set.seed(23)
sim6b <- params6b %>% make_data(N = 500, start = 2008, stop = 2022, rho_t=0.8)
sim6b %>% plot_data()
df_sim6b <- sim6b %>% pluck("df") %>% prepare()
sim6b_truth <- sim6b$truth
sim6b_desc <- sim6b$params$desc
sim6b_est <- df_sim6b %>% estimate()
sim6b_res <- sim6b_est %>% discriminate(truth = sim6b$truth)
plan(multisession,workers=parallel::detectCores()-1)
res6b <-
with_progress({
gen_est_disc(1:100, params = params6b, start = 2008, stop = 2022, rho_t = 0.8, N = 500)
})
p_df6b <-
res6b %>%
bind_rows() %>%
group_by(rel_time) %>%
summarise_all(mean) %>%
gather(estimator,estimate,-rel_time) %>%
filter(estimator!="truth")
ests <- p_df6b$estimator %>% unique()
p_df6b_ <-
ests %>%
map_df(~({
res6b[[1]] %>%
select(rel_time,truth) %>%
gather(estimator,estimate,-rel_time) %>%
mutate(estimator_p = estimator) %>%
mutate(estimator=.x)
}))
p_df6b %>% mutate(estimator = ests_lut[estimator]) %>%
ggplot(aes(x = rel_time, y = estimate)) +
geom_point() +
geom_line() +
geom_line(data = p_df6b_ %>% mutate(estimator = ests_lut[estimator]), colour = "red") +
hrbrthemes::theme_ipsum() +
#ggsci::scale_color_aaas()+
#scale_y_continuous(limits = c(0,5.5)) +
#scale_x_continuous(expand=c(0.25,0), breaks = seq(0,10,1)) +
#geom_dl(method = list("last.bumpup"),aes(label = estimator))+
#geom_dl(method = list("first.bumpup"),aes(label = estimator))+
theme(legend.position="none") +
facet_wrap(~estimator, scales = "free") +
labs(x = "Relative Time\n(Note: Truth is Shown in Red", y = "Estimate") +
ggtitle("Staggered Enty\nHeterogeneous Dynamic Treatment Effects")