Additional analyses

• Here you can find the code and the results of all additional analyses.
• To see a good overview of the results, I recommend you directly go down to the Figures section.
• But if you’re interested in how the analyses were executed, see below.
• To see the code, click on button “Code”. Alternatively, you can download the rmd file from the github repo.

Set-up

Load packages.

# install packages
# devtools::install_github("https://github.com/tdienlin/td@v.0.0.2.5")

# define packages
packages <- c("broom.mixed", "brms", "devtools", "GGally", "ggplot2", 
              "gridExtra", "kableExtra", "knitr", "lavaan", "lme4", 
              "magrittr", "mice", "mvnormalTest", 
              "PerFit", "psych", "quanteda.textstats", "semTools", "tidyverse")

# load packages
lapply(c(packages, "td"), library, character.only = TRUE)

# load workspace
load("data/workspace_2.RData")

With additional covariates

These models include additional covariates that weren’t preregistered and weren’t included for theoretical reasons. These are:

• Trust in ORF
• Trust in police
• Trust in parliament
• Trust in health sector
• Trust in government
• Trust in army

Life satisfaction

model_life_sat_lmer_add <- "
  life_sat ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    corona_pos_b + corona_pos_w +
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b +
    trst_media_w + trst_police_w + trst_media_w + trst_hlthsec_w + trst_gov_w + trst_army_w + 
    trst_media_b + trst_police_b + trst_media_b + trst_hlthsec_b + trst_gov_b + trst_army_b
"

fit_life_sat_lmer_add <- with(d_long_100_mim_mice, 
                              exp = lmerTest::lmer(model_life_sat_lmer_add))
fit_life_sat_lmer_add <- summary(pool(fit_life_sat_lmer_add), conf.int = TRUE)
print_res(fit_life_sat_lmer_add)

Positive Affect

model_aff_pos_lmer_add <- "
  aff_pos_m ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    corona_pos_b + corona_pos_w +
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b +
    trst_media_w + trst_police_w + trst_media_w + trst_hlthsec_w + trst_gov_w + trst_army_w + 
    trst_media_b + trst_police_b + trst_media_b + trst_hlthsec_b + trst_gov_b + trst_army_b
"

fit_aff_pos_lmer_add <- with(d_long_100_mim_mice, 
                              exp = lmerTest::lmer(model_aff_pos_lmer_add))
fit_aff_pos_lmer_add <- summary(pool(fit_aff_pos_lmer_add), conf.int = TRUE)
print_res(fit_aff_pos_lmer_add)

Negative Affect

model_aff_neg_lmer_add <- "
  aff_neg_m ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    corona_pos_b + corona_pos_w +
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b +
    trst_media_w + trst_police_w + trst_media_w + trst_hlthsec_w + trst_gov_w + trst_army_w + 
    trst_media_b + trst_police_b + trst_media_b + trst_hlthsec_b + trst_gov_b + trst_army_b
"

fit_aff_neg_lmer_add <- with(d_long_100_mim_mice, 
                              exp = lmerTest::lmer(model_aff_neg_lmer_add))
fit_aff_neg_lmer_add <- summary(pool(fit_aff_neg_lmer_add), conf.int = TRUE)
print_res(fit_aff_neg_lmer_add)

Without control variables

Let’s inspect how results change when control variables are omitted.

Life satisfaction

model_life_sat_lmer_nco <- "
  life_sat ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b
"
fit_life_sat_lmer_nco <- with(d_long_100_mim_mice, exp = lmerTest::lmer(model_life_sat_lmer_nco))
fit_life_sat_lmer_nco <- summary(pool(fit_life_sat_lmer_nco), conf.int = TRUE)
print_res(fit_life_sat_lmer_nco)

Positive Affect

model_aff_pos_lmer_nco <- "
  aff_pos_m ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b
"
fit_aff_pos_lmer_nco <- with(d_long_100_mim_mice, exp = lmerTest::lmer(model_aff_pos_lmer_nco))
fit_aff_pos_lmer_nco <- summary(pool(fit_aff_pos_lmer_nco), conf.int = TRUE)
print_res(fit_aff_pos_lmer_nco)

Negative Affect

model_aff_neg_lmer_nco <- "
  aff_neg_m ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b
"
fit_aff_neg_lmer_nco <- with(d_long_100_mim_mice, exp = lmerTest::lmer(model_aff_neg_lmer_nco))
fit_aff_neg_lmer_nco <- summary(pool(fit_aff_neg_lmer_nco), conf.int = TRUE)
print_res(fit_aff_neg_lmer_nco)

Preregistered

Some changes were introduced because of feedback from colleagues (multiple imputation, imputation of all participants, inclusion of all variables). In what follows, please find the results as originally planned (but with some necessary deviations; for example not all preregistered variables could be included).

• Use data-set in which participants with >50% data were removed.
• Also use factor scores (possible without multiple imputation).

Note that because here no multiple imputation was used, output looks differently.

Life satisfaction

model_life_sat_lmer_pre <- "
  life_sat ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b
    "
fit_life_sat_lmer_pre <- lmerTest::lmer(model_life_sat_lmer_pre, data = d_long_50_imp)
print_res(broom.mixed::tidy(fit_life_sat_lmer_pre, conf.int = T), imputation = FALSE)

Positive Affect

model_aff_pos_lmer_pre <- "
  aff_pos_fs ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b
    "
fit_aff_pos_lmer_pre <- lmerTest::lmer(model_aff_pos_lmer_pre, data = d_long_50_imp)
print_res(broom.mixed::tidy(fit_aff_pos_lmer_pre, conf.int = T), imputation = FALSE)

Negative Affect

model_aff_neg_lmer_pre <- "
  aff_neg_fs ~ 
    (1 | id) + (1 | wave) + 
    soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
    soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
    soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
    soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
    age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
    res_vienna + acc_bal + acc_gar + home_sqm + 
    work_h_b + work_h_w +
    work_homeoff_b +  work_homeoff_w +
    hh_income_b + hh_income_w +
    hh_adults + hh_child18 + hh_child17 + hh_child14 + hh_child5 + hh_child2 + 
    hh_oldfam + hh_outfam + hh_partner +
    home_owner +
    med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
    med_vid_orf_b + med_vid_pri_b + 
    med_txt_kro_w + med_txt_sta_w + med_txt_pre_w + med_txt_oes_w + med_txt_kur_w + med_txt_slz_w + med_txt_son_w + 
    med_vid_orf_w + med_vid_pri_w + 
    risk_prop_b + risk_prop_w + 
    act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
    act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
    sat_dem_w + sat_dem_b + 
    health_w + health_b +
    loc_cntrl_int_m_w + loc_cntrl_int_m_b
    "
fit_aff_neg_lmer_pre <- lmerTest::lmer(model_aff_neg_lmer_pre, data = d_long_50_imp)
print_res(broom.mixed::tidy(fit_aff_neg_lmer_pre, conf.int = T), imputation = FALSE)

Preregistered without imputation

Same as before, but now don’t use any imputed data whatsoever. Was preregistered as additional analysis to provide comparison.

Life satisfaction

model_life_sat_lmer_noi <- "
  life_sat ~
  (1 | id) + (1 | wave) + 
  soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
  soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
  soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
  soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
  age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
  res_vienna + acc_bal + acc_gar + home_sqm + corona_pos +
  med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + med_vid_orf_b + med_vid_pri_b + 
  risk_prop_b + loc_cntrl_int_m_b + 
  act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
  act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
  sat_dem_w + sat_dem_b + 
  health_w + health_b
"
fit_life_sat_lmer_noi <- lmerTest::lmer(model_life_sat_lmer_noi, data = d_long_50)
print_res(broom.mixed::tidy(fit_life_sat_lmer_noi, conf.int = T), imputation = FALSE)

Positive Affect

model_aff_pos_lmer_noi <- "
  aff_pos_fs ~ 
  (1 | id) + (1 | wave) + 
  soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
  soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
  soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
  soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
  age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
  res_vienna + acc_bal + acc_gar + home_sqm + corona_pos +
  med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + med_vid_orf_b + med_vid_pri_b + 
  risk_prop_b + loc_cntrl_int_m_b + 
  act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
  act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
  sat_dem_w + sat_dem_b + 
  health_w + health_b
"
fit_aff_pos_lmer_noi <- lmerTest::lmer(model_aff_pos_lmer_noi, data = d_long_50)
print_res(broom.mixed::tidy(fit_aff_pos_lmer_noi, conf.int = T), imputation = FALSE)

Negative Affect

model_aff_neg_lmer_noi <- "
  aff_neg_fs ~ 
  (1 | id) + (1 | wave) + 
  soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
  soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
  soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
  soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
  age + male + born_aus + born_aus_prnts + edu_fac + employment_fac +
  res_vienna + acc_bal + acc_gar + home_sqm + corona_pos +
  med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + med_vid_orf_b + med_vid_pri_b + 
  risk_prop_b + loc_cntrl_int_m_b + 
  act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
  act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
  sat_dem_w + sat_dem_b + 
  health_w + health_b
"
fit_aff_neg_lmer_noi <- lmerTest::lmer(model_aff_neg_lmer_noi, data = d_long_50)
print_res(broom.mixed::tidy(fit_aff_neg_lmer_noi, conf.int = T), imputation = FALSE)

Figures

• In what follows, see figure with all results combined.
• Results show that different models are comparable.
• The model without control variables stands out a bit, showing that controlling for additional varying variables is important.
• The preregistered model without imputation also has larger confidence intervals, which was to be expected given fewer data.

# get data
dat_fig_results_activity_pub <- get_dat_res(
  fit_aff_neg_lmer_pub, fit_aff_pos_lmer_pub, fit_life_sat_lmer_pub, 
  type = "Activity", analysis = "1. Published", variance = "within")

dat_fig_results_channel_pub <- get_dat_res(
  fit_aff_neg_lmer_pub, fit_aff_pos_lmer_pub, fit_life_sat_lmer_pub, 
  type = "Channels", analysis = "1. Published", variance = "within")

dat_fig_results_activity_add <- get_dat_res(
  fit_aff_neg_lmer_add, fit_aff_pos_lmer_add, fit_life_sat_lmer_add, 
  type = "Activity", analysis = "2. Additional covars", variance = "within")

dat_fig_results_channel_add <- get_dat_res(
  fit_aff_neg_lmer_add, fit_aff_pos_lmer_add, fit_life_sat_lmer_add, 
  type = "Channels", analysis = "2. Additional covars", variance = "within")

dat_fig_results_activity_nco <- get_dat_res(
  fit_aff_neg_lmer_nco, fit_aff_pos_lmer_nco, fit_life_sat_lmer_nco, 
  type = "Activity", analysis = "3. No covars", variance = "within")

dat_fig_results_channel_nco <- get_dat_res(
  fit_aff_neg_lmer_nco, fit_aff_pos_lmer_nco, fit_life_sat_lmer_nco, 
  type = "Channels", analysis = "3. No covars", variance = "within")

dat_fig_results_activity_pre <- get_dat_res(
  fit_aff_neg_lmer_pre, fit_aff_pos_lmer_pre, fit_life_sat_lmer_pre, 
  type = "Activity", analysis = "4. Preregistered", variance = "within")

dat_fig_results_channel_pre <- get_dat_res(
  fit_aff_neg_lmer_pre, fit_aff_pos_lmer_pre, fit_life_sat_lmer_pre, 
  type = "Channels", analysis = "4. Preregistered", variance = "within")

dat_fig_results_activity_noi <- get_dat_res(
  fit_aff_neg_lmer_noi, fit_aff_pos_lmer_noi, fit_life_sat_lmer_noi, 
  type = "Activity", analysis = "5. No imputation", variance = "within")

dat_fig_results_channel_noi <- get_dat_res(
  fit_aff_neg_lmer_noi, fit_aff_pos_lmer_noi, fit_life_sat_lmer_noi, 
  type = "Channels", analysis = "5. No imputation", variance = "within")

dat_fig_results_comp <- rbind(
  dat_fig_results_activity_pub, 
  dat_fig_results_channel_pub,
  dat_fig_results_activity_nco, 
  dat_fig_results_channel_nco,
  dat_fig_results_activity_add, 
  dat_fig_results_channel_add,
  dat_fig_results_activity_pre, 
  dat_fig_results_channel_pre,
  dat_fig_results_activity_noi, 
  dat_fig_results_channel_noi
)

# make figure
fig_results_comp <- make_graph_res(
  dat_fig_results_comp,
  sesoi = "est",
  facet = "type"
  )
fig_results_comp

# safe figure
ggsave("figures/fig_results_comparison.pdf", 
       width = 7, height = 5,
       plot = fig_results_within_comp)

save.image("data/workspace_2.RData")