Analysis

Last compiled on augustus, 2024

rm(list = ls())
gc()

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, location = "./data/processed/", ...) {
    if (!dir.exists(location))
        dir.create(location)
    datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
    totalname <- paste(location, datename, file, sep = "")
    print(paste("SAVED: ", totalname, sep = ""))
    save(x, file = totalname)
}

fload <- function(fileName) {
    load(fileName)
    get(ls()[ls() != "fileName"])
}

# extrafont::font_import(paths = c('C:/Users/u244147/Downloads/Jost/', prompt = FALSE))
ftheme <- function() {

    # download font at https://fonts.google.com/specimen/Jost/
    theme_minimal(base_family = "Jost") + theme(panel.grid.minor = element_blank(), plot.title = element_text(family = "Jost",
        face = "bold"), axis.title = element_text(family = "Jost Medium"), axis.title.x = element_text(hjust = 0),
        axis.title.y = element_text(hjust = 1), strip.text = element_text(family = "Jost", face = "bold",
            size = rel(0.75), hjust = 0), strip.background = element_rect(fill = "grey90", color = NA),
        legend.position = "bottom")
}

fshowdf <- function(x, digits = 2, ...) {
    knitr::kable(x, digits = digits, "html", ...) %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
        kableExtra::scroll_box(width = "100%", height = "300px")
}

ffit <- function(formula, data) {
    tryCatch({
        model <- lme4::glmer(formula, data = data, family = binomial(link = "logit"), control = glmerControl(optimizer = "bobyqa",
            optCtrl = list(maxfun = 1e+05)))
        cat("Fitting model:", as.character(formula), "\n")
        summary(model)
        cat("\n")
        return(model)
    }, error = function(e) {
        cat("Error fitting model:", as.character(formula), "\n")
        cat("Error message:", conditionMessage(e), "\n")
        return(NULL)
    })
}

packages = c("tidyverse", "lme4", "lmtest", "car", "texreg", "ggplot2", "ggpubr", "ggh4x", "ggtext",
    "parallel")
fpackage.check(packages)
rm(packages)

# list files in processed data folder
list.files("./data/processed/")

# get todays date:
today <- gsub("-", "", Sys.Date())

# use fload
df <- fload(paste0("./data/processed/", today, "networkdata.Rda"))

#subset sports partners at t
df <- df[df$csn == 1,]

#outcome
df$y <- ifelse(df$Ycsn == 1, 1, 0)

#calculate rpelacement candidates: no. of other sports partners next to alter j, at time t, with whom ego does the same activity type
df$nreplace <- NA

for (i in unique(df$ego)) { 
  for (t in unique(df$period[df$ego == i])) { 
    for (j in unique(df$alterid[df$ego == i & df$period == t])) {
    
      #get activity type alter j does with ego at t
      activity <- df$sporttogether[df$ego == i & df$alterid == j & df$period == t]
    
      #get number of *other* alters with whom ego does the same activity at t (ie, replacement candidates)
      df$nreplace[df$ego == i & df$alterid == j & df$period == t] <- length(which(df$sporttogether[df$ego == i & !df$alterid == j & df$period == t] == activity))
    }
  }
}

df$proximity <- factor(df$proximity, levels = c("far","close","roommate"))
df$close <- ifelse(df$proximity == "close",1,0)
df$roommate <- ifelse(df$proximity == "roommate",1,0)
df$far <- ifelse(df$proximity == "far",1,0)

df$ego_context[is.na(df$ego_context)] <- "missing"
df$ego_context <- factor(df$ego_context, levels = c("club", "informal", "gym", "alone", "missing"))
df$club <- ifelse(df$ego_context == "club",1,0)
df$informal <- ifelse(df$ego_context == "informal",1,0)
df$gym <- ifelse(df$ego_context == "gym",1,0)
df$alone <- ifelse(df$ego_context == "alone",1,0)
df$missing <- ifelse(df$ego_context == "missing",1,0)

df$HH <- ifelse(df$ego_grade > 7 & df$alter_grade > 7, 1, 0)
df$HM <- ifelse( ((df$ego_grade > 7 & df$alter_grade > 5 & df$alter_grade < 8) | (df$alter_grade > 7 & df$ego_grade > 5 & df$ego_grade < 8)), 1, 0)
df$HL <- ifelse( ((df$ego_grade > 7 & df$alter_grade < 6) | (df$alter_grade > 7 & df$ego_grade < 6)), 1, 0)
df$MM <- ifelse( ((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade > 5 & df$alter_grade < 8)), 1, 0)
df$ML <- ifelse( ((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade < 6) | (df$ego_grade < 6 & df$alter_grade < 8 & df$alter_grade > 5)), 1, 0)
df$LL <- ifelse( ((df$ego_grade < 6 & df$alter_grade < 6)), 1, 0)

df$skills <- factor(
  1 * (((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade > 5 & df$alter_grade < 8))) +  # MM
  2 * (df$ego_grade > 7 & df$alter_grade > 7) +        # HH
  3 * (((df$ego_grade > 7 & df$alter_grade > 5 & df$alter_grade < 8) | (df$alter_grade > 7 & df$ego_grade > 5 & df$ego_grade < 8))) +  # HM
  4 * (((df$ego_grade > 7 & df$alter_grade < 6) | (df$alter_grade > 7 & df$ego_grade < 6))) +  # HL
  5 * (((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade < 6) | (df$ego_grade < 6 & df$alter_grade < 8 & df$alter_grade > 5))) +  # ML
  6 * (((df$ego_grade < 6 & df$alter_grade < 6))),        # LL
  levels = c(1, 2, 3, 4, 5, 6),
  labels = c("MM", "HH", "HM", "HL", "ML", "LL")
)

df$dif_skill <- abs(df$ego_grade - df$alter_grade)

df$alter_freq2 <- ifelse(df$alter_freq == "1 keer per week", 1,
                        ifelse(df$alter_freq == "2 of 3 keer per week", 2.5,
                             ifelse(df$alter_freq == "4 of 5 keer per week", 4.5,
                                    ifelse(df$alter_freq == "6 keer per week of vaker", 6.5,
                                           ifelse(df$alter_freq == "Minder dan 1 keer per maand", 0.125,
                                                  ifelse(df$alter_freq == "1 of 2 keer per maand", 0.375,
                                                         ifelse(df$alter_freq == "1 of 2 keer per maand", 0.375, NA)))))))
df$ego_freq <- as.numeric(df$ego_freq)
df$ego_activew1 <- ifelse(df$ego_meanfreq >0, 1, 0)

df <- df %>%
  mutate(mean_freq = rowMeans(select(., alter_freq2, ego_freq), na.rm = TRUE))

#df$ego_meanskill[is.na(df$ego_meanskill)] <- mean(df$ego_meanskill[which(!duplicated(df$ego))], na.rm=TRUE )

df$alter_age <- as.numeric(df$alter_age)

df$ff <- ifelse(df$ego_female == 1 & df$alter_female == 1, 1, 0)
df$fm <- ifelse( ((df$ego_female == 1 & df$alter_female == 0) | (df$ego_female == 0 & df$alter_female == 1)), 1, 0)
df$mm <- ifelse(df$ego_female == 0 & df$alter_female == 0, 1, 0)

df$gender <- factor(
  1 * (df$ego_female == 0 & df$alter_female == 0) +  # MM
  2 * ((df$ego_female == 1 & df$alter_female == 0) | (df$ego_female == 0 & df$alter_female == 1)) +  # FM
  3 * (df$ego_female == 1 & df$alter_female == 1),  # FF
  levels = c(1, 2, 3),
  labels = c("MM", "FM", "FF")
)

df$ego_quit[is.na(df$ego_quit)] <- 0

#exclude kin
df[df$kin == "0",] -> df

#types of sport
df$fitness <- ifelse(df$sporttogether == 1,1,0)
df$endurance <- ifelse(df$sporttogether %in% c(2,5,7), 1, 0)
df$team <- ifelse(df$sporttogether %in% c(3,10,12), 1, 0)
df$misc <- ifelse(!df$sporttogether %in% c(1,2,5,7,3,10,12),1,0)

df$sporttype <- factor(
  1 * (df$fitness == 1) +
  2 * (df$endurance == 1) +
  3 * (df$team == 1) +
  4 * (df$misc == 1),
  levels = c(1:4),
  labels = c("fitness", "endurance", "team", "miscellaneous")
)

# listwise deletion
df %>%
    select(proximity, roommate, close, far, frequency.t, closeness.t, duration, csn, bff, study, cdn,
        gender, mm, fm, ff, period, y, ego, alterid, ego_activew1, mean_freq, skills, HH, HM, HL, MM,
        ML, LL, ego_grade, dif_skill, ego_context, club, informal, gym, alone, missing, sporttogether,
        nreplace, sporttype, fitness, endurance, team, misc) %>%
    filter(complete.cases(.)) -> df

# describe
df %>%
    select(-c(proximity, gender, skills, ego_context, ego, alterid, ego_activew1, sporttogether, sporttype)) %>%
    psych::describe() %>%
    fshowdf(caption = "descriptive statistics of ego's (non-kin) social relations WITHIN SPORTS at time t")

length(unique(paste0(df$ego, "X", df$alterid)))  #N_alter =1222

#> [1] 1222

nrow(df)  #N_observation = 1426

#> [1] 1426

#tie continuation:

formula <- list(
  #model 1: main predictors
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period),
  
  #model 2: control for sports behavior dyad
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq),
  
  #model 3: include "traditional" dyadic controls
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender,
  
  #model 4: add other relational dimensions (multiplexity)
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill)  + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study,
  
  #model 5: add replacement candidates
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study + scale(nreplace)
  )

ans <- lapply(formula, ffit, data = df)
lapply(ans, summary)
do.call(lmtest::lrtest,ans)

df %>%
    select(sporttogether) %>%
    table(.) %>%
    prop.table(.)

# of all sports partner observations: 27% are in fitness; this is the most popular sports in dyads
# 11% are in endurance sports (i.e., running, cycling, swimming) 15% are in team (ball) sports
# (i.e., football, hockey, volleybal)

# 1. solution for fitness partners:
ans_fitness <- lapply(formula, ffit, data = df[df$sporttogether == 1, ])
lapply(ans_fitness, summary)

# 2. solution excluding fitness partners:
ans_nfitness <- lapply(formula, ffit, data = df[!df$sporttogether == 1, ])
lapply(ans_nfitness, summary)

# 3. solution for endurance sports partners:
ans_endurance <- lapply(formula, ffit, data = df[df$sporttogether %in% c(2, 5, 7), ])
lapply(ans_endurance, summary)

# 4. solution for team (ball) sports partners:
ans_team <- lapply(formula, ffit, data = df[df$sporttogether %in% c(3, 10, 12), ])
lapply(ans_team, summary)

# 2. tie maintenance
dfclose1 <- dfclose0 <- df
dfroommate1 <- dfroommate0 <- df

dfclose1$proximity <- "close"
dfclose0$proximity <- "far"
dfroommate1$proximity <- "roommate"
dfroommate0$proximity <- "far"

s <- 0.001
dfclosenessplus <- dfclosenessmin <- df
dfclosenessplus$closeness.t <- df$closeness.t + s
dfclosenessmin$closeness.t <- df$closeness.t - s

dffriend1 <- dffriend0 <- df
dfstudy1 <- dfstudy0 <- df
dfcdn1 <- dfcdn0 <- df
dffriend1$bff <- 1
dffriend0$bff <- 0
dfstudy1$study <- 1
dfstudy0$study <- 0
dfcdn1$cdn <- 1
dfcdn0$cdn <- 0

dfwomen1 <- dfwomen0 <- dfmixed1 <- dfmixed0 <- df
dfwomen1$gender <- "FF"
dfwomen0$gender <- "MM"
dfmixed1$gender <- "FM"
dfmixed0$gender <- "MM"

dfdurationplus <- dfdurationmin <- df
dfdurationplus$duration <- df$duration + s
dfdurationmin$duration <- df$duration - s

dffrequencyplus <- dffrequencymin <- df
dffrequencyplus$frequency.t <- df$frequency.t + s
dffrequencymin$frequency.t <- df$frequency.t - s

dfperiod21 <- dfperiod20 <- df
dfperiod21$period <- 2
dfperiod20$period <- 1

dfmeanfreqplus <- dfmeanfreqmin <- df
dfmeanfreqplus$mean_freq <- df$mean_freq + s
dfmeanfreqmin$mean_freq <- df$mean_freq - s

# dfquit1 <- dfquit0 <- df dfquit1$ego_quit <- 1 dfquit0$ego_quit <- 0

# dfHH1 <- dfHH0 <- dfHM1 <- dfHM0 <- dfHL1 <- dfHL0 <- dfML1 <- dfML0 <- dfLL1 <- dfLL0 <- df
# dfHH1$skills <- 'HH' dfHH0$skills <- 'MM' dfHM1$skills <- 'HM' dfHM0$skills <- 'MM' dfHL1$skills
# <- 'HL' dfHL0$skills <- 'MM' dfML1$skills <- 'ML' dfML0$skills <- 'MM' dfLL1$skills <- 'LL'
# dfLL0$skills <- 'MM'

dfegoskillplus <- dfegoskillmin <- df
dfegoskillplus$ego_grade <- df$ego_grade + s
dfegoskillmin$ego_grade <- df$ego_grade - s

dfskilldifplus <- dfskilldifmin <- df
dfskilldifplus$dif_skill <- df$dif_skill + s
dfskilldifmin$dif_skill <- df$dif_skill - s

dfinformal1 <- dfinformal0 <- dfgym1 <- dfgym0 <- dfalone1 <- dfalone0 <- dfmissing1 <- dfmissing0 <- df
dfinformal1$ego_context <- "informal"
dfinformal0$ego_context <- "club"
dfgym1$ego_context <- "gym"
dfgym0$ego_context <- "club"
dfalone1$ego_context <- "alone"
dfalone0$ego_context <- "club"
dfmissing1$ego_context <- "missing"
dfmissing0$ego_context <- "club"

dfreplaceplus <- dfreplacemin <- df
dfreplaceplus$nreplace <- df$nreplace + s
dfreplacemin$nreplace <- df$nreplace - s

fpred <- function(x) {
    me_close <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclose1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfclose0)
    ame_close <- mean(me_close)

    me_roommate <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfroommate1) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfroommate0)
    ame_roommate <- mean(me_roommate)

    me_closeness <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclosenessplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclosenessmin))/(2 * s)
    ame_closeness <- mean(me_closeness)

    me_friend <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffriend1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dffriend0)
    ame_friend <- mean(me_friend)

    me_study <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfstudy1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfstudy0)
    ame_study <- mean(me_study)

    me_cdn <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfcdn1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfcdn0)
    ame_cdn <- mean(me_cdn)

    me_women <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfwomen1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfwomen0)
    ame_women <- mean(me_women)

    me_mixed <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmixed1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfmixed0)
    ame_mixed <- mean(me_mixed)

    me_duration <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfdurationplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfdurationmin))/(2 * s)
    ame_duration <- mean(me_duration)

    me_frequency <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffrequencyplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffrequencymin))/(2 * s)
    ame_frequency <- mean(me_frequency)

    me_period2 <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfperiod21) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfperiod20)
    ame_period2 <- mean(me_period2)

    # me_quit <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata = dfquit1) -
    # lme4:::predict.merMod(x, #type = 'response', re.form = NULL, newdata = dfquit0) ame_quit <-
    # mean(me_quit)

    # me_HH <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata = dfHH1) -
    # lme4:::predict.merMod(x, type = #'response', re.form = NULL, newdata = dfHH0) ame_HH <-
    # mean(me_HH) me_HM <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata =
    # dfHM1) - lme4:::predict.merMod(x, type = #'response', re.form = NULL, newdata = dfHM0) ame_HM
    # <- mean(me_HM) me_HL <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata =
    # dfHL1) - lme4:::predict.merMod(x, type = #'response', re.form = NULL, newdata = dfHL0) ame_HL
    # <- mean(me_HL) me_ML <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata =
    # dfML1) - lme4:::predict.merMod(x, type = #'response', re.form = NULL, newdata = dfML0) ame_ML
    # <- mean(me_ML) me_LL <- lme4:::predict.merMod(x, type = 'response', re.form = NULL, newdata =
    # dfLL1) - lme4:::predict.merMod(x, type = #'response', re.form = NULL, newdata = dfLL0) ame_LL
    # <- mean(me_LL)

    me_egoskill <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfegoskillplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfegoskillmin))/(2 * s)
    ame_egoskill <- mean(me_egoskill)

    me_skilldif <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfskilldifplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfskilldifmin))/(2 * s)
    ame_skilldif <- mean(me_skilldif)

    me_informal <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfinformal1) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfinformal0)
    ame_informal <- mean(me_informal)

    me_gym <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfgym1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfgym0)
    ame_gym <- mean(me_gym)

    me_alone <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfalone1) - lme4:::predict.merMod(x,
        type = "response", re.form = NULL, newdata = dfalone0)
    ame_alone <- mean(me_alone)

    me_missing <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmissing1) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmissing0)
    ame_missing <- mean(me_missing)

    me_replace <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfreplaceplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfreplacemin))/(2 * s)
    ame_replace <- mean(me_replace)

    me_meanfreq <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmeanfreqplus) -
        lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmeanfreqmin))/(2 * s)
    ame_meanfreq <- mean(me_meanfreq)

    c(ame_close, ame_roommate, ame_closeness, ame_friend, ame_study, ame_cdn, ame_women, ame_mixed, ame_duration,
        ame_frequency, ame_period2, ame_egoskill, ame_skilldif, ame_informal, ame_gym, ame_alone, ame_missing,
        ame_replace, ame_meanfreq)
}

# fpred(ans[[5]])

seed <- 242523
nIter <- 500
nCore <- detectCores() - 1
mycl <- makeCluster(rep("localhost", nCore))
clusterEvalQ(mycl, library(lme4))
clusterExport(mycl, varlist = c("ans", "s", "df", "dfclose1", "dfclose0", "dfroommate1", "dfroommate0",
    "dfclosenessplus", "dfclosenessmin", "dffriend1", "dffriend0", "dfstudy1", "dfstudy0", "dfcdn1",
    "dfcdn0", "dfwomen1", "dfwomen0", "dfmixed1", "dfmixed0", "dfdurationplus", "dfdurationmin", "dffrequencyplus",
    "dffrequencymin", "dfperiod21", "dfperiod20", "dfmeanfreqplus", "dfmeanfreqmin", "dfegoskillmin",
    "dfegoskillplus", "dfskilldifmin", "dfskilldifplus", "dfinformal1", "dfinformal0", "dfgym1", "dfgym0",
    "dfalone1", "dfalone0", "dfmissing1", "dfmissing0", "dfreplaceplus", "dfreplacemin"))

system.time(boo_m <- bootMer(ans[[5]], fpred, nsim = nIter, parallel = "snow", ncpus = nCore, cl = mycl,
    seed = seed))

stopCluster(mycl)

save(boo_m, file = "./results/bootm.Rda")

load("./results/bootm.Rda")

#tie formation
#plotdata1 <- data.frame(
#  pred = c("Outside municipality", "Same municipality", "Same house", "*Emotional closeness*", "Friendship", "Study partnership", "Confidant", "Male dyad", "Female dyad", "Mixed dyad", #"*Years known*", "*Communication frequency*", "Ego residential change", "Ego study transition", "Period 1", "Period 2"),
#  Outcome = "Tie formation",
#  ame = c(0, booL[[1]]$t0[1:6], 0, booL[[1]]$t0[7:12], 0, booL[[1]]$t0[13]),
#  ame_se = c(0, apply(booL[[1]]$t, 2, sd)[1:6], 0, apply(booL[[1]]$t, 2, sd)[7:12], 0, apply(booL[[1]]$t, 2, sd)[13]),
#  ref = c(1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0))

#tie maintenance

plotdata <- data.frame(
  pred = c("Outside municipality", "Same municipality", "Same house", 
           "*Emotional closeness*", "Friendship", "Study partnership", "Confidant", "Male dyad", "Female dyad", "Mixed dyad", "*Years known*", "*Communication frequency*", "Period 1", "Period 2", "*Ego skill*", "*Ego-alter skill difference*", "Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing", "*No. of replacement candidates*", "*Sports frequency dyad*" ),
  #Outcome = "Tie maintenance",
  ame = c(0, boo_m$t0[1:6], 0, boo_m$t0[7:10], 0, boo_m$t0[11:13], 0,  boo_m$t0[14:19] ),
  
  ame_se = c(0, apply(boo_m$t, 2, sd)[1:6], 0, apply(boo_m$t, 2, sd)[7:10], 0, apply(boo_m$t, 2, sd)[11:13], 0, apply(boo_m$t, 2, sd)[14:19]),
  
  ref = c(1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0))

plotdata$pred <- factor(plotdata$pred, levels = rev(c(
  "*Ego-alter skill difference*",  "*Emotional closeness*", "Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing", "Outside municipality", "Same municipality", "Same house", "*Communication frequency*","*Years known*", "Male dyad", "Female dyad", "Mixed dyad", "Friendship", "Confidant", "Study partnership", "*Ego skill*", "*Sports frequency dyad*", "*No. of replacement candidates*", "Period 1", "Period 2")))
  
plotdata <- plotdata[order(plotdata$pred),]
row.names(plotdata) <- 1:nrow(plotdata)
plotdata$ref <- as.factor(plotdata$ref)

#also include coefficients as labels, but leave out the labels for the reference level
plotdata$label <- ifelse(plotdata$ref == 1, 0, 1)

#in main text, only main predictors..
plotdata2 <- plotdata[plotdata$pred %in% c( "*Ego-alter skill difference*",  "*Emotional closeness*","Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing"),]

p <- ggplot(plotdata2, aes(x = ame, y = pred, #color = Outcome, 
                           shape = ref)) +
  geom_vline(xintercept = 0, color = "grey") +
  geom_point() +
  geom_errorbar(data = subset(plotdata2, label == 1), aes(xmin = ame - 1.96*ame_se, xmax = ame + 1.96*ame_se), width = 0, linewidth = 0.3) +
  geom_text(data = subset(plotdata2, label == 1), aes(label = sprintf("%0.2f (%0.2f)", ame, ame_se )), size = 3, color = "black", position = position_nudge(y = 0.4)) +
  #facet_grid(Outcome ~., switch = "y", scales = "free_y", space = "free_y") +
  labs(x = "AME", y = NULL) +
  scale_x_continuous(labels = scales::percent) +
  scale_shape_manual("", values = c("1" = 1, "0" = 16)) +
  theme(axis.line = element_line(), 
        legend.position = "none", 
        strip.background = element_blank(),
        strip.text.x = element_text(face = "bold"),
        strip.text.y = element_blank(),
        axis.text.y = element_markdown()) + guides(shape = "none")

ggsave("./figures/ames.png", height = 2.5)

plotdata %>% 
  arrange(desc(row_number())) %>%
  select(-label) %>%
  fshowdf(digits = 3, caption = "Average marginal effects on sports partnership maintenance")

#now with all controls:
p2 <- ggplot(plotdata, aes(x = ame, y = pred, #color = Outcome, 
                           shape = ref)) +
  geom_vline(xintercept = 0, color = "grey") +
  geom_point() +
  geom_errorbar(data = subset(plotdata, label == 1), aes(xmin = ame - 1.96*ame_se, xmax = ame + 1.96*ame_se), width = 0, linewidth = 0.3) +
  geom_text(data = subset(plotdata, label == 1), aes(label = sprintf("%0.2f (%0.2f)", ame, ame_se )), size = 2, color = "black", position = position_nudge(y = 0.4)) +
  #facet_grid(Outcome ~., switch = "y", scales = "free_y", space = "free_y") +
  labs(x = "AME", y = NULL) +
  scale_x_continuous(labels = scales::percent) +
  scale_shape_manual("", values = c("1" = 1, "0" = 16)) +
  #ftheme() +
  theme(axis.line = element_line(), 
        legend.position = "none", 
        strip.background = element_blank(),
        strip.text.x = element_text(face = "bold"),
        strip.text.y = element_blank(),
        axis.text.y = element_markdown()) + guides(shape = "none")

ggsave("./figures/ames_incl_control.png", height = 5)
#knitr::include_graphics("./figures/ames_incl_control.png")

print(p2) 

---
title: "Analysis"
bibliography: references.bib
link-citations: true
date: "Last compiled on `r format(Sys.time(), '%B, %Y')`"
output: 
  html_document:
    css: tweaks.css
    toc:  true
    toc_float: true
    number_sections: true
    toc_depth: 2
    code_folding: show
    code_download: yes
---

```{r, globalsettings, echo=FALSE, warning=FALSE, results='hide',message=FALSE}
library(knitr)
library(tidyverse)
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("test3"))
options(width = 100)
rgl::setupKnitr()

colorize <- function(x, color) {sprintf("<span style='color: %s;'>%s</span>", color, x) }
```


```{r klippy, echo=FALSE, include=TRUE}
klippy::klippy(position = c('top', 'right'))
#klippy::klippy(color = 'darkred')
#klippy::klippy(tooltip_message = 'Click to copy', tooltip_success = 'Done')
```



---  
  
# Getting started

To copy the code, click the button in the upper right corner of the code-chunks.

## clean up

```{r, eval=FALSE, results='hide'}
rm(list=ls())
gc()
```

<br>

## general custom functions

- `fpackage.check`: Check if packages are installed (and install if not) in R
- `fsave`: Function to save data with time stamp in correct directory
- `fload`: Function to load R-objects under new names
- `ftheme`: pretty ggplot2 theme
- `fshowdf`: Print objects (`tibble` / `data.frame`) nicely on screen in `.Rmd`.
- `ffit`: fit a series of (here, generalized linear mixed-effects) models 

```{r}
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, location = "./data/processed/", ...) {
    if (!dir.exists(location))
        dir.create(location)
    datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
    totalname <- paste(location, datename, file, sep = "")
    print(paste("SAVED: ", totalname, sep = ""))
    save(x, file = totalname)
}

fload  <- function(fileName){
  load(fileName)
  get(ls()[ls() != "fileName"])
}

#extrafont::font_import(paths = c("C:/Users/u244147/Downloads/Jost/", prompt = FALSE))
ftheme <- function() {
  
  #download font at https://fonts.google.com/specimen/Jost/
  theme_minimal(base_family = "Jost") +
    theme(panel.grid.minor = element_blank(),
          plot.title = element_text(family = "Jost", face = "bold"),
          axis.title = element_text(family = "Jost Medium"),
          axis.title.x = element_text(hjust = 0),
          axis.title.y = element_text(hjust = 1),
          strip.text = element_text(family = "Jost", face = "bold",
                                    size = rel(0.75), hjust = 0),
          strip.background = element_rect(fill = "grey90", color = NA),
          legend.position = "bottom")
}

fshowdf <- function(x, digits = 2, ...) {
    knitr::kable(x, digits = digits, "html", ...) %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
        kableExtra::scroll_box(width = "100%", height = "300px")
}

ffit <- function(formula, data) {
  tryCatch({
    model <- lme4::glmer(formula, data = data, family = binomial(link = "logit"),
                   control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e5)))
    cat("Fitting model:", as.character(formula), "\n")
    summary(model)
    cat("\n")
    return(model)
  }, error = function(e) {
    cat("Error fitting model:", as.character(formula), "\n")
    cat("Error message:", conditionMessage(e), "\n")
    return(NULL)
  })
}
```

```{r fonts, echo=FALSE, warning=FALSE, results='hide'}
# import font JOST
#extrafont::font_import(pattern = "Jost")
extrafont::loadfonts(device="win")

# Set default theme and font stuff
theme_set(ftheme())
update_geom_defaults("text", list(family = "Jost", fontface = "plain"))
update_geom_defaults("label", list(family = "Jost", fontface = "plain"))

#nice color palette
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
```

<br>


## necessary packages

- `tidyverse`
- `lme4`: fitting random effects models
- `lmtest`: diagnostics test (likelihood ratio test)
- `car`: companion applied regression (calculate VIF)
- `texreg`: output to HTML table
- `ggplot2` 
- `ggpubr`: format ggplot2 plots
- `ggh4x`: hacks for ggplot2
- `ggtext`: text rendering
- `parallel`: parallel computing


```{r, results='hide', message=FALSE, warning=FALSE}
packages = c("tidyverse", "lme4","lmtest","car","texreg","ggplot2","ggpubr","ggh4x","ggtext", "parallel")
fpackage.check(packages)
rm(packages)
``` 

<br>

## load data-set

Load the replicated data-set. To load these file, adjust the filename in the following code so that it matches the most recent version of the `.RDa` file you have in your `./data/processed/` folder.

You may also obtain them by downloading: `r xfun::embed_file("./data shared/networkdata.Rda")`


```{r, results = 'hide'}
#list files in processed data folder
list.files("./data/processed/")

#get todays date:
today <- gsub("-", "", Sys.Date())

#use fload
df <- fload(paste0("./data/processed/", today, "networkdata.Rda"))
```



<br>

## last alterations

- subset sports partners at t to study tie maintenance at t+1
- binary outcome (sports partner: yes/no; 1/0)
- calculate no. of 'replacement candidates' (i.e., no. of other sports partners, other than alter j, with whom ego does the sport type he/she does with alter)
- proximity levels
- sports settings
- dyadic skill category combinations (HH, HM, HL, ML, MM, LL)
- also difference score in skill
- alter sports frequency and ego/alter mean
- gender composition dyad (MM, FM, FF)
- type of sport (fitness, endurance, team, miscellaneous)

```{r}
#subset sports partners at t
df <- df[df$csn == 1,]

#outcome
df$y <- ifelse(df$Ycsn == 1, 1, 0)

#calculate rpelacement candidates: no. of other sports partners next to alter j, at time t, with whom ego does the same activity type
df$nreplace <- NA

for (i in unique(df$ego)) { 
  for (t in unique(df$period[df$ego == i])) { 
    for (j in unique(df$alterid[df$ego == i & df$period == t])) {
    
      #get activity type alter j does with ego at t
      activity <- df$sporttogether[df$ego == i & df$alterid == j & df$period == t]
    
      #get number of *other* alters with whom ego does the same activity at t (ie, replacement candidates)
      df$nreplace[df$ego == i & df$alterid == j & df$period == t] <- length(which(df$sporttogether[df$ego == i & !df$alterid == j & df$period == t] == activity))
    }
  }
}

df$proximity <- factor(df$proximity, levels = c("far","close","roommate"))
df$close <- ifelse(df$proximity == "close",1,0)
df$roommate <- ifelse(df$proximity == "roommate",1,0)
df$far <- ifelse(df$proximity == "far",1,0)

df$ego_context[is.na(df$ego_context)] <- "missing"
df$ego_context <- factor(df$ego_context, levels = c("club", "informal", "gym", "alone", "missing"))
df$club <- ifelse(df$ego_context == "club",1,0)
df$informal <- ifelse(df$ego_context == "informal",1,0)
df$gym <- ifelse(df$ego_context == "gym",1,0)
df$alone <- ifelse(df$ego_context == "alone",1,0)
df$missing <- ifelse(df$ego_context == "missing",1,0)

df$HH <- ifelse(df$ego_grade > 7 & df$alter_grade > 7, 1, 0)
df$HM <- ifelse( ((df$ego_grade > 7 & df$alter_grade > 5 & df$alter_grade < 8) | (df$alter_grade > 7 & df$ego_grade > 5 & df$ego_grade < 8)), 1, 0)
df$HL <- ifelse( ((df$ego_grade > 7 & df$alter_grade < 6) | (df$alter_grade > 7 & df$ego_grade < 6)), 1, 0)
df$MM <- ifelse( ((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade > 5 & df$alter_grade < 8)), 1, 0)
df$ML <- ifelse( ((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade < 6) | (df$ego_grade < 6 & df$alter_grade < 8 & df$alter_grade > 5)), 1, 0)
df$LL <- ifelse( ((df$ego_grade < 6 & df$alter_grade < 6)), 1, 0)

df$skills <- factor(
  1 * (((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade > 5 & df$alter_grade < 8))) +  # MM
  2 * (df$ego_grade > 7 & df$alter_grade > 7) +        # HH
  3 * (((df$ego_grade > 7 & df$alter_grade > 5 & df$alter_grade < 8) | (df$alter_grade > 7 & df$ego_grade > 5 & df$ego_grade < 8))) +  # HM
  4 * (((df$ego_grade > 7 & df$alter_grade < 6) | (df$alter_grade > 7 & df$ego_grade < 6))) +  # HL
  5 * (((df$ego_grade > 5 & df$ego_grade < 8 & df$alter_grade < 6) | (df$ego_grade < 6 & df$alter_grade < 8 & df$alter_grade > 5))) +  # ML
  6 * (((df$ego_grade < 6 & df$alter_grade < 6))),        # LL
  levels = c(1, 2, 3, 4, 5, 6),
  labels = c("MM", "HH", "HM", "HL", "ML", "LL")
)

df$dif_skill <- abs(df$ego_grade - df$alter_grade)

df$alter_freq2 <- ifelse(df$alter_freq == "1 keer per week", 1,
                        ifelse(df$alter_freq == "2 of 3 keer per week", 2.5,
                             ifelse(df$alter_freq == "4 of 5 keer per week", 4.5,
                                    ifelse(df$alter_freq == "6 keer per week of vaker", 6.5,
                                           ifelse(df$alter_freq == "Minder dan 1 keer per maand", 0.125,
                                                  ifelse(df$alter_freq == "1 of 2 keer per maand", 0.375,
                                                         ifelse(df$alter_freq == "1 of 2 keer per maand", 0.375, NA)))))))
df$ego_freq <- as.numeric(df$ego_freq)
df$ego_activew1 <- ifelse(df$ego_meanfreq >0, 1, 0)

df <- df %>%
  mutate(mean_freq = rowMeans(select(., alter_freq2, ego_freq), na.rm = TRUE))

#df$ego_meanskill[is.na(df$ego_meanskill)] <- mean(df$ego_meanskill[which(!duplicated(df$ego))], na.rm=TRUE )

df$alter_age <- as.numeric(df$alter_age)

df$ff <- ifelse(df$ego_female == 1 & df$alter_female == 1, 1, 0)
df$fm <- ifelse( ((df$ego_female == 1 & df$alter_female == 0) | (df$ego_female == 0 & df$alter_female == 1)), 1, 0)
df$mm <- ifelse(df$ego_female == 0 & df$alter_female == 0, 1, 0)

df$gender <- factor(
  1 * (df$ego_female == 0 & df$alter_female == 0) +  # MM
  2 * ((df$ego_female == 1 & df$alter_female == 0) | (df$ego_female == 0 & df$alter_female == 1)) +  # FM
  3 * (df$ego_female == 1 & df$alter_female == 1),  # FF
  levels = c(1, 2, 3),
  labels = c("MM", "FM", "FF")
)

df$ego_quit[is.na(df$ego_quit)] <- 0

#exclude kin
df[df$kin == "0",] -> df

#types of sport
df$fitness <- ifelse(df$sporttogether == 1,1,0)
df$endurance <- ifelse(df$sporttogether %in% c(2,5,7), 1, 0)
df$team <- ifelse(df$sporttogether %in% c(3,10,12), 1, 0)
df$misc <- ifelse(!df$sporttogether %in% c(1,2,5,7,3,10,12),1,0)

df$sporttype <- factor(
  1 * (df$fitness == 1) +
  2 * (df$endurance == 1) +
  3 * (df$team == 1) +
  4 * (df$misc == 1),
  levels = c(1:4),
  labels = c("fitness", "endurance", "team", "miscellaneous")
)
```

---

<br>

# Descriptives

```{r, eval=TRUE}
#listwise deletion
df %>% 
  select(proximity, roommate, close, far, frequency.t, closeness.t, duration, csn, bff, study, cdn, gender, mm, fm, ff, period, y, ego, alterid, ego_activew1,
         mean_freq, skills, HH, HM, HL, MM, ML, LL, ego_grade, dif_skill, ego_context, club, informal, gym, alone, missing, sporttogether, nreplace, sporttype, fitness, endurance, team, misc) %>%
  filter(complete.cases(.)) -> df

#describe
df %>%
  select(-c(proximity, gender, skills, ego_context, ego, alterid, ego_activew1, sporttogether, sporttype)) %>%
  psych::describe() %>%
  fshowdf(caption="descriptive statistics of ego's (non-kin) social relations WITHIN SPORTS at time t")

length(unique(paste0(df$ego,"X",df$alterid))) #N_alter =1222
nrow(df) #N_observation = 1426
```

<br>

----

# Multivariate analyses

## logistic regression

```{r,eval=FALSE}
#tie continuation:

formula <- list(
  #model 1: main predictors
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period),
  
  #model 2: control for sports behavior dyad
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq),
  
  #model 3: include "traditional" dyadic controls
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender,
  
  #model 4: add other relational dimensions (multiplexity)
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill)  + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study,
  
  #model 5: add replacement candidates
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period) + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study + scale(nreplace)
  )

ans <- lapply(formula, ffit, data = df)
lapply(ans, summary)
do.call(lmtest::lrtest,ans)
```

```{r, eval=FALSE, echo=FALSE}
texreg::htmlreg(ans,
        file="./results/coeftab_maintenance.html",
        caption="Results of random effects models predicting sports partnership maintenance at t+1 (1=yes, 0=no)", caption.above = TRUE,
        custom.model.names = c("M1: main predictors", "M2: sports beh. dyad", "M3: dyadic covars", "M4: multiplexity", "M5: replacement candidates"),
       custom.coef.names = c("(Intercept)", 
                             "Ego skill", "Ego-alter skill diference", 
                             "Emotional closeness", "Informal group", "Commercial gym", "Unorganized", "Missing", 
                             "Period: waves 2-3",  "Mean sports frequency dyad", "Same municipality", "Roommate", "Communication frequency", "Years known",
                             "Woman-man", "Woman-woman", "Friendship", "Confidant", "Study partner", "No. of replacement candidates"),
       digits=2, single.row = TRUE
        )
```

<br>

## output

```{r, echo = FALSE}
htmltools::tags$div(
  style = "height: 600px; overflow-y: scroll;",
  htmltools::includeHTML("./results/coeftab_maintenance.html")
)
```

<br>

## robustness checks

### patterns depend on type of sport? {.tabset .tabset-fade}

We compare results in sports partnerships in vs. outside fitness.

```{r, eval=FALSE}
df %>%
  select(sporttogether) %>%
  table(.) %>%
  prop.table(.)

#of all sports partner observations:
#27% are in fitness; this is the most popular sports in dyads
#11% are in endurance sports (i.e., running, cycling, swimming)
#15% are in team (ball) sports (i.e., football, hockey, volleybal)

#1. solution for fitness partners:
ans_fitness <- lapply(formula, ffit, data = df[df$sporttogether == 1,])
lapply(ans_fitness, summary)

#2. solution excluding fitness partners:
ans_nfitness <- lapply(formula, ffit, data = df[!df$sporttogether == 1,])
lapply(ans_nfitness, summary)

#3. solution for endurance sports partners:
ans_endurance <- lapply(formula, ffit, data = df[df$sporttogether %in% c(2,5,7),])
lapply(ans_endurance, summary)

#4. solution for team (ball) sports partners:
ans_team <- lapply(formula, ffit, data = df[df$sporttogether %in% c(3,10,12),])
lapply(ans_team, summary)
```

```{r, eval=FALSE, echo=FALSE}
texreg::htmlreg(ans_fitness,
        file="./results/coeftab_fitness.html",
        caption="Results of random effects models predicting sports partnership maintenance IN FITNESS at t+1 (1=yes, 0=no)", caption.above = TRUE,
        custom.model.names = c("M1: main predictors", "M2: sports beh. dyad", "M3: dyadic covars", "M4: multiplexity", "M5: replacement candidates"),
       custom.coef.names = c("(Intercept)", 
                             "Ego skill", "Ego-alter skill diference", 
                             "Emotional closeness", "Informal group", "Commercial gym", "Unorganized", "Missing", 
                             "Period: waves 2-3",  "Mean sports frequency dyad", "Same municipality", "Roommate", "Communication frequency", "Years known",
                             "Woman-man", "Woman-woman", "Friendship", "Confidant", "Study partner", "No. of replacement candidates"),
       digits=2, single.row = TRUE
        )

texreg::htmlreg(ans_nfitness,
        file="./results/coeftab_nfitness.html",
        caption="Results of random effects models predicting sports partnership maintenance OUTSIDE FITNESS at t+1 (1=yes, 0=no)", caption.above = TRUE,
        custom.model.names = c("M1: main predictors", "M2: sports beh. dyad", "M3: dyadic covars", "M4: multiplexity", "M5: replacement candidates"),
              custom.coef.names = c("(Intercept)", 
                             "Ego skill", "Ego-alter skill diference", 
                             "Emotional closeness", "Informal group", "Commercial gym", "Unorganized", "Missing",
                             "Period: waves 2-3",  "Mean sports frequency dyad", "Same municipality", "Roommate", "Communication frequency", "Years known",
                             "Woman-man", "Woman-woman", "Friendship", "Confidant", "Study partner", "No. of replacement candidates"),
       digits=2, single.row = TRUE
        )
```

<br>

#### sports partners in fitness

```{r, echo = FALSE}
htmltools::tags$div(
  style = "height: 600px; overflow-y: scroll;",
  htmltools::includeHTML("./results/coeftab_fitness.html")
)
```

<br>

#### sports partners outside fitness

```{r, echo = FALSE}
htmltools::tags$div(
  style = "height: 600px; overflow-y: scroll;",
  htmltools::includeHTML("./results/coeftab_nfitness.html")
)
```

<!--- 


### {.unlisted .unnumbered}

<br>

### skill level difference score

We use an alternative measure of dyadic skill level differences: We include the absolute difference between ego and alter, and we control for ego's skill level.

```{r, eval=FALSE}
formula2 <- list(
  #model 1: main predictors
  y ~ 1 + (1 | ego) + scale(ego_grade) + scale(dif_skill) + scale(closeness.t) + ego_context + as.factor(period),
  
  #model 2: control for sports behavior dyad
  y ~ 1 + (1 | ego) +  scale(ego_grade)  + scale(dif_skill)  + scale(closeness.t) + ego_context + as.factor(period) + ego_quit + scale(mean_freq),
  
  #model 3: include "traditional" dyadic controls
  y ~ 1 + (1 | ego) +  scale(ego_grade)  + scale(dif_skill)   + scale(closeness.t) + ego_context + as.factor(period) + ego_quit + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender,
  
  #model 4: add other relational dimensions (multiplexity)
  y ~ 1 + (1 | ego) +  scale(ego_grade)  + scale(dif_skill)   + scale(closeness.t) + ego_context + as.factor(period) + ego_quit + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study,
  
  #model 5: add replacement candidates
  y ~ 1 + (1 | ego) +  scale(ego_grade)  + scale(dif_skill)   + scale(closeness.t) + ego_context + as.factor(period) + ego_quit + scale(mean_freq) + proximity + scale(frequency.t) + scale(duration) + gender + bff + cdn + study + scale(nreplace)
  )

#estimate
ans_skilldif <- lapply(formula2, ffit, data = df)
lapply(ans_skilldif, summary)
```

```{r, eval=FALSE, echo=FALSE}
texreg::htmlreg(ans_skilldif,
        file="./results/coeftab_skilldif.html",
        caption="Results of random effects models predicting sports partnership maintenance at t+1 (1=yes, 0=no); with alternative operationalization for dyadic skills gap", caption.above = TRUE,
        custom.model.names = c("M1: main predictors", "M2: sports beh. dyad", "M3: dyadic covars", "M4: multiplexity", "M5: replacement candidates"),
       custom.coef.names = c("(Intercept)", 
                             "Ego skills", "Absolute dyadic skill difference", 
                             "Emotional closeness", "Informal group", "Commercial gym", "Unorganized", "Missing", 
                             "Period: waves 2-3", "Ego quit at t+1", "Mean sports frequency dyad", "Same municipality", "Roommate", "Communication frequency", "Years known",
                             "Woman-man", "Woman-woman", "Friendship", "Confidant", "Study partner", "No. of replacement candidates"),
       digits=2, single.row = TRUE
        )
```

```{r, echo = FALSE}
htmltools::tags$div(
  style = "height: 600px; overflow-y: scroll;",
  htmltools::includeHTML("./results/coeftab_skilldif.html")
)
```


-->

---


<br>

## AMEs

For more information on the (numerical) approach to computing AMEs, see https://www.jochemtolsma.nl/tutorials/me/.

#### define data-sets

```{r, eval=FALSE}
#2. tie maintenance
dfclose1 <- dfclose0 <- df
dfroommate1 <- dfroommate0 <- df

dfclose1$proximity <- "close"
dfclose0$proximity <- "far"
dfroommate1$proximity <- "roommate"
dfroommate0$proximity <- "far"

s <- 0.001
dfclosenessplus <- dfclosenessmin <- df
dfclosenessplus$closeness.t <- df$closeness.t + s
dfclosenessmin$closeness.t <- df$closeness.t - s

dffriend1 <- dffriend0 <- df
dfstudy1 <- dfstudy0 <- df
dfcdn1 <- dfcdn0 <- df
dffriend1$bff <- 1
dffriend0$bff <- 0
dfstudy1$study <- 1
dfstudy0$study <- 0
dfcdn1$cdn <- 1
dfcdn0$cdn <- 0

dfwomen1 <- dfwomen0 <- dfmixed1 <- dfmixed0 <- df
dfwomen1$gender <- "FF"
dfwomen0$gender <- "MM"
dfmixed1$gender <- "FM"
dfmixed0$gender <- "MM"

dfdurationplus <- dfdurationmin <- df
dfdurationplus$duration <- df$duration + s
dfdurationmin$duration <- df$duration - s

dffrequencyplus <- dffrequencymin <- df
dffrequencyplus$frequency.t <- df$frequency.t + s
dffrequencymin$frequency.t <- df$frequency.t - s

dfperiod21 <- dfperiod20 <- df
dfperiod21$period <- 2
dfperiod20$period <- 1

dfmeanfreqplus <- dfmeanfreqmin <- df
dfmeanfreqplus$mean_freq <- df$mean_freq + s
dfmeanfreqmin$mean_freq <- df$mean_freq - s

#dfquit1 <- dfquit0 <- df
#dfquit1$ego_quit <- 1
#dfquit0$ego_quit <- 0

#dfHH1 <- dfHH0 <- dfHM1 <- dfHM0 <- dfHL1 <- dfHL0 <- dfML1 <- dfML0 <- dfLL1 <- dfLL0 <- df
#dfHH1$skills <- "HH"
#dfHH0$skills <- "MM"
#dfHM1$skills <- "HM"
#dfHM0$skills <- "MM"
#dfHL1$skills <- "HL"
#dfHL0$skills <- "MM"
#dfML1$skills <- "ML"
#dfML0$skills <- "MM"
#dfLL1$skills <- "LL"
#dfLL0$skills <- "MM"

dfegoskillplus <- dfegoskillmin <- df
dfegoskillplus$ego_grade <- df$ego_grade + s
dfegoskillmin$ego_grade <- df$ego_grade - s

dfskilldifplus <- dfskilldifmin <- df
dfskilldifplus$dif_skill  <- df$dif_skill + s
dfskilldifmin$dif_skill  <- df$dif_skill - s

dfinformal1 <- dfinformal0 <- dfgym1 <- dfgym0 <- dfalone1 <- dfalone0 <- dfmissing1 <- dfmissing0 <- df
dfinformal1$ego_context <- "informal"
dfinformal0$ego_context <- "club"
dfgym1$ego_context <- "gym"
dfgym0$ego_context <- "club"
dfalone1$ego_context <- "alone"
dfalone0$ego_context <- "club"
dfmissing1$ego_context <- "missing"
dfmissing0$ego_context <- "club"

dfreplaceplus <- dfreplacemin <- df
dfreplaceplus$nreplace <- df$nreplace + s
dfreplacemin$nreplace <- df$nreplace - s
```

<br>

#### function to calculate AMEs

```{r, eval=FALSE}
fpred <- function(x) {
  me_close <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclose1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclose0)
  ame_close <- mean(me_close)
  
  me_roommate <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfroommate1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfroommate0)
  ame_roommate <- mean(me_roommate)
  
  me_closeness <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclosenessplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfclosenessmin))/(2 * s)
  ame_closeness <- mean(me_closeness)
  
  me_friend <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffriend1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffriend0)
  ame_friend <- mean(me_friend)
  
  me_study <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfstudy1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfstudy0)
  ame_study <- mean(me_study)
  
  me_cdn <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfcdn1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfcdn0)
  ame_cdn <- mean(me_cdn)
  
  me_women <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfwomen1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfwomen0)
  ame_women <- mean(me_women)
  
  me_mixed <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmixed1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmixed0)
  ame_mixed <- mean(me_mixed)
  
  me_duration <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfdurationplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfdurationmin))/(2 * s)
  ame_duration <- mean(me_duration)
  
  me_frequency <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffrequencyplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dffrequencymin))/(2 * s)
  ame_frequency <- mean(me_frequency)
  
  me_period2 <-  lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfperiod21) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfperiod20)
  ame_period2 <- mean(me_period2)
  
#  me_quit <-  lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfquit1) - lme4:::predict.merMod(x, #type = "response", re.form = NULL, newdata = dfquit0)
#  ame_quit <- mean(me_quit)
  
#  me_HH <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfHH1) - lme4:::predict.merMod(x, type = #"response", re.form = NULL, newdata = dfHH0)
#  ame_HH <- mean(me_HH)
#  me_HM <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfHM1) - lme4:::predict.merMod(x, type = #"response", re.form = NULL, newdata = dfHM0)
#  ame_HM <- mean(me_HM)
#  me_HL <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfHL1) - lme4:::predict.merMod(x, type = #"response", re.form = NULL, newdata = dfHL0)
#  ame_HL <- mean(me_HL)
#  me_ML <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfML1) - lme4:::predict.merMod(x, type = #"response", re.form = NULL, newdata = dfML0)
#  ame_ML <- mean(me_ML)
#  me_LL <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfLL1) - lme4:::predict.merMod(x, type = #"response", re.form = NULL, newdata = dfLL0)
#  ame_LL <- mean(me_LL)
 
  me_egoskill <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfegoskillplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfegoskillmin))/(2 * s)
  ame_egoskill <- mean(me_egoskill)
  
  me_skilldif <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfskilldifplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfskilldifmin))/(2 * s)
  ame_skilldif <- mean(me_skilldif)
  
   
  me_informal <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfinformal1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfinformal0)
  ame_informal <- mean(me_informal)
  
  me_gym <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfgym1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfgym0)
  ame_gym <- mean(me_gym)
  
  me_alone <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfalone1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfalone0)
  ame_alone <- mean(me_alone)
  
  me_missing <- lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmissing1) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmissing0)
  ame_missing <- mean(me_missing)
  
  me_replace <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfreplaceplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfreplacemin))/(2 * s)
  ame_replace <- mean(me_replace)

  me_meanfreq <- (lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmeanfreqplus) - lme4:::predict.merMod(x, type = "response", re.form = NULL, newdata = dfmeanfreqmin))/(2 * s)
  ame_meanfreq <- mean(me_meanfreq)

  c(ame_close, ame_roommate, ame_closeness, ame_friend, ame_study, ame_cdn, ame_women, ame_mixed, ame_duration, ame_frequency, ame_period2, ame_egoskill, ame_skilldif, ame_informal, ame_gym, ame_alone, ame_missing, ame_replace, ame_meanfreq)
}

#fpred(ans[[5]])
```

<br>

#### bootstrapping

```{r, eval=FALSE}
seed <- 242523
nIter <- 500
nCore <- detectCores() - 1
mycl <- makeCluster(rep("localhost", nCore))
clusterEvalQ(mycl, library(lme4))
clusterExport(mycl, varlist=c("ans","s","df", "dfclose1", "dfclose0", "dfroommate1", "dfroommate0", "dfclosenessplus", "dfclosenessmin", "dffriend1", "dffriend0", "dfstudy1", "dfstudy0", "dfcdn1", "dfcdn0", "dfwomen1", "dfwomen0", "dfmixed1", "dfmixed0", "dfdurationplus", "dfdurationmin", "dffrequencyplus", "dffrequencymin", "dfperiod21", "dfperiod20", "dfmeanfreqplus", "dfmeanfreqmin", "dfegoskillmin", "dfegoskillplus", "dfskilldifmin", "dfskilldifplus",  "dfinformal1", "dfinformal0", "dfgym1", "dfgym0", "dfalone1", "dfalone0", "dfmissing1", "dfmissing0", "dfreplaceplus", "dfreplacemin") )
        
system.time (boo_m <- bootMer(ans[[5]], fpred, nsim = nIter, parallel = "snow", ncpus = nCore, cl = mycl, seed = seed) )

stopCluster(mycl)

save(boo_m, file = "./results/bootm.Rda")
```

<br>

#### plot

```{r, fig.height=7}
load("./results/bootm.Rda")

#tie formation
#plotdata1 <- data.frame(
#  pred = c("Outside municipality", "Same municipality", "Same house", "*Emotional closeness*", "Friendship", "Study partnership", "Confidant", "Male dyad", "Female dyad", "Mixed dyad", #"*Years known*", "*Communication frequency*", "Ego residential change", "Ego study transition", "Period 1", "Period 2"),
#  Outcome = "Tie formation",
#  ame = c(0, booL[[1]]$t0[1:6], 0, booL[[1]]$t0[7:12], 0, booL[[1]]$t0[13]),
#  ame_se = c(0, apply(booL[[1]]$t, 2, sd)[1:6], 0, apply(booL[[1]]$t, 2, sd)[7:12], 0, apply(booL[[1]]$t, 2, sd)[13]),
#  ref = c(1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0))

#tie maintenance

plotdata <- data.frame(
  pred = c("Outside municipality", "Same municipality", "Same house", 
           "*Emotional closeness*", "Friendship", "Study partnership", "Confidant", "Male dyad", "Female dyad", "Mixed dyad", "*Years known*", "*Communication frequency*", "Period 1", "Period 2", "*Ego skill*", "*Ego-alter skill difference*", "Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing", "*No. of replacement candidates*", "*Sports frequency dyad*" ),
  #Outcome = "Tie maintenance",
  ame = c(0, boo_m$t0[1:6], 0, boo_m$t0[7:10], 0, boo_m$t0[11:13], 0,  boo_m$t0[14:19] ),
  
  ame_se = c(0, apply(boo_m$t, 2, sd)[1:6], 0, apply(boo_m$t, 2, sd)[7:10], 0, apply(boo_m$t, 2, sd)[11:13], 0, apply(boo_m$t, 2, sd)[14:19]),
  
  ref = c(1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0))

plotdata$pred <- factor(plotdata$pred, levels = rev(c(
  "*Ego-alter skill difference*",  "*Emotional closeness*", "Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing", "Outside municipality", "Same municipality", "Same house", "*Communication frequency*","*Years known*", "Male dyad", "Female dyad", "Mixed dyad", "Friendship", "Confidant", "Study partnership", "*Ego skill*", "*Sports frequency dyad*", "*No. of replacement candidates*", "Period 1", "Period 2")))
  
plotdata <- plotdata[order(plotdata$pred),]
row.names(plotdata) <- 1:nrow(plotdata)
plotdata$ref <- as.factor(plotdata$ref)

#also include coefficients as labels, but leave out the labels for the reference level
plotdata$label <- ifelse(plotdata$ref == 1, 0, 1)

#in main text, only main predictors..
plotdata2 <- plotdata[plotdata$pred %in% c( "*Ego-alter skill difference*",  "*Emotional closeness*","Sports club", "Informal group", "Commercial gym", "Unorganized", "Missing"),]

p <- ggplot(plotdata2, aes(x = ame, y = pred, #color = Outcome, 
                           shape = ref)) +
  geom_vline(xintercept = 0, color = "grey") +
  geom_point() +
  geom_errorbar(data = subset(plotdata2, label == 1), aes(xmin = ame - 1.96*ame_se, xmax = ame + 1.96*ame_se), width = 0, linewidth = 0.3) +
  geom_text(data = subset(plotdata2, label == 1), aes(label = sprintf("%0.2f (%0.2f)", ame, ame_se )), size = 3, color = "black", position = position_nudge(y = 0.4)) +
  #facet_grid(Outcome ~., switch = "y", scales = "free_y", space = "free_y") +
  labs(x = "AME", y = NULL) +
  scale_x_continuous(labels = scales::percent) +
  scale_shape_manual("", values = c("1" = 1, "0" = 16)) +
  theme(axis.line = element_line(), 
        legend.position = "none", 
        strip.background = element_blank(),
        strip.text.x = element_text(face = "bold"),
        strip.text.y = element_blank(),
        axis.text.y = element_markdown()) + guides(shape = "none")

ggsave("./figures/ames.png", height = 2.5)

plotdata %>% 
  arrange(desc(row_number())) %>%
  select(-label) %>%
  fshowdf(digits = 3, caption = "Average marginal effects on sports partnership maintenance")

#now with all controls:
p2 <- ggplot(plotdata, aes(x = ame, y = pred, #color = Outcome, 
                           shape = ref)) +
  geom_vline(xintercept = 0, color = "grey") +
  geom_point() +
  geom_errorbar(data = subset(plotdata, label == 1), aes(xmin = ame - 1.96*ame_se, xmax = ame + 1.96*ame_se), width = 0, linewidth = 0.3) +
  geom_text(data = subset(plotdata, label == 1), aes(label = sprintf("%0.2f (%0.2f)", ame, ame_se )), size = 2, color = "black", position = position_nudge(y = 0.4)) +
  #facet_grid(Outcome ~., switch = "y", scales = "free_y", space = "free_y") +
  labs(x = "AME", y = NULL) +
  scale_x_continuous(labels = scales::percent) +
  scale_shape_manual("", values = c("1" = 1, "0" = 16)) +
  #ftheme() +
  theme(axis.line = element_line(), 
        legend.position = "none", 
        strip.background = element_blank(),
        strip.text.x = element_text(face = "bold"),
        strip.text.y = element_blank(),
        axis.text.y = element_markdown()) + guides(shape = "none")

ggsave("./figures/ames_incl_control.png", height = 5)
#knitr::include_graphics("./figures/ames_incl_control.png")

print(p2) 
``` 