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pandemic_panel_supps_v2.R
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pandemic_panel_supps_v2.R
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####################################
### SUPPLEMENTARY PLOTS ###
### version 2.0, with four waves ###
####################################
### Install packages, their dependencies, and specific versions locally to ensure a reproducible package environment
renv::use(
"BH@1.75.0-0",
"Brobdingnag@1.2-6",
"DBI@1.1.2",
"DT@0.20",
"HDInterval@0.2.2",
"MASS@7.3-54",
"Matrix@1.3-4",
"R6@2.5.1",
"RColorBrewer@1.1-2",
"Rcpp@1.0.8",
"RcppEigen@0.3.3.9.1",
"RcppParallel@5.1.5",
"StanHeaders@2.21.0-7",
"abind@1.4-5",
"arrayhelpers@1.1-0",
"askpass@1.1",
"assertthat@0.2.1",
"backports@1.4.1",
"base64enc@0.1-3",
"bayesplot@1.8.1",
"bit64@4.0.5",
"bit@4.0.4",
"blob@1.2.2",
"bridgesampling@1.1-2",
"brms@2.16.3",
"bslib@0.3.1",
"cachem@1.0.6",
"callr@3.7.0",
"cellranger@1.1.0",
"checkmate@2.0.0",
"cli@3.1.1",
"clipr@0.7.1",
"coda@0.19-4",
"codetools@0.2-18",
"colorspace@2.0-2",
"colourpicker@1.1.1",
"commonmark@1.7",
"cpp11@0.4.2",
"crayon@1.4.2",
"crosstalk@1.2.0",
"curl@4.3.2",
"data.table@1.14.2",
"dbplyr@2.1.1",
"desc@1.4.0",
"digest@0.6.29",
"distributional@0.3.0",
"dplyr@1.0.7",
"dtplyr@1.2.1",
"dygraphs@1.1.1.6",
"ellipsis@0.3.2",
"evaluate@0.14",
"fansi@1.0.2",
"farver@2.1.0",
"fastmap@1.1.0",
"fontawesome@0.2.2",
"forcats@0.5.1",
"fs@1.5.2",
"future@1.23.0",
"gargle@1.2.0",
"generics@0.1.2",
"ggdist@3.0.1",
"ggplot2@3.3.5",
"ggridges@0.5.3",
"globals@0.14.0",
"glue@1.6.1",
"googledrive@2.0.0",
"googlesheets4@1.0.0",
"gridExtra@2.3",
"gtable@0.3.0",
"gtools@3.9.2",
"haven@2.4.3",
"highr@0.9",
"hms@1.1.1",
"htmltools@0.5.2",
"htmlwidgets@1.5.4",
"httpuv@1.6.5",
"httr@1.4.2",
"ids@1.0.1",
"igraph@1.2.11",
"inline@0.3.19",
"isoband@0.2.5",
"jquerylib@0.1.4",
"jsonlite@1.7.3",
"knitr@1.37",
"labeling@0.4.2",
"later@1.3.0",
"lattice@0.20-45",
"lazyeval@0.2.2",
"lifecycle@1.0.1",
"listenv@0.8.0",
"loo@2.4.1",
"lubridate@1.8.0",
"magrittr@2.0.2",
"markdown@1.1",
"matrixStats@0.61.0",
"mgcv@1.8-38",
"mime@0.12",
"miniUI@0.1.1.1",
"modelr@0.1.8",
"munsell@0.5.0",
"mvtnorm@1.1-3",
"nleqslv@3.3.2",
"nlme@3.1-153",
"numDeriv@2016.8-1.1",
"openssl@1.4.5",
"packrat@0.7.0",
"parallelly@1.29.0",
"patchwork@1.1.1",
"pillar@1.7.0",
"pkgbuild@1.3.1",
"pkgconfig@2.0.3",
"plyr@1.8.6",
"posterior@1.2.0",
"prettyunits@1.1.1",
"processx@3.5.2",
"progress@1.2.2",
"promises@1.2.0.1",
"ps@1.6.0",
"purrr@0.3.4",
"rappdirs@0.3.3",
"readr@2.1.1",
"readxl@1.3.1",
"rematch2@2.1.2",
"rematch@1.0.1",
"renv@0.15.2",
"reprex@2.0.1",
"reshape2@1.4.4",
"rlang@1.0.1",
"rmarkdown@2.11",
"rprojroot@2.0.2",
"rsconnect@0.8.25",
"rstan@2.21.3",
"rstantools@2.1.1",
"rstudioapi@0.13",
"rvest@1.0.2",
"sass@0.4.0",
"scales@1.1.1",
"selectr@0.4-2",
"shiny@1.7.1",
"shinyjs@2.1.0",
"shinystan@2.5.0",
"shinythemes@1.2.0",
"sourcetools@0.1.7",
"stringi@1.7.6",
"stringr@1.4.0",
"svUnit@1.0.6",
"sys@3.4",
"tensorA@0.36.2",
"threejs@0.3.3",
"tibble@3.1.6",
"tidybayes@3.0.2",
"tidymodels/broom@HEAD",
"tidyr@1.2.0",
"tidyselect@1.1.1",
"tidyverse@1.3.1",
"tinytex@0.36",
"tzdb@0.2.0",
"utf8@1.2.2",
"uuid@1.0-3",
"vctrs@0.3.8",
"viridis@0.6.2",
"viridisLite@0.4.0",
"vroom@1.5.7",
"withr@2.4.3",
"xfun@0.29",
"xml2@1.3.2",
"xtable@1.8-4",
"xts@0.12.1",
"yaml@2.2.2",
"zoo@1.8-9"
)
# renv::embed() # to initialize and embed lock file in script; generates the renv::use(...) above
### Load packages
library(brms) # for implementing Bayesian multilevel analysis
library(tidyverse) # wrangling, plotting, etc.
library(tidybayes) # for preparing and visualizing prior and posterior draws
library(bayesplot) # for prior and posterior predictive plots
library(patchwork) # for panel plots
library(modelr) # for plotting
library(xtable) # for printing TeX tables
# load all fitted models
if (file.exists("pandemic_panel_fits_v2.RData")) base::load(file = "pandemic_panel_fits_v2.RData")
# increase memory allocation
memory.limit(size=56000)
### PRIOR VS. POSTERIOR PREDICTIVE CHECKS
## m0:
# prior predictive check
set.seed(2021)
yrep_pc_m0 <- posterior_predict(priorcheck_m0)
priorplot_m0 <- ppc_bars_grouped(y = as.numeric(priorcheck_m0$data[["y"]]),
yrep = yrep_pc_m0,
group = priorcheck_m0$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m0 <- posterior_predict(m0)
postplot_m0 <- ppc_bars_grouped(y = as.numeric(m0$data[["y"]]),
yrep = yrep_m0,
group = m0$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m0_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m0 + postplot_m0 + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
## m1:
# prior predictive check
set.seed(2021)
yrep_pc_m1 <- posterior_predict(priorcheck_m1)
priorplot_m1 <- ppc_bars_grouped(y = as.numeric(priorcheck_m1$data[["y"]]),
yrep = yrep_pc_m1,
group = priorcheck_m1$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m1 <- posterior_predict(m1)
postplot_m1 <- ppc_bars_grouped(y = as.numeric(m1$data[["y"]]),
yrep = yrep_m1,
group = m1$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m1_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m1 + postplot_m1 + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
## m_hinc:
# prior predictive check
set.seed(2021)
yrep_pc_m_hinc <- posterior_predict(pc_m_hinc)
priorplot_m_hinc <- ppc_bars_grouped(y = as.numeric(pc_m_hinc$data[["y"]]),
yrep = yrep_pc_m_hinc,
group = pc_m_hinc$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m_hinc <- posterior_predict(m_hinc)
postplot_m_hinc <- ppc_bars_grouped(y = as.numeric(m_hinc$data[["y"]]),
yrep = yrep_m_hinc,
group = m_hinc$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m_hinc_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m_hinc + postplot_m_hinc + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
## m_health:
# prior predictive check
set.seed(2021)
yrep_pc_m_health <- posterior_predict(pc_m_health)
priorplot_m_health <- ppc_bars_grouped(y = as.numeric(pc_m_health$data[["y"]]),
yrep = yrep_pc_m_health,
group = pc_m_health$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m_health <- posterior_predict(m_health)
postplot_m_health <- ppc_bars_grouped(y = as.numeric(m_health$data[["y"]]),
yrep = yrep_m_health,
group = m_health$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m_health_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m_health + postplot_m_health + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
## m_mrp:
# prior predictive check
set.seed(2021)
yrep_pc_m_mrp <- posterior_predict(priorcheck_m_mrp, resp = "y")
priorplot_m_mrp <- ppc_bars_grouped(y = as.numeric(priorcheck_m_mrp$data[["y"]]),
yrep = yrep_pc_m_mrp,
group = priorcheck_m_mrp$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m_mrp <- posterior_predict(m_mrp, resp = "y")
postplot_m_mrp <- ppc_bars_grouped(y = as.numeric(m_mrp$data[["y"]]),
yrep = yrep_m_mrp,
group = m_mrp$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m_mrp_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m_mrp + postplot_m_mrp + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
## m1_imp:
# prior predictive check
set.seed(2021)
yrep_pc_m1_imp <- posterior_predict(m1_imp_ppc, resp = "y")
priorplot_m1_imp <- ppc_bars_grouped(y = as.numeric(m1_imp_ppc$data[["y"]]),
yrep = yrep_pc_m1_imp,
group = m1_imp_ppc$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Prior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# posterior predictive check
set.seed(2021)
yrep_m1_imp <- posterior_predict(m1_imp, resp = "y")
postplot_m1_imp <- ppc_bars_grouped(y = as.numeric(m1_imp$data[["y"]]),
yrep = yrep_m1_imp,
group = m1_imp$data[["wave"]],
prob = 0.95,
size = 0.5,
fatten = 1,
facet_args = list(ncol = 4),
freq = FALSE) + # easier to compare groups (i.e., wave) with proportions, instead of counts
ylim(c(0,1)) + theme(legend.position = "null") + ggtitle("Posterior predictive check") +
scale_x_continuous(name="Response options")
set.seed(NULL)
# plot
cairo_pdf("m1_imp_ppc.pdf", width = 7, height = 5.5) # start print to pdf
(priorplot_m1_imp + postplot_m1_imp + patchwork::plot_layout(ncol = 1, nrow = 2))
dev.off() # end print to pdf
### POSTERIOR PREDICTED CUMULATIVE PROBABILITIES
## m1 at observed covariate values: alternative to plot in main manuscript
set.seed(2021)
m1_fitted <- m1$data %>%
group_by(id) %>%
# ndraws are numbers of draws per unique id, wave and response option
add_epred_draws(m1, ndraws = 1, newdata = ., re_formula = NA, robust = TRUE, resp = "y") %>%
mutate(category = factor(as.numeric(.category)))
set.seed(NULL)
m1_fitted <- m1_fitted %>%
group_by(id, .draw, category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
cairo_pdf("m1_obs_cumprob.pdf",
width = 8.5, height = 4.5) # start print to pdf
m1_fitted %>%
ggplot(aes(x = wave,
y = .epred,
color = category,
group = indices)) +
facet_wrap(~factor(gender, levels = c("2","1")), nrow = 1) +
geom_line(alpha = .1) +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = 1,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
theme_bw() +
theme(
panel.border = element_blank(),
panel.grid.minor = element_blank(),
strip.text.x = element_blank(),
axis.text = element_text( size = 10),
axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12),
plot.margin = margin(1, 1, 1, 1)) + # top, right, bottom, left
scale_x_continuous(name="Wave", breaks=c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
ggtitle("Posterior predictions from adjusted model for men (left) and women (right)", subtitle = "With covariates at their observed values")
dev.off() # end print to pdf
## m0:
set.seed(2021)
m0_fitted <- m0$data %>%
modelr::data_grid(wave = modelr::seq_range(1:4, by = 1)) %>%
add_epred_draws(m0, ndraws = 300, newdata = ., re_formula = NA) %>%
mutate(category = factor(as.numeric(.category)))
set.seed(NULL)
m0_fitted <- m0_fitted %>%
group_by(.draw, category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
m0_fitted$c <- 0 # for facet_wrap() later
## m0_complete:
set.seed(2021)
m0_complete_fitted <- m0_complete$data %>%
modelr::data_grid(wave = modelr::seq_range(1:4, by = 1)) %>%
add_epred_draws(m0_complete, ndraws = 300, newdata = ., re_formula = NA) %>%
mutate(category = factor(as.numeric(.category)))
set.seed(NULL)
m0_complete_fitted <- m0_complete_fitted %>%
group_by(.draw, category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
m0_complete_fitted$c <- 1 # for facet_wrap() later
m0_plot_df <- rbind(m0_fitted, m0_complete_fitted)
cairo_pdf("m0_cumprob.pdf", width = 8.5, height = 4.5) # start print to pdf
m0_est_plot <- m0_plot_df %>%
ggplot(aes(x = wave,
y = .epred,
color = .category,
group = indices)) +
geom_line(alpha = .1) +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = 1,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap(~ factor(c, levels = c("0", "1"))) +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text = element_text( size = 10),
axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12),
plot.margin = margin(1, 1, 1, 1)) +
scale_x_continuous(name="Wave", breaks=c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) + # controls the legend alpha separately from the geom_line() alpha
ggtitle("Posterior predictions from unadjusted models: listwise deletion (left) vs. complete-cases (right)")
m0_est_plot
dev.off() # end to print pdf
## m1_complete
set.seed(2021)
m1_complete_fitted <- m1_complete$data %>%
modelr::data_grid(wave = modelr::seq_range(1:4, by = 1), age.c = 0, gender = c(2,1), edu = 3, health = 3, hinc = 6) %>%
add_epred_draws(m1_complete, ndraws = 300, newdata = ., re_formula = NA) %>%
mutate(category = factor(as.numeric(.category)))
set.seed(NULL)
m1_complete_fitted <- m1_complete_fitted %>%
group_by(.draw, category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
cairo_pdf("m1_complete_cumprob.pdf", width = 8.5, height = 4.5) # start print to pdf
m1_complete_fitted %>%
ggplot(aes(x = wave,
y = .epred,
color = category,
group = indices)) +
facet_wrap(~factor(gender, levels = c("2","1")), nrow = 1) +
geom_line(alpha = .1) +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = 1,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
theme_bw() +
theme(
panel.border = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text = element_text( size = 10),
axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12),
plot.margin = margin(1, 1, 1, 1)) + # top, right, bottom, left
scale_x_continuous(name="Wave", breaks=c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
ggtitle("Posterior predictions from complete-cases adjusted model men (left) and women (right)", subtitle = "With covariates at their reference values")
dev.off() # end to print pdf
## m_mrp
# credit to:
# https://www.monicaalexander.com/posts/2019-08-07-mrp/
# https://rohanalexander.com/posts/2019-12-04-getting_started_with_mrp/
# https://osf.io/preprints/socarxiv/3v5g7/
# construct external dataset to use for poststratification:
# https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do
# https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfsa_pgaed
# load external data for poststratification
eurostat <- read.csv("eurostat2021.csv", sep = ",")
# create data for poststratification
post_df <- with(eurostat, data.frame(wave = rep(seq(1, 4, by = 1), each = 18),
gender = rep(SEX, 4),
age = rep(AGE, 4),
edu = rep(ISCED11, 4),
value = rep(Value, 4))) # number of people with given combination of demographic characteristics
# bin demographic variables
post_df$gender[post_df$gender == "Females"] <- 1 # women
post_df$gender[post_df$gender == "Males"] <- 2 # men
post_df$age[post_df$age == "From 15 to 24 years"] <- 1 # 15-24
post_df$age[post_df$age == "From 25 to 49 years"] <- 2 # 25-49
post_df$age[post_df$age == "From 50 to 74 years"] <- 3 # 50-74
post_df$edu[post_df$edu == "Less than primary, primary and lower secondary education (levels 0-2)"] <- 1 # primary to lower secondary
post_df$edu[post_df$edu == "Upper secondary and post-secondary non-tertiary education (levels 3 and 4)"] <- 2 # # upper secondary and post-secondary non-tertiary education
post_df$edu[post_df$edu == "Tertiary education (levels 5-8)"] <- 3 # tertiary education
str(post_df)
# Table S1: table of external poststrat data
eurostat$ISCED11[eurostat$ISCED11 == "Less than primary, primary and lower secondary education (levels 0-2)"] <- "Levels 0-2" # primary to lower secondary
eurostat$ISCED11[eurostat$ISCED11 == "Upper secondary and post-secondary non-tertiary education (levels 3 and 4)"] <- "Levels 3-4" # # upper secondary and post-secondary non-tertiary education
eurostat$ISCED11[eurostat$ISCED11 == "Tertiary education (levels 5-8)"] <- "Levels 5-8" # tertiary education
eurostat$SEX[eurostat$SEX == "Females"] <- "Female" # women
eurostat$SEX[eurostat$SEX == "Males"] <- "Male" # men
post_tab <- eurostat %>%
group_by(SEX, AGE, ISCED11) %>%
summarize(N = round(Value*1000)) %>%
distinct() %>%
ungroup()
colnames(post_tab) <- c("Gender", "Age", "Education", "N")
print(xtable(post_tab,
align = c("l", "l", "c", "c", "c"), digits = c(rep(0, 5)),
caption = "Poststratification matrix"), include.rownames = FALSE)
# overall poststratification
ps_prop_pop <- post_df %>% # get proportions for each combination of demographic variables
group_by(wave) %>%
mutate(prop = value/sum(value)) %>%
ungroup()
ps_prop_pop
set.seed(2021)
post_est_pop_fitted <- m_mrp %>%
add_epred_draws(newdata=ps_prop_pop, ndraws = 300, re_formula = ~ (1 | age) + (1 | gender) + (1 | edu), allow_new_levels = TRUE) %>%
rename(estimate = .epred) %>%
mutate(estimate_prop = estimate*prop) %>%
group_by(wave, .draw, .category) %>%
summarise(estimate_sum = sum(estimate_prop)) %>%
group_by(.draw, .category) %>%
mutate(indices = cur_group_id()) %>%
ungroup() %>%
rename(.epred = estimate_sum)
set.seed(NULL)
post_est_pop_fitted
post_est_pop_fitted$ps <- 1 # for plotting
# compare with unadjusted model (m0)
set.seed(2021)
no_post_est_pop_fitted <- m0 %>%
add_epred_draws(newdata = data.frame(wave=seq(1, 4, by = 1)), re_formula = NA, ndraws = 300) %>%
group_by(wave, .draw, .category) %>%
summarise(.epred = .epred) %>%
group_by(.draw, .category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
set.seed(NULL)
no_post_est_pop_fitted
no_post_est_pop_fitted$ps <- 0 # for plotting
# plot poststratified model predictions vs. unadjusted model (m0)
post_est <- rbind(post_est_pop_fitted, no_post_est_pop_fitted)
cairo_pdf("mrp_cumprob.pdf", width = 8.5, height = 4.5) # start print to pdf
post_est_plot <- post_est %>%
ggplot(aes(x = wave,
y = .epred,
color = .category,
group = indices)) +
geom_line(alpha = .1) +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = 1,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap(~ factor(ps, levels = c("1", "0"))) +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text = element_text( size = 10),
axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12),
plot.margin = margin(1, 1, 1, 1)) +
scale_x_continuous(name="Wave", breaks=c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) + # controls the legend alpha separately from the geom_line() alpha
ggtitle("Posterior predictions with (left) and without (right) poststratification",
subtitle = "Poststratification marginalizes over covariates (age, gender, education)")
post_est_plot
dev.off() # end print to pdf
## m1_imp
## plug posterior mean of imputations
m1_imp_pred <- m1_imp$data
# imputed income
hinc_impute <- rstan::extract(m1_imp$fit, "Ymi_hinc")$"Ymi_hinc"
hinc_impute_mean <- apply(hinc_impute, 2, mean)
hinc_impute_idx <- which(is.na(m1_imp_pred$hinc))
m1_imp_pred[hinc_impute_idx,]$hinc <- hinc_impute_mean
# imputed health
health_impute <- rstan::extract(m1_imp$fit, "Ymi_health")$"Ymi_health"
health_impute_mean <- apply(health_impute, 2, mean)
health_impute_idx <- which(is.na(m1_imp_pred$health))
m1_imp_pred[health_impute_idx,]$health <- health_impute_mean
# imputed edu
edu_impute <- rstan::extract(m1_imp$fit, "Ymi_edu")$"Ymi_edu"
edu_impute_mean <- apply(edu_impute, 2, mean)
edu_impute_idx <- which(is.na(m1_imp_pred$edu))
m1_imp_pred[edu_impute_idx,]$edu <- edu_impute_mean
set.seed(2021)
m1_imp_fitted <- m1_imp_pred %>%
group_by(id) %>%
# ndraws are numbers of draws per unique id, wave and response option
add_epred_draws(m1_imp, ndraws = 1, newdata = ., re_formula = NA, resp = "y") %>%
mutate(category = factor(as.numeric(.category)))
set.seed(NULL)
m1_imp_fitted <- m1_imp_fitted %>%
group_by(id, .draw, category) %>%
mutate(indices = cur_group_id()) %>%
ungroup()
cairo_pdf("m1_imp_cumprob.pdf", width = 8.5, height = 4.5) # start print to pdf
m1_imp_fitted %>%
ggplot(aes(x = wave,
y = .epred,
color = category,
group = indices)) +
facet_wrap(~factor(gender, levels = c("2","1")), nrow = 1) +
geom_line(alpha = .1) +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = 1,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
theme_bw() +
theme(
panel.border = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text = element_text( size = 10),
axis.title.x = element_text(size=12),
axis.title.y = element_text(size=12),
plot.margin = margin(1, 1, 1, 1)) + # top, right, bottom, left
scale_x_continuous(name="Wave", breaks=c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
ggtitle("Posterior predictions from imputation model: men (left) and women (right)",
subtitle = "With covariates at their observed/imputed values")
dev.off() #end print to pdf
### INTERACTION PLOTS
## m_hinc:
hinc_c <- as.data.frame(modelr::data_grid(d_full, wave = c(1,2,3,4), hinc = c(1,2,3,4,5,6,7,8,9,10,11))) # combination of covariates to condition on
hinc_cond <- conditional_effects(m_hinc, effect = "wave", conditions = hinc_c, # effect goes on the x-axis
robust = TRUE, # TRUE = posterior medians (instead of means)
categorical = TRUE, # set categorical = F to see (improper) continuous interaction
prob = .95, # toggle interval width
re_formula = NA) # include (NULL) or not (NA) random effects
hinc_labels <- c("1" = "Less than 100.000 kr.", "2" = "100.000 to 199.999 kr.",
"3" = "200.000 to 299.999 kr.", "4" = "300.000 to 399.999 kr.",
"5" = "400.000 to 499.999 kr.", "6" = "500.000 to 599.999 kr.",
"7" = "600.000 to 699.999 kr.", "8" = "700.000 to 799.999 kr.",
"9" = "800.000 to 899.999 kr.", "10" = "900.000 to 999.999 kr.",
"11" = "1.000.000 kr. or more")
cairo_pdf("hinc_cumprob.pdf", width = 8.5, height = 8.5) # start print to pdf
plot(hinc_cond, plot = FALSE)[[1]] +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
viridis::scale_fill_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap(~ factor(hinc, levels = c(1,2,3,4,5,6,7,8,9,10,11)), labeller = as_labeller(hinc_labels)) +
theme_minimal() +
theme(legend.position = c(0.9, 0.15)) +
scale_x_continuous(breaks = c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
ggtitle("Annual household income")
dev.off()
## m_health:
health_c <- as.data.frame(modelr::data_grid(d_full, wave = c(1,2,3,4), health = c(1,2,3,4,5))) # combination of covariates to condition on
health_cond <- conditional_effects(m_health, effect = "wave", conditions = health_c, # effect goes on the x-axis
robust = TRUE, # TRUE = posterior medians (instead of means)
categorical = TRUE, # set categorical = F to see (improper) continuous interaction
prob = .95, # toggle interval width
re_formula = NA) # include (NULL) or not (NA) random effects
health_labels <- c("1" = "''Very bad''", "2" = "''Bad''",
"3" = "''Okay''", "4" = "''Good''",
"5" = "''Very good''")
cairo_pdf("health_cumprob.pdf", width = 8.5, height = 8.5) # start print to pdf
plot(health_cond, plot = FALSE)[[1]] +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
viridis::scale_fill_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap(~ factor(health, levels = c(1,2,3,4,5)), labeller = as_labeller(health_labels)) +
theme_minimal() +
theme(legend.position = c(0.85, 0.25)) +
scale_x_continuous(breaks = c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
ggtitle("''How would you describe your current health?''")
dev.off()
## exposure models
exp_labels <- c("1" = "''No, but have not been tested''", "2" = "''No, and have tested negative''",
"3" = "''Yes, have tested positive but was not very ill''", "4" = "''Yes, have tested positive and was very ill''",
"5" = "''Yes, was hospitalized''")
# m_self:
self_c <- as.data.frame(modelr::data_grid(df_exp, wave = c(1,2,3,4), exp_self = c(1,2,3,4,5))) # combination of covariates to condition on
self_cond <- conditional_effects(m_self, effect = "wave", conditions = self_c, # effect goes on the x-axis
robust = TRUE, # TRUE = posterior medians (instead of means)
categorical = TRUE, # set categorical = F to see (improper) continuous interaction
prob = .95, # toggle interval width
re_formula = NA) # include (NULL) or not (NA) random effects
cairo_pdf("self_cumprob.pdf", width = 8.5, height = 8.5) # start print to pdf
plot(self_cond, plot = FALSE)[[1]] +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
viridis::scale_fill_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap("exp_self", labeller = as_labeller(exp_labels)) +
theme_minimal() +
theme(legend.position = c(0.85, 0.225)) +
scale_x_continuous(breaks = c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
ggtitle("''I've been ill [with corona virus] myself''")
dev.off()
# m_household:
household_c <- as.data.frame(modelr::data_grid(df_exp, wave = c(1,2,3, 4), exp_household = c(1,2,3,4,5))) # combination of covariates to condition on
household_cond <- conditional_effects(m_household, effect = "wave", conditions = household_c, # effect goes on the x-axis
robust = TRUE, # TRUE = posterior medians (instead of means)
categorical = TRUE, # set categorical = F to see (improper) continuous interaction
prob = .95, # toggle interval width
re_formula = NA) # include (NULL) or not (NA) random effects
cairo_pdf("household_cumprob.pdf", width = 8.5, height = 8.5) # start print to pdf
plot(household_cond, plot = FALSE)[[1]] +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
viridis::scale_fill_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap("exp_household", labeller = as_labeller(exp_labels)) +
theme_minimal() +
theme(legend.position = c(0.85, 0.225)) +
scale_x_continuous(breaks = c(1,2,3, 4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
ggtitle("''Someone in my household is/has been ill [with corona virus]''")
dev.off()
# m_family:
family_c <- as.data.frame(modelr::data_grid(df_exp, wave = c(1,2,3,4), exp_family = c(1,2,3,4,5))) # combination of covariates to condition on
family_cond <- conditional_effects(m_family, effect = "wave", conditions = family_c, # effect goes on the x-axis
robust = TRUE, # TRUE = posterior medians (instead of means)
categorical = TRUE, # set categorical = F to see (improper) continuous interaction
prob = .95, # toggle interval width
re_formula = NA) # include (NULL) or not (NA) random effects
cairo_pdf("family_cumprob.pdf", width = 8.5, height = 8.5) # start print to pdf
plot(family_cond, plot = FALSE)[[1]] +
viridis::scale_color_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
viridis::scale_fill_viridis(discrete = TRUE,
option = "D",
alpha = .8,
name="How important is \nreligion in your life?",
labels=c("Not at all important", "Not very important", "Somewhat important", "Very important")) +
facet_wrap("exp_family", labeller = as_labeller(exp_labels)) +
theme_minimal() +
theme(legend.position = c(0.85, 0.225)) +
scale_x_continuous(breaks = c(1,2,3,4)) +
scale_y_continuous(name="Probability", breaks=c(0, .2, .4, .6, .8, 1), limits = c(0,.96)) +
ggtitle("''Someone in my family is/has been ill [with corona virus]''")