Standard Convergence
## Error t-ratio
##
## Rate parameters:
## 0 Rate parameter 3.6553 ( 0.4893 )
##
## Other parameters:
## 1. eval outdegree (density) -0.6923 ( 1.3763 ) 0.0696
## 2. eval reciprocity 2.6938 ( 0.3593 ) -0.0229
## 3. eval outdegree - activity -0.3040 ( 0.2436 ) 0.0220
## 4. eval network-isolate 5.1484 ( 1.7725 ) -0.0538
## 5. eval functie alter 1.4434 ( 0.3218 ) -0.0005
## 6. eval functie ego -3.3458 ( 1.0559 ) -0.0040
## 7. eval same functie -0.1756 ( 0.3177 ) 0.0341
##
## Overall maximum convergence ratio: 0.1190
##
##
## Total of 2750 iteration steps.6 Homework
Read manual, pick one relevant statistics in chapter 12. Present. Make data ready.
#start clean
rm(list=ls())#Custom functions
fpackage.check <- function(packages) {
lapply(packages, FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, character.only = TRUE)
}
})
}
fsave <- function(x, file = NULL, location = "./data/processed/") {
ifelse(!dir.exists("data"), dir.create("data"), FALSE)
ifelse(!dir.exists("data/processed"), dir.create("data/processed"), FALSE)
if (is.null(file))
file = deparse(substitute(x))
datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
totalname <- paste(location, datename, file, ".rda", sep = "")
save(x, file = totalname) #need to fix if file is reloaded as input name, not as x.
}
fload <- function(filename) {
load(filename)
get(ls()[ls() != "filename"])
}
fshowdf <- function(x, ...) {
knitr::kable(x, digits = 2, "html", ...) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
kableExtra::scroll_box(width = "100%", height = "300px")
}
colorize <- function(x, color) {
sprintf("<span style='color: %s;'>%s</span>", color, x)
}
#the big one
fcolnet <- function(data = scholars, university = "RU", discipline = "sociology", waves = list(c(2015,
2018), c(2019, 2023)), type = c("first")) {
# step 1
demographics <- do.call(rbind.data.frame, data$demographics)
demographics <- demographics %>%
mutate(Universiteit1.22 = replace(Universiteit1.22, is.na(Universiteit1.22), ""), Universiteit2.22 = replace(Universiteit2.22,
is.na(Universiteit2.22), ""), Universiteit1.24 = replace(Universiteit1.24, is.na(Universiteit1.24),
""), Universiteit2.24 = replace(Universiteit2.24, is.na(Universiteit2.24), ""), discipline.22 = replace(discipline.22,
is.na(discipline.22), ""), discipline.24 = replace(discipline.24, is.na(discipline.24), ""))
sample <- which((demographics$Universiteit1.22 %in% university | demographics$Universiteit2.22 %in%
university | demographics$Universiteit1.24 %in% university | demographics$Universiteit2.24 %in%
university) & (demographics$discipline.22 %in% discipline | demographics$discipline.24 %in% discipline))
demographics_soc <- demographics[sample, ]
scholars_sel <- lapply(scholars, "[", sample)
# step 2
ids <- demographics_soc$au_id
nwaves <- length(waves)
nets <- array(0, dim = c(nwaves, length(ids), length(ids)), dimnames = list(wave = 1:nwaves, ids,
ids))
dimnames(nets)
# step 3
df_works <- tibble(works_id = unlist(lapply(scholars_sel$work, function(l) l$id)), works_author = unlist(lapply(scholars_sel$work,
function(l) l$author), recursive = FALSE), works_year = unlist(lapply(scholars_sel$work, function(l) l$publication_year),
recursive = FALSE))
df_works <- df_works[!duplicated(df_works), ]
# step 4
if (type == "first") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
ego <- df_works_w$works_author[i][[1]]$au_id[1]
alters <- df_works_w$works_author[i][[1]]$au_id[-1]
if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
}
}
}
}
if (type == "last") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
ego <- rev(df_works_w$works_author[i][[1]]$au_id)[1]
alters <- rev(df_works_w$works_author[i][[1]]$au_id)[-1]
if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
}
}
}
}
if (type == "all") {
for (j in 1:nwaves) {
df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
]
for (i in 1:nrow(df_works_w)) {
egos <- df_works_w$works_author[i][[1]]$au_id
if (sum(ids %in% egos) > 0) {
nets[j, which(ids %in% egos), which(ids %in% egos)] <- 1
}
}
}
}
output <- list()
output$data <- scholars_sel
output$nets <- nets
return(output)
}
fpackage.check(c("tidyverse", "RSiena"))## [[1]]
## NULL
##
## [[2]]
## NULL7 Add citations to the data
scholars <- fload("C:/Users/kalle/OneDrive/Documenten/REMA/Jaar 2/Social Networks/KS_labjournal/data/processed/scholars_20240924.rda")
scholars_net <- fcolnet(data = scholars,
university = c("RU", "UU", "RUG", "UvA", "VU", "EUR", "Leiden", "UvT"),
discipline = c("sociology", "political science"),
waves = list(c(2015, 2018), c(2019, 2023)),
type = c("all"))
df_ego <- do.call(rbind.data.frame, scholars_net$data$demographics)
df_citations <- fload("C:/Users/kalle/OneDrive/Documenten/REMA/Jaar 2/Social Networks/KS_labjournal/data/processed/df_20250929.rda")
#merging
df_ego <- df_ego |>
left_join(df_citations |> select(Naam, citations_w1, citations_w2),
by = "Naam")8 make ordinal variable function
table(df_ego$Functie.24, useNA = "always")##
## ?
## 1
## Adjunct hoogleraar
## 1
## Associate researcher
## 1
## Bijzonder hoogleraar
## 14
## Bijzonder Hoogleraar
## 2
## Datamanager
## 1
## Directeur SoG
## 1
## Docent
## 24
## Docent (gepensioneerd)
## 1
## Docent, phd
## 1
## Docent/PhD student
## 1
## Docent?
## 1
## Doctoral Researcher
## 2
## Doctoral student
## 1
## Does project at UvA, has worked as professor, lecturer, and research fellow at different unis
## 1
## Emeritus hoogleraar
## 12
## Emeritus hoogleraar (gast?)
## 1
## External Phd
## 6
## External PhD
## 1
## Fellow
## 1
## Gast/ Onderzoeker
## 1
## Gast/Emeritus universitair hoofddocent
## 1
## Gastonderzoeker
## 1
## Gepensioneerd docent sociologie
## 1
## Gepensioneerd UHD
## 1
## Guest Researcher
## 2
## Honorary professor
## 2
## Hoofdonderzoeker
## 1
## Hoogleraar
## 83
## Hoogleraar (gast)
## 1
## Hoogleraar (visiting)
## 1
## Hoogleraar + Universitair hoofddocent
## 1
## Hoogleraar/Afdelingsvoorzitter
## 1
## Junior docent
## 1
## Junior onderzoeker
## 1
## Lecturer
## 9
## Lecturer and Researcher
## 1
## Lecturer/researcher
## 1
## Managing director/co-founder SocioQuest
## 1
## Medewerker
## 1
## Onderwijsontwikkelaar
## 1
## Onderzoeker
## 34
## Onderzoeker/Lecturer
## 1
## Onderzoeksfellow
## 1
## Onderzoeksmedewerker
## 1
## Other researcher
## 1
## part-time doctoral researcher
## 1
## Phd & postdoc
## 1
## Phd Student
## 1
## PhD student
## 42
## PhD Student
## 112
## PhD Student anthropologie
## 1
## PhD student, Junior Docent
## 2
## PhD student/Gast
## 2
## PhD studentt
## 1
## Postdoc
## 22
## Postdoctoral Research Fellow
## 1
## Promovendus
## 1
## Research fellow
## 1
## Scholarship student
## 1
## Senior lecturer
## 3
## Senior Onderzoeker
## 1
## Senior Research Fellow
## 1
## Senior researcher
## 1
## Senior Researcher
## 1
## Senior researcher part-time
## 1
## Universitair docent
## 154
## Universitair Docent
## 3
## Universitair hoofddocent
## 84
## Universitair hoofddocent/Onderwijsdirecteur
## 1
## Unversitair docent GPM
## 1
## Wetenschappelijk directeur
## 1
## <NA>
## 8#categories:
#(bijzonder) hoogleraar, UHD, UD, postdoc/senior/docent/researcher, PhD (external).
df_ego <- df_ego |>
mutate(functie_level =
case_when(
grepl("hoogleraar|professor", tolower(Functie.24)) ~ 5,
grepl("universitair hoofddocent|uhd", tolower(Functie.24)) ~ 4,
grepl("universitair docent|unversitair docent", tolower(Functie.24)) ~ 3,
grepl("postdoc|senior|fellow", tolower(Functie.24)) ~ 2,
tolower(Functie.24) %in% c(
"docent", "lecturer", "onderzoeker", "onderzoeker/lecturer",
"lecturer and researcher", "lecturer/researcher",
"docent (gepensioneerd)", "docent?", "gast/ onderzoeker",
"gastonderzoeker", "gepensioneerd docent sociologie",
"guest researcher"
) ~ 2,
tolower(Functie.24) %in% c("doctoral researcher", "doctoral student", "part-time doctoral researcher") ~ 1,
grepl("phd|junior|promovendus", tolower(Functie.24)) ~ 1,
.default = NA
)
)
table(df_ego$functie_level, df_ego$Functie.24, useNA="always")##
## ? Adjunct hoogleraar Associate researcher Bijzonder hoogleraar Bijzonder Hoogleraar
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 14 2
## <NA> 1 0 1 0 0
##
## Datamanager Directeur SoG Docent Docent (gepensioneerd) Docent, phd Docent/PhD student
## 1 0 0 0 0 1 1
## 2 0 0 24 1 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## <NA> 1 1 0 0 0 0
##
## Docent? Doctoral Researcher Doctoral student
## 1 0 2 1
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## <NA> 0 0 0
##
## Does project at UvA, has worked as professor, lecturer, and research fellow at different unis
## 1 0
## 2 0
## 3 0
## 4 0
## 5 1
## <NA> 0
##
## Emeritus hoogleraar Emeritus hoogleraar (gast?) External Phd External PhD Fellow
## 1 0 0 6 1 0
## 2 0 0 0 0 1
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 12 1 0 0 0
## <NA> 0 0 0 0 0
##
## Gast/ Onderzoeker Gast/Emeritus universitair hoofddocent Gastonderzoeker
## 1 0 0 0
## 2 1 0 1
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## <NA> 0 0 0
##
## Gepensioneerd docent sociologie Gepensioneerd UHD Guest Researcher Honorary professor
## 1 0 0 0 0
## 2 1 0 2 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 2
## <NA> 0 0 0 0
##
## Hoofdonderzoeker Hoogleraar Hoogleraar (gast) Hoogleraar (visiting)
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 83 1 1
## <NA> 1 0 0 0
##
## Hoogleraar + Universitair hoofddocent Hoogleraar/Afdelingsvoorzitter Junior docent
## 1 0 0 1
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 1 1 0
## <NA> 0 0 0
##
## Junior onderzoeker Lecturer Lecturer and Researcher Lecturer/researcher
## 1 1 0 0 0
## 2 0 9 1 1
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## <NA> 0 0 0 0
##
## Managing director/co-founder SocioQuest Medewerker Onderwijsontwikkelaar Onderzoeker
## 1 0 0 0 0
## 2 0 0 0 34
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## <NA> 1 1 1 0
##
## Onderzoeker/Lecturer Onderzoeksfellow Onderzoeksmedewerker Other researcher
## 1 0 0 0 0
## 2 1 1 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## <NA> 0 0 1 1
##
## part-time doctoral researcher Phd & postdoc Phd Student PhD student PhD Student
## 1 1 0 1 42 112
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## <NA> 0 0 0 0 0
##
## PhD Student anthropologie PhD student, Junior Docent PhD student/Gast PhD studentt Postdoc
## 1 1 2 2 1 0
## 2 0 0 0 0 22
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## <NA> 0 0 0 0 0
##
## Postdoctoral Research Fellow Promovendus Research fellow Scholarship student Senior lecturer
## 1 0 1 0 0 0
## 2 1 0 1 0 3
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## <NA> 0 0 0 1 0
##
## Senior Onderzoeker Senior Research Fellow Senior researcher Senior Researcher
## 1 0 0 0 0
## 2 1 1 1 1
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## <NA> 0 0 0 0
##
## Senior researcher part-time Universitair docent Universitair Docent Universitair hoofddocent
## 1 0 0 0 0
## 2 1 0 0 0
## 3 0 154 3 0
## 4 0 0 0 84
## 5 0 0 0 0
## <NA> 0 0 0 0
##
## Universitair hoofddocent/Onderwijsdirecteur Unversitair docent GPM
## 1 0 0
## 2 0 0
## 3 0 1
## 4 1 0
## 5 0 0
## <NA> 0 0
##
## Wetenschappelijk directeur <NA>
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## <NA> 1 8table(df_ego$functie_level, useNA = "always")##
## 1 2 3 4 5 <NA>
## 177 112 158 87 120 20fsave(df_ego)9 First analyses (undirected network)
9.1 Homophily?
#Step 1: define data
wave1 <- scholars_net$nets[1,,]
wave2 <- scholars_net$nets[2,,]
##some checks
dim(wave1)## [1] 674 674dim(wave2)## [1] 674 674#should be 0
sum(is.na(wave1))## [1] 0#set diagonal to 0
sum(diag(wave2)==0)## [1] 26diag(wave1) <- 0
diag(wave2) <- 0
#only 1s and 0s
sum(wave1>1)## [1] 0#at least some 1s
sum(wave1>0)## [1] 940#make array
nets <- array(data = c(wave1, wave2), dim = c(dim(wave1), 2))
# dependent
net <- sienaDependent(nets)
#independent
functie <- coCovar(df_ego$functie_level)
mydata <- sienaDataCreate(net, functie)#Step 2: effects
myeff <- getEffects(mydata)
myeff## effectName include fix test initialValue parm
## 1 basic rate parameter net TRUE FALSE FALSE 4.52955 0
## 2 degree (density) TRUE FALSE FALSE -1.66572 0#Step 3: initial description
print01Report(mydata, modelname = "./results/secondtest_scholars")#step 4: specify model
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, gwesp, outAct)## effectNumber effectName shortName include fix test initialValue parm
## 1 21 GWESP (#) gwesp TRUE FALSE FALSE 0 69
## 2 39 degree of ego outAct TRUE FALSE FALSE 0 0myeff <- includeEffects(myeff, simX, interaction1 = "functie")## effectNumber effectName shortName include fix test initialValue parm
## 1 153 functie similarity simX TRUE FALSE FALSE 0 0myeff## effectName include fix test initialValue parm
## 1 basic rate parameter net TRUE FALSE FALSE 4.52955 0
## 2 degree (density) TRUE FALSE FALSE -1.66572 0
## 3 GWESP (#) TRUE FALSE FALSE 0.00000 69
## 4 degree of ego TRUE FALSE FALSE 0.00000 0
## 5 functie similarity TRUE FALSE FALSE 0.00000 0#Estimate
myAlgorithm <- sienaAlgorithmCreate(projname = "scholars")
EstM1 <- siena07(myAlgorithm, data = mydata, effects = myeff, returnDeps = TRUE)
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hY3Rpb24xID0gImZ1bmN0aWUiKQ0KDQpteWVmZg0KYGBgDQoNCmBgYHtyLCBldmFsPUZBTFNFLCBlY2hvPVRSVUV9DQojRXN0aW1hdGUNCm15QWxnb3JpdGhtIDwtIHNpZW5hQWxnb3JpdGhtQ3JlYXRlKHByb2puYW1lID0gInNjaG9sYXJzIikNCkVzdE0xIDwtIHNpZW5hMDcobXlBbGdvcml0aG0sIGRhdGEgPSBteWRhdGEsIGVmZmVjdHMgPSBteWVmZiwgcmV0dXJuRGVwcyA9IFRSVUUpDQoNCkVzdE0xDQoNCmBgYA0K