In this script I extract the coefficients for gender, age and education and their interaction variables and put them in a dataset together, to use them as input for the meta-regression.
rm(list=ls())
library(tidyverse)
library(dplyr)
library(gamlss)load("./data/final_data/regression_outputs/ess_list_interactions_w_new.RData")
# Store the results in a new dataframe
ess_inter_preds_df <- data.frame(dep_var = character())
# Loop over the list of models
for (i in seq_along(ess_list_interactions)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(ess_list_interactions[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(ess_list_interactions[[i]])
# Extract the intercept and standard deviation for mu and sigma
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results
ess_inter_preds_df <- rbind(ess_inter_preds_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(ess_inter_preds_df, file="./data/final_data/indep_var/ess_inter_var_new.RData")# Now the same for the EVS
rm(list=ls())
load("./data/final_data/regression_outputs/evs_list_interactions_w_new.RData")
# Store the results in a new dataframe
evs_indep_interactions_df <- data.frame(dep_var = character())
# Loop over the list of models
for (i in seq_along(evs_list_interactions)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(evs_list_interactions[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(evs_list_interactions[[i]])
# Extract the interaction effects for sex, education and age
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results
evs_indep_interactions_df <- rbind(evs_indep_interactions_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(evs_indep_interactions_df, file="./data/final_data/indep_var/evs_inter_var_new.RData")# Now for I&O research
rm(list=ls())
load("./data/final_data/regression_outputs/io_list_interactions_w_new.RData")
# Store the results in a new dataframe
io_indep_interactions_df <- data.frame(dep_var = character())
# Loop over the list of models
for (i in seq_along(io_list_interactions)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(io_list_interactions[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(io_list_interactions[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results
io_indep_interactions_df <- rbind(io_indep_interactions_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(io_indep_interactions_df, file="./data/final_data/indep_var/io_inter_var_new.RData")# Now for ISSP, which has to happen twice (bc 2 subsets based on dependent vars)
rm(list=ls())
load("./data/final_data/regression_outputs/issp_list_interactions_w_new.RData")
# Store the results in a new dataframe
issp_indep_interactions_df <- data.frame(dep_var = character())
# Loop over the list of models
for (i in seq_along(issp_list_interactions)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(issp_list_interactions[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(issp_list_interactions[[i]])
# Extract the intercept and standard deviatisspn for mu and sigma
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results
issp_indep_interactions_df <- rbind(issp_indep_interactions_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(issp_indep_interactions_df, file="./data/final_data/indep_var/issp_inter_var_new.RData")
# For the other indep vars as well
rm(list=ls())
load("./data/final_data/regression_outputs/issp_list_interactions_2_waves_w_new.RData")
# Store the results in a new dataframe
issp_2_indep_interactions_df <- data.frame(dep_var = character())
# Loop over the list of models
for (i in seq_along(issp_list_interactions_2_waves)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(issp_list_interactions_2_waves[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(issp_list_interactions_2_waves[[i]])
# Extract the intercept and standard deviation for mu and sigma
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results
issp_2_indep_interactions_df <- rbind(issp_2_indep_interactions_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(issp_2_indep_interactions_df, file="./data/final_data/indep_var/issp_2_inter_var_new.RData")# Unfortunately, the loop I used in the other scripts does not seem to work for the interaction effects, so I need to do them one by one
rm(list=ls())
load("./data/final_data/regression_outputs/eb_list_interactions_1986_w_new.RData")
# Store the results in a new dataframe
eb_interactions_1986_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_1986)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_1986[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_1986[[i]])
# Extract info
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_interactions_1986_df <- rbind(eb_interactions_1986_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
save(eb_interactions_1986_df, file="./data/final_data/indep_var/eb_1986_inter_var_new.RData")# Extract the indep vars from the regressions Eurobarometer one by one
rm(list=ls())
load("./data/final_data/regression_outputs/eb_list_interactions_2007_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_2008_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_2009_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_2011_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_buyprod_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_cchange2_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_doprot_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_eff_daily_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_pers_imp_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_prsaction_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_quallife_w_new.RData")
load("./data/final_data/regression_outputs/eb_list_interactions_doprot_city_w_new.RData")
# Store the results in a new dataframe
eb_indep_interactions_2007_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_2007)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_2007[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_2007[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_2007_df <- rbind(eb_indep_interactions_2007_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# 2008
# Store the results in a new dataframe
eb_indep_interactions_2008_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_2008)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_2008[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_2008[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_2008_df <- rbind(eb_indep_interactions_2008_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_2009_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_2009)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_2009[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_2009[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_2009_df <- rbind(eb_indep_interactions_2009_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_2011_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_2011)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_2011[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_2011[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_2011_df <- rbind(eb_indep_interactions_2011_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_buyprod_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_buyprod)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_buyprod[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_buyprod[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_buyprod_df <- rbind(eb_indep_interactions_buyprod_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_cchange2_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_cchange2)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_cchange2[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_cchange2[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_cchange2_df <- rbind(eb_indep_interactions_cchange2_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_doprot_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_doprot)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_doprot[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_doprot[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_doprot_df <- rbind(eb_indep_interactions_doprot_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_doprot_city_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_doprot_city)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_doprot_city[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_doprot_city[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_doprot_city_df <- rbind(eb_indep_interactions_doprot_city_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_eff_daily_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_eff_daily)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_eff_daily[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_eff_daily[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_eff_daily_df <- rbind(eb_indep_interactions_eff_daily_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_pers_imp_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_pers_imp)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_pers_imp[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_pers_imp[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_pers_imp_df <- rbind(eb_indep_interactions_pers_imp_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_prsaction_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_prsaction)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_prsaction[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_prsaction[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_prsaction_df <- rbind(eb_indep_interactions_prsaction_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}# Store the results in a new dataframe
eb_indep_interactions_quallife_df <- data.frame(dep_var = character())
# Loop over the list
for (i in seq_along(eb_list_interactions_quallife)) {
# Extract the dependent variable so that we know for which dep var the effects are
dep_var <- as.character(eb_list_interactions_quallife[[i]]$mu.terms[[2]])
# Summarize the model
sum <- summary(eb_list_interactions_quallife[[i]])
mu_sex_est <- sum[[3]]
mu_sex_sd <- sum[[3,2]]
mu_isced_med_est <- sum[[4]]
mu_isced_med_sd <- sum[[4,2]]
mu_isced_high_est <- sum[[5]]
mu_isced_high_sd <- sum[[5,2]]
mu_age_est <- sum[[6]]
mu_age_sd <- sum[[6,2]]
mu_sex_est_inter <- sum[[8]]
mu_sex_sd_inter <- sum[[8,2]]
mu_isced_med_est_inter <- sum[[9]]
mu_isced_med_sd_inter <- sum[[9,2]]
mu_isced_high_est_inter <- sum[[10]]
mu_isced_high_sd_inter <- sum[[10,2]]
mu_age_est_inter <- sum[[7]]
mu_age_sd_inter <- sum[[7,2]]
# Variance
sig_sex_est <- sum[[9]]
sig_sex_sd <- sum[[9,2]]
sig_isced_med_est <- sum[[10]]
sig_isced_med_sd <- sum[[10,2]]
sig_isced_high_est <- sum[[11]]
sig_isced_high_sd <- sum[[11,2]]
sig_age_est <- sum[[12]]
sig_age_sd <- sum[[12,2]]
sig_sex_est_inter <- sum[[18]]
sig_sex_sd_inter <- sum[[18,2]]
sig_isced_med_est_inter <- sum[[19]]
sig_isced_med_sd_inter <- sum[[19,2]]
sig_isced_high_est_inter <- sum[[20]]
sig_isced_high_sd_inter <- sum[[20,2]]
sig_age_est_inter <- sum[[17]]
sig_age_sd_inter <- sum[[17,2]]
# Put the results in a dataframe
eb_indep_interactions_quallife_df <- rbind(eb_indep_interactions_quallife_df, data.frame(dep_var = dep_var, mu_sex_est, mu_sex_sd, mu_isced_med_est, mu_isced_med_sd, mu_isced_high_est, mu_isced_high_sd, mu_age_est, mu_age_sd, sig_sex_est, sig_sex_sd, sig_isced_med_est, sig_isced_med_sd, sig_isced_high_est, sig_isced_high_sd, sig_age_est, sig_age_sd, mu_sex_est_inter, mu_sex_sd_inter, mu_isced_med_est_inter, mu_isced_med_sd_inter, mu_isced_high_est_inter, mu_isced_high_sd_inter, mu_age_est_inter, mu_age_sd_inter, sig_sex_est_inter, sig_sex_sd_inter, sig_isced_med_est_inter, sig_isced_med_sd_inter, sig_isced_high_est_inter, sig_isced_high_sd_inter, sig_age_est_inter, sig_age_sd_inter))
}
load("./data/final_data/indep_var/eb_1986_inter_var_new.RData")
eb_indep_interactions_df <- rbind(eb_interactions_1986_df, eb_indep_interactions_2007_df, eb_indep_interactions_2008_df, eb_indep_interactions_2009_df, eb_indep_interactions_2011_df, eb_indep_interactions_buyprod_df, eb_indep_interactions_cchange2_df, eb_indep_interactions_doprot_city_df, eb_indep_interactions_doprot_df, eb_indep_interactions_eff_daily_df, eb_indep_interactions_pers_imp_df, eb_indep_interactions_prsaction_df, eb_indep_interactions_quallife_df)
save(eb_indep_interactions_df, file= "./data/final_data/indep_var/eb_inter_var_new.RData" )# The final step is to merge all results into one dataframe
rm(list=ls())
load("./data/final_data/indep_var/eb_inter_var_new.RData")
load("./data/final_data/indep_var/ess_inter_var_new.RData")
load("./data/final_data/indep_var/evs_inter_var_new.RData")
load("./data/final_data/indep_var/io_inter_var_new.RData")
load("./data/final_data/indep_var/issp_2_inter_var_new.RData")
load("./data/final_data/indep_var/issp_inter_var_new.RData")
total_inter_results <- rbind(eb_indep_interactions_df, ess_inter_preds_df, evs_indep_interactions_df, io_indep_interactions_df, issp_indep_interactions_df, issp_2_indep_interactions_df)
save(total_inter_results, file= "./data/meta_analysis/total_inter_results_new.RData" )