Install and load packages
install.packages("DeclareDesign")
devtools::install_github("gerasy1987/hiddenmeta", build_vignettes = TRUE)Step 1. Provide study design features
## STUDY 1
study_1 <-
list(
pop =
list(
handler = get_study_population,
# network structure setup
network_handler = sim_block_network,
network_handler_args =
list(N = 1000, K = 2, prev_K = c(known = .1, hidden = .2), rho_K = 0,
p_edge_within = list(known = c(0.1, 0.1), hidden = c(0.1, 0.3)),
p_edge_between = list(known = 0.02, hidden = 0.02),
directed = FALSE),
# groups
group_names = c("known", "hidden"),
# probability of visibility (show-up) for each group
p_visible = list(known = 1, hidden = .8),
# probability of service utilization in hidden population
# for service multiplier
add_groups =
list(
service_use = "rbinom(.N, 1, 0.25)",
"paste0('loc_', 1:10) := lapply(rep(.2, times = 10), function(add) rbinom(.N, 1, 0.05 + hidden * add))",
target = 0.3,
"paste0('known_', 2:10) := lapply(2:10, function(x) rbinom(.N, 1, 0.3))")
),
sample =
list(
rds = list(handler = sample_rds,
# RDS parameters
sampling_variable = "rds",
hidden_var = "hidden", # default
n_seed = 20,
n_coupons = 3,
add_seeds = 3,
target_type = "sample",
target_n_rds = 60),
tls = list(handler = sample_tls,
sampling_variable = "tls",
hidden_var = NULL,
# TLS sampling parameters
target_n_clusters = 5,
target_cluster_type = "fixed",
target_per_cluster = 15,
clusters = paste0("loc_", 1:10)),
pps = list(handler = sample_pps,
sampling_variable = "pps",
# prop sampling parameters
sampling_frame = NULL,
strata = NULL,
cluster = NULL,
target_n_pps = 150),
lts = list(handler = sample_rds,
sampling_variable = "lts",
hidden_var = "hidden",
n_seed = 50,
n_coupons = 3,
n_waves = 3,
target = "waves",
target_n_rds = 200,
linktrace = "all")
),
inquiries = list(handler = get_study_estimands,
in_groups = "^target$",
out_groups = "^known$",
hidden_var = "hidden"),
estimators =
list(
rds =
list(ss = list(handler = get_study_est_ss,
sampling_var = "hidden",
hidden_var = "target", # estimates prevalence withing RDS
n_coupons = 3,
total = 1000,
prefix = "rds",
label = "ss"),
sspse = list(handler = get_study_est_sspse,
prior_mean = 200,
mcmc_params = list(interval = 5, burnin = 2000, samplesize = 500),
total = 1000,
prefix = "rds",
label = "rds_sspse"),
chords = list(handler = get_study_est_chords,
type = "mle",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds",
label = "rds_chords"),
multiplier = list(handler = get_study_est_multiplier,
service_var = "service_use",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds",
label = "rds_multi")),
tls =
list(ht = list(handler = get_study_est_ht,
hidden_var = "hidden",
weight_var = "tls_weight",
survey_design = ~ tls_loc_sampled,
prefix = "tls",
label = "tls_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("known", paste0("known_", 2:10)),
hidden = "hidden_visible_out",
survey_design = ~ tls_loc_sampled,
n_boot = 100,
prefix = "tls",
label = "tls_nsum"),
recap = list(handler = get_study_est_recapture,
capture_parse =
"strsplit(x = unique(na.omit(tls_locs_sampled)), split = ';')[[1]]",
# sample_condition = "tls == 1",
model = "Mt",
hidden_variable = "hidden",
label = "tls_recap"),
mse = list(handler = get_study_est_mse,
capture_parse =
"strsplit(x = unique(na.omit(tls_locs_sampled)), split = ';')[[1]]",
# sample_condition = "tls == 1",
method = "stepwise",
hidden_variable = "hidden",
label = "tls_mse")),
pps =
list(ht = list(handler = get_study_est_ht,
hidden_var = "hidden",
prefix = "pps",
label = "pps_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("known", paste0("known_", 2:10)),
hidden = "hidden_visible_out",
survey_design = ~ pps_cluster + strata(pps_strata),
n_boot = 100,
prefix = "pps",
label = "pps_nsum")),
all =
list(recap1 = list(handler = get_study_est_recapture,
capture_vars = c("rds", "pps"),
model = "Mt",
hidden_variable = "hidden",
label = "rds_pps_recap"),
recap2 = list(handler = get_study_est_recapture,
capture_vars = c("rds"),
capture_parse =
"strsplit(x = unique(na.omit(tls_locs_sampled)), split = ';')[[1]]",
model = "Mt",
hidden_variable = "hidden",
label = "rds_tls_recap"),
mse1 = list(handler = get_study_est_mse,
capture_vars = c("rds", "pps"),
method = "stepwise",
hidden_variable = "hidden",
label = "rds_pps_mse"),
mse2 = list(handler = get_study_est_mse,
capture_vars = c("rds"),
capture_parse =
"strsplit(x = unique(na.omit(tls_locs_sampled)), split = ';')[[1]]",
method = "stepwise",
hidden_variable = "hidden",
label = "rds_tls_mse")),
lts =
list(link = list(handler = get_study_est_linktrace,
total = 200,
strata = "hidden",
gibbs_params = list(n_samples = 5L,
chain_samples = 250L,
chain_burnin = 50L),
priors = list(p_n = 0L, p_l = 0.1, p_b = 1L),
prefix = "lts",
label = "link"))
)
)Step 2. Declare study population (M)odel
study_population <-
eval(as.call(c(list(declare_population), study_1$pop)))
set.seed(872312)
example_pop <- study_population()
example_pop[sample(1:min(.N, 1000))] %>%
DT::datatable(options = list(scrollX = TRUE, pageLength = 15))Simulated population network
g <-
example_pop %$% {
hiddenmeta:::retrieve_graph(links) %>%
igraph::set_vertex_attr("name", value = name) %>%
igraph::set_vertex_attr("type", value = type)
}
pal <-
grDevices::palette.colors(n = length(unique(igraph::V(g)$type)),
palette = "Dark2",
recycle = TRUE) %>%
`names<-`(unique(igraph::V(g)$type))
GGally::ggnet2(g, mode = "fruchtermanreingold",
edge.alpha = .5, edge.size = .2,
node.alpha = .9, node.size = 2.5, node.color = "type",
palette = pal,
color.legend = "type",
max_size = 4, shape = 16,
legend.size = 12, legend.position = "bottom") +
ggplot2::coord_equal()
Step 4. Declare study (D)ata strategy or Sampling
The sampling procedures are additive in a sense that each procedure
appends several columns relevant to the sampling procedure and
particular draw based on population simulation, but does not change the
study population data frame (unless you specify
drop_nonsampled = TRUE).
Step 5. Declare study (A)nswer strategy or estimators
est_ss <-
eval(as.call(c(list(declare_estimator), study_1$estimators$rds$ss)))
est_sspse <-
eval(as.call(c(list(declare_estimator), study_1$estimators$rds$sspse)))
est_chords <-
eval(as.call(c(list(declare_estimator), study_1$estimators$rds$chords)))
est_multi <-
eval(as.call(c(list(declare_estimator), study_1$estimators$rds$multiplier)))
est_ht_pps <-
eval(as.call(c(list(declare_estimator), study_1$estimators$pps$ht)))
est_nsum_pps <-
eval(as.call(c(list(declare_estimator), study_1$estimators$pps$nsum)))
est_ht_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$tls$ht)))
est_nsum_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$tls$nsum)))
est_recap_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$tls$recap)))
est_mse_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$tls$mse)))
est_recap_rds_pps <-
eval(as.call(c(list(declare_estimator), study_1$estimators$all$recap1)))
est_recap_rds_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$all$recap2)))
est_mse_rds_pps <-
eval(as.call(c(list(declare_estimator), study_1$estimators$all$mse1)))
est_mse_rds_tls <-
eval(as.call(c(list(declare_estimator), study_1$estimators$all$mse2)))
est_link <-
eval(as.call(c(list(declare_estimator), study_1$estimators$lts$link)))Step 6. Declare full study
study1 <-
# model (population)
study_population +
# inquiries
study_estimands +
# data strategy (samples)
study_sample_rds + study_sample_pps + study_sample_tls + study_sample_lts +
# answer strategy (estimators)
est_ss + est_sspse + est_chords +
est_multi +
est_nsum_pps + est_ht_pps +
est_nsum_tls + est_ht_tls + est_recap_tls + est_mse_tls +
est_recap_rds_pps + est_recap_rds_tls + est_mse_rds_pps + est_mse_rds_tls +
est_linkDraw inquiries
# can draw estimands of interest
draw_estimands(study1) %>%
kable(digits = 2)| inquiry | estimand |
|---|---|
| known_size | 89.00 |
| known_prev | 0.09 |
| hidden_size | 183.00 |
| hidden_prev | 0.18 |
| target_size | 304.00 |
| target_prev | 0.30 |
| target_size_in_hidden | 51.00 |
| target_prev_in_hidden | 0.28 |
| hidden_size_in_known | 18.00 |
| hidden_prev_in_known | 0.20 |
Draw estimates
# can draw estimates
draw_estimates(study1) %>%
kable(digits = 2)| estimator | estimate | se | inquiry |
|---|---|---|---|
| target_prev_in_hidden_ss | 0.44 | 0.10 | target_prev_in_hidden |
| hidden_size_rds_sspse | 160.50 | 88.73 | hidden_size |
| hidden_size_rds_chords | 42.00 | 51.16 | hidden_size |
| hidden_size_rds_multi | 176.67 | 32.49 | hidden_size |
| hidden_size_pps_nsum | 170.34 | 25.80 | hidden_size |
| hidden_size_pps_ht | 246.67 | 39.44 | hidden_size |
| hidden_prev_pps_ht | 0.25 | 0.04 | hidden_prev |
| hidden_size_tls_nsum | 252.44 | 40.23 | hidden_size |
| hidden_size_tls_ht | 131.41 | 41.37 | hidden_size |
| hidden_prev_tls_ht | 0.13 | 0.04 | hidden_prev |
| hidden_size_tls_recap | 190.95 | 11.69 | hidden_size |
| hidden_size_tls_mse | 182.52 | 8.49 | hidden_size |
| hidden_size_rds_pps_recap | 164.57 | 29.82 | hidden_size |
| hidden_size_rds_tls_recap | 190.95 | 11.69 | hidden_size |
| hidden_size_rds_pps_mse | 170.77 | 36.05 | hidden_size |
| hidden_size_rds_tls_mse | 182.52 | 8.49 | hidden_size |
| hidden_size_link | 148.60 | 7.31 | hidden_size |
Step 7. Diagnose study design
requireNamespace(c("doParallel", "parallel"))
doParallel::registerDoParallel(cores = 10)
set.seed(872312)
study1_simulations <-
plyr::llply(
as.list(1:100),
.fun = function(x) {
base::suppressWarnings(
DeclareDesign::simulate_design(study1, sims = 1) %>%
dplyr::mutate(
sim_ID = x
)
)
},
.parallel = TRUE
) %>%
dplyr::bind_rows()
saveRDS(study1_simulations, here::here("inst/extdata/study1_sim.rds"))
study_diagnosands <-
declare_diagnosands(
mean_estimand = mean(estimand),
mean_estimate = mean(estimate),
sd_estimate = sd(estimate),
mean_se = mean(se),
bias = mean(estimate - estimand),
rmse = sqrt(mean((estimate - estimand) ^ 2))
)
study1_simulations <-
readRDS(system.file("extdata", "study1_sim.rds", package = "hiddenmeta"))
diagnose_design(simulations_df = study1_simulations,
diagnosands = study_diagnosands) %>%
reshape_diagnosis() %>%
dplyr::select(-'Design') %>%
DT::datatable(options = list(scrollX = TRUE, pageLength = 15))Diagnosis of multiple studies
Additional study designs
study_2 <-
list(
pop =
list(
handler = get_study_population,
network_handler = sim_block_network,
network_handler_args =
list(N = 5000, K = 3,
prev_K = c(frame = .5, known = .2, hidden = .1),
rho_K = c(.05, .05, .05),
p_edge_within = list(frame = c(0.05, 0.05),
known = c(0.1, 0.05),
hidden = c(0.2, 0.9)),
p_edge_between = list(frame = 0.05,
known = 0.1,
hidden = 0.01),
directed = FALSE),
group_names = c("frame", "known", "hidden"),
# probability of visibility (show-up) for each group
p_visible = list(frame = 1, known = 1, hidden = .6),
# probability of service utilization in hidden population
# for service multiplier
add_groups =
list(service_use = "rbinom(.N, 1, 0.25 + hidden * 0.3)",
"paste0('loc_', 1:8) := lapply(runif(8, 0.05,.3), function(add) rbinom(.N, 1, 0.1 + hidden * add))",
known_2 = 0.1, known_3 = 0.2)
),
sample =
list(
rds = list(handler = sample_rds,
# RDS parameters
sampling_variable = "rds",
hidden_var = "hidden",
n_seed = 100,
n_coupons = 3,
n_waves = 2,
target_type = "waves",
target_n_rds = 2),
tls = list(handler = sample_tls,
sampling_variable = "tls",
# TLS sampling parameters
target_n_clusters = 8,
target_cluster_type = "fixed",
target_per_cluster = 40,
clusters = paste0("loc_", 1:10)),
pps = list(handler = sample_pps,
sampling_variable = "pps",
# prop sampling parameters
sampling_frame = "frame",
strata = NULL,
cluster = NULL,
target_n_pps = 800),
link_trace = list(handler = sample_rds,
sampling_variable = "link_trace",
hidden_var = "hidden",
n_seed = 50,
n_coupons = 3,
add_seeds = 0,
target = "sample",
target_n_rds = 150,
linktrace = TRUE)
),
inquiries = list(handler = get_study_estimands,
out_groups = "^known$|^frame$",
hidden_var = "hidden"),
estimators =
list(
rds =
list(sspse = list(handler = get_study_est_sspse,
label = "rds_sspse",
prior_mean = 450,
mcmc_params = list(interval = 5, burnin = 2000, samplesize = 500),
total = 5000,
prefix = "rds"),
chords = list(handler = get_study_est_chords,
type = "jeffreys",
label = "rds_chords",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds"),
multiplier = list(handler = get_study_est_multiplier,
service_var = "service_use",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds",
label = "rds_multi")),
tls =
list(ht = list(handler = get_study_est_ht,
weight_var = "tls_weight",
survey_design = ~ tls_loc_sampled,
prefix = "tls",
label = "tls_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("known", "frame"),
hidden = "hidden_visible_out",
survey_design = ~ tls_loc_sampled,
n_boot = 100,
prefix = "tls",
label = "tls_nsum"),
recap = list(handler = get_study_est_recapture,
capture_vars = paste0("loc_", 1:8),
sample_condition = "tls == 1",
model = "Mt",
hidden_variable = "hidden",
label = "tls_recap")),
pps =
list(
ht = list(handler = get_study_est_ht,
label = "pps_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("frame", "known"),
hidden = "hidden_visible_out",
survey_design = ~ pps_cluster + strata(pps_strata),
n_boot = 100,
label = "pps_nsum")),
all =
list(recap1 = list(handler = get_study_est_recapture,
capture_vars = c("rds", "pps"),
model = "Mt",
hidden_variable = "hidden",
label = "rds_pps_recap"),
recap2 = list(handler = get_study_est_recapture,
capture_vars = c("rds", paste0("loc_", 1:8)),
model = "Mt",
hidden_variable = "hidden",
label = "rds_tls_recap")),
link_trace =
list(link_trace = list(handler = get_study_est_linktrace,
total = 300,
strata = "hidden",
gibbs_params = list(n_samples = 5L,
chain_samples = 250L,
chain_burnin = 50L),
priors = list(p_n = 0L, p_l = 0.1, p_b = 1L),
prefix = "link_trace",
label = "link_trace"))
)
)
study_3 <-
list(
pop =
list(
handler = get_study_population,
# network structure setup
network_handler = sim_block_network,
network_handler_args =
list(N = 2000, K = 2, prev_K = c(known = .3, hidden = .1), rho_K = .3,
p_edge_within = list(known = c(0.05, 0.1), hidden = c(0.05, 0.7)),
p_edge_between = list(known = 0.05, hidden = 0.05),
directed = FALSE),
# groups
group_names = c("known", "hidden"),
# probability of visibility (show-up) for each group
p_visible = list(known = 1, hidden = .7),
# probability of service utilization in hidden population
# for service multiplier
add_groups =
list(service_use = "rbinom(n(), 1, 0.25 + hidden * 0.05)",
"paste0('loc_', 1:8) := lapply(c(.2, .1, .3, .2, .4, .1, .3, .2), function(add) rbinom(.N, 1, 0.1 + hidden * add))",
known_2 = 0.1, known_3 = 0.2)
),
sample =
list(
rds = list(handler = sample_rds,
# RDS parameters
sampling_variable = "rds",
hidden_var = "hidden", # default
n_seed = 20,
n_coupons = 3,
target_type = "sample",
target_n_rds = 100),
tls = list(handler = sample_tls,
sampling_variable = "tls",
# TLS sampling parameters
target_n_clusters = 4,
target_cluster_type = "fixed",
target_per_cluster = 30,
clusters = paste0("loc_", 1:8)),
pps = list(handler = sample_pps,
sampling_variable = "pps",
# prop sampling parameters
sampling_frame = NULL,
strata = NULL,
cluster = NULL,
target_n_pps = 400),
link_trace = list(handler = sample_rds,
sampling_variable = "link_trace",
hidden_var = "hidden",
n_seed = 50,
n_coupons = 3,
add_seeds = 0,
target = "sample",
target_n_rds = 150,
linktrace = TRUE)
),
inquiries = list(handler = get_study_estimands,
out_groups = "^known$",
hidden_var = "hidden"),
estimators =
list(
rds =
list(sspse = list(handler = get_study_est_sspse,
prior_mean = 200,
mcmc_params = list(interval = 5, burnin = 2000, samplesize = 500),
total = 2000,
prefix = "rds",
label = "rds_sspse"),
chords = list(handler = get_study_est_chords,
type = "jeffreys",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds",
label = "rds_chords"),
multiplier = list(handler = get_study_est_multiplier,
service_var = "service_use",
seed_condition = "rds_from == -999",
n_boot = 100,
prefix = "rds",
label = "rds_multi")),
tls =
list(ht = list(handler = get_study_est_ht,
weight_var = "tls_weight",
survey_design = ~ tls_loc_sampled,
prefix = "tls",
label = "tls_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("known", "known_2", "known_3"),
hidden = "hidden_visible_out",
survey_design = ~ tls_loc_sampled,
n_boot = 100,
prefix = "tls",
label = "tls_nsum"),
recap = list(handler = get_study_est_recapture,
capture_vars = paste0("loc_", 1:6),
# sample_condition = "tls == 1",
model = "Mt",
hidden_variable = "hidden",
label = "tls_recap")),
pps =
list(ht = list(handler = get_study_est_ht,
prefix = "pps",
label = "pps_ht"),
nsum = list(handler = get_study_est_nsum,
known = c("known", "known_2", "known_3"),
hidden = "hidden_visible_out",
survey_design = ~ pps_cluster + strata(pps_strata),
n_boot = 100,
prefix = "pps",
label = "pps_nsum")),
all =
list(recap1 = list(handler = get_study_est_recapture,
capture_vars = c("rds", "pps"),
model = "Mt",
hidden_variable = "hidden",
label = "rds_pps_recap"),
recap2 = list(handler = get_study_est_recapture,
capture_vars = c("rds", paste0("loc_", 1:4)),
model = "Mt",
hidden_variable = "hidden",
label = "rds_tls_recap")),
link_trace =
list(link_trace = list(handler = get_study_est_linktrace,
total = 300,
strata = "hidden",
gibbs_params = list(n_samples = 5L,
chain_samples = 250L,
chain_burnin = 50L),
priors = list(p_n = 0L, p_l = 0.1, p_b = 1L),
prefix = "link_trace",
label = "link_trace"))
)
)Declare multiple studies
multi_population <-
declare_population(handler = get_multi_populations,
pops_args = list(study_1 = study_1$pop,
# study_2 = study_2$pop,
study_3 = study_3$pop))
multi_sampling <-
declare_sampling(handler = get_multi_samples,
samples_args = list(study_1 = study_1$sample,
# study_2 = study_2$sample,
study_3 = study_3$sample))
multi_inquiry <-
declare_inquiry(handler = get_multi_estimands,
inquiries_args = list(study_1 = study_1$inquiries,
# study_2 = study_2$inquiries,
study_3 = study_3$inquiries))
multi_estimators <-
declare_estimator(handler = get_multi_estimates,
estimators_args = list(study_1 = study_1$estimators,
# study_2 = study_2$estimators,
study_3 = study_3$estimators))
multi_study <- multi_population + multi_sampling + multi_inquiry + multi_estimators
# set.seed(872312)
# draw_estimands(multi_study) %>%
# kable()
#
# draw_estimates(multi_study) %>%
# kable()Diagnose multiple studies
# multi_simulations <- simulate_design(multi_study, sims = 2)
requireNamespace(c("doParallel", "parallel"))
doParallel::registerDoParallel(cores = 40)
set.seed(872312)
multi_simulations <-
plyr::llply(
as.list(1:100),
.fun = function(x) {
base::suppressWarnings(
DeclareDesign::simulate_design(multi_study, sims = 1) %>%
dplyr::mutate(
sim_ID = x
)
)
},
.parallel = TRUE
) %>%
dplyr::bind_rows()
saveRDS(multi_simulations, file = here::here("inst/extdata/multi_sim.rds"))
multi_simulations <-
readRDS(system.file("extdata", "multi_sim.rds", package = "hiddenmeta"))
diagnose_design(simulations_df = multi_simulations,
diagnosands = study_diagnosands) %>%
reshape_diagnosis(digits = 2) %>%
dplyr::select(-'Design') %>%
dplyr::filter(`Mean Estimate` != "NA") %>%
DT::datatable(options = list(scrollX = TRUE, pageLength = 15))Diagnosis of meta-study
Conduct multi-study design for as many sampling-estimator pairs in each study as possible, then diagnose the multi-study design. Calculate average (across simulations) estimand and bias of sampling-estimator for each of the studies and estimator sampling strategies. These will serve as population we will be drawing population-sampling-estimator triads
-
Estimands include:
- Average estimand by inquiry label (within study)
- Average bias of specific sampling-estimator pair (across studies) compared to truth
- Average relative bias of sampling-estimator pair (across studies) compared to “gold standard”
- Ratio of average bias to costs of sampling-estimator pair
As mentioned before sampling will consist of drawing population-sampling-estimator triads from the population presuming that each study uses at least two sampling strategies at a time
Once we draw sample we use Stan model to estimate study-specific estimands and sampling-estimator specific errors (biases)
We have all parts to conduct the full cycle of meta-analyses
meta_population <-
declare_population(multi_design = multi_study, n_sim = 3, parallel = FALSE,
handler = get_meta_population)
meta_inquiry <-
declare_inquiry(study_estimand = "hidden_size",
samp_est_benchmark = "pps_ht",
handler = get_meta_estimands)
meta_sample <-
declare_sampling(sampling_variable = "meta",
selection_variables = c("sample", "estimator"),
samples_per_study = 2, estimator_per_sample = 3,
force = list(sample = "pps", estimator = "ht"),
handler = get_meta_sample)
meta_estimator <-
declare_estimator(sampling_variable = "meta",
which_estimand = "hidden_size",
benchmark = list(sample = "pps", estimator = "ht"),
control_params = list(
iter = 8000, chains = 8, thin = 10,
seed = 872312,
cores = 1),
stan_handler = get_meta_stan,
handler = get_meta_estimates)
meta_study <- meta_population + meta_inquiry + meta_sample + meta_estimator
# set.seed(872312)
# draw_estimands(meta_study) %>%
# kable()
#
# draw_estimates(meta_study) %>%
# kable()
requireNamespace(c("doParallel", "parallel"))
doParallel::registerDoParallel(cores = 20)
set.seed(872312)
meta_simulations <-
plyr::llply(
as.list(1:100),
.fun = function(x) {
base::suppressWarnings(
DeclareDesign::simulate_design(meta_study, sims = 1) %>%
dplyr::mutate(
sim_ID = x
)
)
},
.parallel = TRUE
) %>%
dplyr::bind_rows()
saveRDS(meta_simulations, file = here::here("inst/extdata/meta_sim.rds"))
meta_simulations <-
readRDS(system.file("extdata", "meta_sim.rds", package = "hiddenmeta"))
diagnose_design(simulations_df = meta_simulations,
diagnosands = study_diagnosands) %>%
reshape_diagnosis(digits = 2) %>%
dplyr::select(-'Design') %>%
dplyr::filter(`Mean Estimate` != "NA") %>%
DT::datatable(options = list(scrollX = TRUE, pageLength = 15))