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server.R
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###############################################################################
#### IMPORTANT NOTE FOR ****** APP DEVELOPERS *******
###############################################################################
# At the time of writing, it is only possible to publish this app to shinyapps.io
# if using a UNIX-based opertating sustem (e.g., Linux Mint, Ubuntu, MacOS)
# This is because the Stan models need to be pre-compiled, and the compiler
# flags (i.e., CXXFLAGS) need to match the ones that
# shinyapps.io (shinyapps.io is hosted on a UNIX server).
# See https://discourse.mc-stan.org/t/compile-stan-model-in-shiny-app/10022/16
# for more details
# # Although not using pre-compiled models would allow app developers to publish
# the app using Windows, Without doing this, users would need to wait 15+ minutes
# for the models to compile before they can even start using the app, every
# time the app is opened or refreshed!
###############################################################################
server <- function(input, output, session) {
shinyalert(title = "Message from Authors",
text = paste0("If you have time it would be greatly appreciated if you could fill
out the user feedback questionnaire ",
tags$a(href="https://docs.google.com/forms/d/e/1FAIpQLSdBvMFpWma87JV1R0lAkmiVWxcIFf9I0m2BiDS6JV20MrvE9Q/viewform?vc=0&c=0&w=1&flr=0"
, "here.",target="_blank"),
" Please report any bugs or suggestions for new features to the CRSU Team at ", tags$a(href="mailto:[email protected]", "[email protected]"), "."),
type = "info",
confirmButtonText = "Okay",
html = TRUE)
shinyalert(title = "Important message",
text = paste("In accordance with Data Protection legislation, we would like to inform you of the following before you
use our website: We collect your usage data within the MetaBayesDTA app to perform analytics of usage and
improve our app. By clicking", tags$i(tags$u("I consent")), "below, you consent to the use of data
by us through Google Analytics. For details of policy, please check",
tags$a(href="https://policies.google.com/privacy?hl=en", "Google Privacy & Terms.",target="_blank")
),
type = "info",
confirmButtonText = "I consent",
html = TRUE)
Standard <- read.csv('./Standard.csv')
QA <- read.csv('./QA.csv')
Cov <- read.csv('./Cov.csv')
QA_Cov <- read.csv('./QA_Cov.csv')
defaultData <- dataset_default_import_server(id = "dataset_id",
QA = QA,
Cov = Cov,
QA_Cov = QA_Cov,
Standard = Standard)
# Load data
data <- dataset_import_server(
id = "dataset_id",
defaultData
)
# Download User Guide
# Allow users the option to download the standard example dataset
output$downloadUG <- downloadHandler(
# Specify the file name
filename = function() {
paste("MetaDTA User Guide v1_0.pdf")
},
content = function(file) {
file.copy("www/MetaDTA User Guide v1_0.pdf", file)
}
)
#Download the instructions for reproducing analyses in the Cochrane Handbook
output$downloadCochraneGuide <- downloadHandler(
filename = function(){
paste("Cochrane-DTA-in-MetaBayesDTA v2_0.html")
},
content = function(file){
utils::download.file("https://github.com/CRSU-Apps/MBDTA-Cochrane-guide/blob/main/Cochrane-DTA-in-MetaBayesDTA.html?raw=TRUE", file)
}
)
# Load data tab --------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------------
data_for_analysis_server(id = "dataset_id",
data = data)
dataset_example_download_server(id = "dataset_id",
QA = QA,
Cov = Cov,
QA_Cov = QA_Cov,
Standard = Standard)
##### Standard bivariate model (BVM) ---------------------------------------- --------------------------------------------------------------------------------------------------------------
# load sensitivity analysis (SA) study list and data
SA_list_studies_server(id = "SA_MA_model_id",
data = data)
SA_data <- SA_data_server(id = "SA_MA_model_id",
data = data)
SA_data_table_server(id = "SA_MA_model_id",
data = data)
SA_indicator <- SA_indicator_out(id = "SA_MA_model_id")
SA_display_conditional_plot_checkbox_renderUI(id = "MA_model_id",
SA_indicator = SA_indicator)
MA_p_scale_priors_indicator <- p_scale_priors_indicator_checkbox_out(id = "MA_model_id")
# BVM- Forest plots ------------------------------------------------------------ ----------------------------------
MA_forest_plots_server(id = "MA_model_id",
data = data)
# BVM- Display input options for standard BVM tab ------------------------------ ----------------------------------
# BVM- server-side module for prior options UI (to be used w/ shinydashboard)
MA_priors_options_inputModule_renderUI_server(id = "MA_model_id")
priors <- MA_priors_options_inputModule_server(id = "MA_model_id")
# BVM- server-side module for Stan sampler options UI (to be used w/ shinydashboard)
sampler_options_server(id = "MA_model_id")
sampler_options <- sampler_options_inputModule_server(id = "MA_model_id")
# BVM- server functions for progress indicators / loading bars ---------------- ----------------------------------
progress_prior_model_server(id = "MA_model_id")
progress_main_model_server(id = "MA_model_id")
progress_main_model_server(id = "SA_MA_model_id")
# BVM- Run Bivariate Model - prior-only model ------------------------------- ----------------------------------
MA_model_PO <- StanModel$new(stan_model_rds_path = "./models/BVM_PO.rds",
stan_model_path = "./models/BVM_PO.stan")
MA_model_PO_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/BVM_PO_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/BVM_PO_p_scale_priors.stan")
# BVM- call the server module for button
MA_model_PO_button <- run_prior_model_button_server(id = "MA_prior_model_button_id")
# BVM- call server module to run stan model and save posterior draws
draws_PO <- MA_run_model_priors_only(id = "MA_model_id",
button = MA_model_PO_button,
stan_model = MA_model_PO,
stan_model_p_scale_priors = MA_model_PO_p_scale_priors,
p_scale_priors_indicator = MA_p_scale_priors_indicator)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# BVM- Run Bivariate Model - full model ----------------------------- ----------------------------------
MA_model <- StanModel$new(stan_model_rds_path = "./models/BVM.rds",
stan_model_path = "./models/BVM.stan")
MA_model_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/BVM_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/BVM_p_scale_priors.stan")
# BVM- call the server module for buttons
MA_model_button <- run_model_button_server(id = "MA_model_button_id")
SA_MA_model_button <- SA_run_model_button_server(id = "SA_MA_model_button_id")
## BVM- call server module to run stan model and save posterior draws
draws <- MA_run_model(id = "MA_model_id",
button = MA_model_button,
stan_model = MA_model,
stan_model_p_scale_priors = MA_model_p_scale_priors,
dataset = data,
priors = priors,
sampler_options = sampler_options,
SA_indicator = SA_indicator,
SA_indicator_local = 0,
p_scale_priors_indicator = MA_p_scale_priors_indicator)$draws
SA_draws <- MA_run_model(id = "SA_MA_model_id",
button = SA_MA_model_button,
stan_model = MA_model,
stan_model_p_scale_priors = MA_model_p_scale_priors,
dataset = data,
priors = priors,
sampler_options = sampler_options,
SA_indicator = SA_indicator,
SA_indicator_local = 1,
p_scale_priors_indicator = MA_p_scale_priors_indicator)$draws
# BVM- Revman plots ----------------------------------------------------------- ------------------------------------
MA_revman_plots_server(id = "MA_model_id",
data = data,
draws = draws)
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# BVM- Table of model diagnostics --------------------------------------------- ------------------------------------
MA_model_diagnostics_tab_renderUI_server(id = "MA_model_id",
SA_indicator = SA_indicator)
sampler_diagnostics_table_server(id = "MA_model_id",
draws = draws,
sampler_options = sampler_options)
# SA
sampler_diagnostics_table_server(id = "SA_MA_model_id",
draws = SA_draws,
sampler_options = sampler_options)
# BVM- Table of r-hat statistics -------------------------------------------- ------------------------------------
MA_rhat_table_server(id = "MA_model_id",
draws = draws)
# SA
MA_rhat_table_server(id = "SA_MA_model_id",
draws = SA_draws)
# BVM- Posterior density plots ---------------------------------------------- -------------------------------------
MA_model_posterior_density_plots_server(id = "MA_model_id",
data = data,
draws = draws)
# SA
MA_model_posterior_density_plots_server(id = "SA_MA_model_id",
data = SA_data,
draws = SA_draws)
# BVM- Trace plots ---------------------------------------------------------- -------------------
MA_model_trace_plots_server(id = "MA_model_id",
data = data,
draws = draws)
# SA
MA_model_trace_plots_server(id = "SA_MA_model_id",
data = SA_data,
draws = SA_draws)
# BVM- Plot of prior distributions ------------------------------------------- --------------------------------------------------------
MA_model_priors_plot_server(id = "MA_model_id",
draws = draws_PO)
# BVM- Table of prior distributions ------------------------------------------ ---------------------------------------------------------
MA_model_priors_table_server(id = "MA_model_id",
draws = draws_PO)
# BVM- Table which displays observed data and study weights from model + download ----------------------------------------------------------------
MA_data_table_server(id = "MA_model_id",
data = data,
draws = draws)
# BVM- Parameter estimates table -------------------------------------------- -------------------------------------------------------
# renderUI module
MA_parameter_estimates_tab_renderUI_server(id = "MA_model_id",
SA_indicator = SA_indicator)
MA_parameter_estimates_table_server(id = "MA_model_id",
data = data,
draws = draws)
# SA
MA_parameter_estimates_table_server(id = "SA_MA_model_id",
data = SA_data,
draws = SA_draws)
# BVM- sROC plot + download + tooltips ------------------------------------------ --------------------------------------------------------------------------------------
covariate_display_server(id = "MA_model_id",
data = data)
HSROC_curve_type_renderUI(id = "MA_model_id")
#SA_display_indicator_renderUI(id = "MA_model_id", SA_indicator = SA_indicator)
sroc_plot_settings_menu_server(id = "MA_model_id",
data = data)
MA_sroc_plot_server(id = "MA_model_id",
data = data,
SA_data = SA_data,
draws = draws,
SA_draws = SA_draws,
SA_indicator = SA_indicator)
# BVM - Scripts to overlay tooltips over the sROC plot, when study clicked on --- -------------------------------
# for study info
output$MA_sroc_study_info_css_head <- renderUI({
tags$head( (tags$style('
#MA_model_id-my_tooltip {
position: absolute;
width: 500px;
z-index: 10;
padding: 0;
}
#MA_model_id-vals {
color: white;
background: blue;
font-size: 11px;
}
')) ) })
output$MA_sroc_study_info_css_script <- renderUI({
tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#MA_model_id-plot").mousemove(function(e) {
// put ID of uiOutput below
$("#MA_model_id-my_tooltip").show();
$("#MA_model_id-my_tooltip").css({
top: (e.pageY + 50 ) + "px",
left: (e.pageX - 1400) + "px"
});
});
});
') })
## for piecharts
output$MA_sroc_piechart_css_head <- renderUI({
tags$head( tags$style('
#MA_model_id-piechart {
position: absolute;
width: 500px;
z-index: 5;
padding: 0;
}
') ) })
output$MA_sroc_piechart_css_script <- renderUI({
tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#MA_model_id-plot").mousemove(function(e) {
// put ID of uiOutput below
$("#MA_model_id-piechart").show();
$("#MA_model_id-piechart").css({
top: (e.pageY + 200 ) + "px",
left: (e.pageX - 2000) + "px"
});
});
});
') })
# BVM- Prevalence tab -------------------------------------------------------------------------------------
Prev_input_server(id = "MA_model_id",
data = data)
Prev_plot_server(id = "MA_model_id",
data = data,
draws = draws)
# SA
Prev_input_server(id = "SA_MA_model_id",
data = SA_data)
Prev_plot_server(id = "SA_MA_model_id",
data = SA_data,
draws = SA_draws)
# Meta-regression (MR) ---------------------------------------------------------------------------------------------------------------------------------------------------------------
MR_covariate_model_server(id = "MR_model_id",
data = data,
cts_cov_indicator = MR_cts_cov_indicator)
MR_cts_cov_indicator <- MR_cts_cov_indicator_out(id = "MR_model_id",
data = data)
# observe({
# print(MR_cts_cov_indicator$cts_cov_indicator)
# })
# MR -server-side module for prior options UI (to be used w/ shinydashboard)
MR_priors_options_server(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator)
# MR - server-side module for Stan sampler options UI (to be used w/ shinydashboard)
sampler_options_server(id = "MR_model_id")
sampler_options <- sampler_options_inputModule_server(id = "MR_model_id")
# MR - server functions for progress indicators / loading bars
progress_prior_model_server(id = "MR_model_id")
progress_main_model_server(id = "MR_model_id")
### MR - Run models ----------------------------------------------
MR_p_scale_priors_indicator_checkbox_renderUI(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator)
MR_p_scale_priors_indicator <- MR_p_scale_priors_indicator_checkbox_out(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator)
# MR - prior-only models ---
MR_model_cts_PO <- StanModel$new(stan_model_rds_path = "./models/MR_cts_PO.rds",
stan_model_path = "./models/MR_cts_PO.stan")
MR_model_cat_PO<- StanModel$new(stan_model_rds_path = "./models/MR_cat_PO.rds",
stan_model_path = "./models/MR_cat_PO.stan")
MR_model_cat_PO_p_scale_priors<- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/MR_cat_PO_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/MR_cat_PO_p_scale_priors.stan")
# MR - call the server module for button
MR_run_PO_prior_model_button_server <- run_prior_model_button_server(id = "MR_prior_model_button_id")
MR_draws_PO <- MR_run_model_priors_only(id = "MR_model_id",
data = data,
stan_model_cts = MR_model_cts_PO,
stan_model_cat = MR_model_cat_PO,
stan_model_cat_PO_p_scale_priors = MR_model_cat_PO_p_scale_priors,
cts_cov_indicator = MR_cts_cov_indicator,
p_scale_priors_indicator = MR_p_scale_priors_indicator,
button = MR_run_PO_prior_model_button_server)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# MR - Full models ------
MR_model_cts <- StanModel$new(stan_model_rds_path = "./models/MR_cts.rds",
stan_model_path = "./models/MR_cts.stan")
MR_model_cat <- StanModel$new(stan_model_rds_path = "./models/MR_cat_v2.rds",
stan_model_path = "./models/MR_cat_v2.stan")
MR_model_cat_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/MR_cat_p_scale_priors_v2.rds",
stan_model_path = "./models/p_scale_priors/MR_cat_p_scale_priors_v2.stan")
# MR - call the server module for button
MR_run_model_button_server <- run_model_button_server(id = "MR_model_button_id")
MR_draws <- MR_run_model(id = "MR_model_id",
stan_model_cts = MR_model_cts,
stan_model_cat = MR_model_cat,
stan_model_cat_p_scale_priors = MR_model_cat_p_scale_priors,
data = data,
cts_cov_indicator = MR_cts_cov_indicator,
p_scale_priors_indicator = MR_p_scale_priors_indicator,
button = MR_run_model_button_server)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# MR - Plot of prior distributions ---------------------------------
MR_model_priors_plot_server(id = "MR_model_id",
draws_PO = MR_draws_PO,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - table for prior distributions -----------------------------
MR_model_priors_table_server(id = "MR_model_id",
draws_PO = MR_draws_PO,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - Table of model diagnostics -----------------------------
sampler_diagnostics_table_server(id = "MR_model_id",
draws = MR_draws,
sampler_options = sampler_options)
# MR - Table of r-hat statistics ---------------------------------------------------------------------------------------------------
MR_rhat_table_server(id = "MR_model_id",
draws = MR_draws,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - Posterior density plots -----------------------------
MR_model_posterior_density_plots_server(id = "MR_model_id",
draws = MR_draws,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - Trace plots -----------------------------
MR_model_trace_plots_server(id = "MR_model_id",
draws = MR_draws,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - Observed data table / study-level outcomes -----------------------
MR_data_table_server(id = "MR_model_id",
draws = MR_draws,
data = data,
cts_cov_indicator = MR_cts_cov_indicator)
# MR - Table of parameter estimates --------------------
MR_parameter_estimates_table_renderUI(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator)
MR_parameter_estimates_table_server(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator,
data = data,
draws = MR_draws)
# MR - sROC plot ---------------------------------------------
covariate_display_server(id = "MR_model_id",
data = data)
HSROC_curve_type_renderUI(id = "MR_model_id")
MR_sroc_plot_server(id = "MR_model_id",
draws = MR_draws,
cts_cov_indicator = MR_cts_cov_indicator,
data = data)
# MR - sROC plot - Javascript code to overlay study info for Meta-regression sROC
output$css_MR_sroc_head <-renderUI({(tags$style('
#MR_model_id-my_tooltip {
position: absolute;
width: 500px;
z-index: 10;
padding: 0;
}
#MR_model_id-vals {
color: white;
background: blue;
font-size: 11px;
}
'))})
output$css_MR_sroc_script <-renderUI({(tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#MR_model_id-plot").mousemove(function(e) {
// put ID of uiOutput below
$("#MR_model_id-my_tooltip").show();
$("#MR_model_id-my_tooltip").css({
top: (e.pageY + 50) + "px",
left: (e.pageX - 1400) + "px"
});
});
});
'))})
# MR - covariate level vs Se/Sp Plots ----------------------------------------------------------
MR_plot_2_renderUI(id = "MR_model_id",
cts_cov_indicator = MR_cts_cov_indicator)
MR_plot_2_server(id = "MR_model_id",
draws = MR_draws,
cts_cov_indicator = MR_cts_cov_indicator,
data = data)
# MR - sROC plot - Javascript code to overlay study info for Meta-regression Se/Sp vs covariate plot
output$css_MR_plot_2_head <-renderUI({(tags$style('
#MR_model_id-my_tooltip_2 {
position: absolute;
width: 500px;
z-index: 10;
padding: 0;
}
#MR_model_id-vals_2 {
color: white;
background: blue;
font-size: 11px;
}
'))})
output$css_MR_plot_2_script <-renderUI({(tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#MR_model_id-plot_2").mousemove(function(e) {
// put ID of uiOutput below
$("#MR_model_id-my_tooltip_2").show();
$("#MR_model_id-my_tooltip_2").css({
top: (e.pageY + 50) + "px",
left: (e.pageX - 1400) + "px"
});
});
});
'))})
# Subgroup analysis (SG) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_covariate_model_server(id = "SG_model_id",
data = data)
SG_priors_options_server(id = "SG_model_id",
data = data)
# SG - server-side module for Stan sampler options UI (to be used w/ shinydashboard)
sampler_options_server(id = "SG_model_id")
sampler_options <- sampler_options_inputModule_server(id = "SG_model_id")
# SG - server functions for progress indicators / loading bars ----------------------------------------------------------------------
progress_prior_model_server(id = "SG_model_id")
progress_main_model_server(id = "SG_model_id")
### Run Subgroup analysis models ---------------------------------------------------------------------------------------------------------------------------------
# indicator for p-scale priors models
SG_p_scale_priors_indicator <- p_scale_priors_indicator_checkbox_out(id = "SG_model_id")
# priors-only model-----
SG_model_PO <- StanModel$new(stan_model_rds_path = "./models/SG_PO.rds",
stan_model_path = "./models/SG_PO.stan")
SG_model_PO_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/SG_PO_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/SG_PO_p_scale_priors.stan")
SG_run_prior_model_button_server <- run_prior_model_button_server(id = "SG_prior_model_button_id")
SG_draws_PO <- SG_run_model_priors_only(id = "SG_model_id",
stan_model = SG_model_PO,
stan_model_p_scale_priors = SG_model_PO_p_scale_priors,
data = data,
p_scale_priors_indicator = SG_p_scale_priors_indicator,
button = SG_run_prior_model_button_server)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# full model --------
SG_model <- StanModel$new(stan_model_rds_path = "./models/SG.rds",
stan_model_path = "./models/SG.stan")
SG_model_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/SG_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/SG_p_scale_priors.stan")
SG_run_model_button_server <- run_model_button_server(id = "SG_model_button_id")
SG_draws <- SG_run_model(id = "SG_model_id",
stan_model = SG_model,
stan_model_p_scale_priors = SG_model_p_scale_priors,
data = data,
p_scale_priors_indicator = SG_p_scale_priors_indicator,
button = SG_run_model_button_server)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# Subgroup analysis - Plot of prior distributions --------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_model_priors_plot_server(id = "SG_model_id",
draws_PO = SG_draws_PO,
data = data)
# Subgroup analysis - table for prior distributions --------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_model_priors_table_server(id = "SG_model_id",
draws_PO = SG_draws_PO,
data = data)
# Subgroup analysis - Table of model diagnostics --------------------------------------------------------------------------------------------------------------------------------------------------------------------
sampler_diagnostics_table_server(id = "SG_model_id",
draws = SG_draws,
sampler_options = sampler_options)
# Subgroup analysis - Table of r-hat statistics ---------------------------------------------------------------------------------------------------
SG_rhat_table_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# Subgroup analysis - Posterior density plots --------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_model_posterior_density_plots_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# Subgroup analysis - Trace plots --------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_model_trace_plots_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# Subgroup analysis - Observed data table / study-level outcomes --------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_data_table_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# Subgroup analysis - Table of parameter estimates -----------------------------------------------------------------------------------------------------------------------------------------------------------
SG_parameter_estimates_table_server(id = "SG_model_id",
data = data,
draws = SG_draws)
# Subgroup analysis - sROC plot ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SG_covariate_display_server(id = "SG_model_id",
data = data)
HSROC_curve_type_renderUI(id = "SG_model_id")
SG_sroc_plot_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# sROC plot - Javascript code to overlay study info for Subgroup sROC plot
output$css_SG_sroc_head <-renderUI({(tags$style('
#SG_model_id-my_tooltip {
position: absolute;
width: 500px;
z-index: 10;
padding: 0;
}
#SG_model_id-vals {
color: white;
background: blue;
font-size: 11px;
}
'))})
output$css_SG_sroc_script <-renderUI({(tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#SG_model_id-plot").mousemove(function(e) {
// put ID of uiOutput below
$("#SG_model_id-my_tooltip").show();
$("#SG_model_id-my_tooltip").css({
top: (e.pageY + 50) + "px",
left: (e.pageX - 1400) + "px"
});
});
});
'))})
# # Meta-regression: covariate level vs Se/Sp Plots ----------------------------------------------------
SG_plot_2_server(id = "SG_model_id",
draws = SG_draws,
data = data)
# sROC plot - Javascript code to overlay study info for Subgroup Se/Sp vs covariate plot
output$css_SG_plot_2_head <-renderUI({(tags$style('
#SG_model_id-my_tooltip_2 {
position: absolute;
width: 500px;
z-index: 10;
padding: 0;
}
#SG_model_id-vals_2 {
color: white;
background: blue;
font-size: 11px;
}
'))})
output$css_SG_plot_2_script <-renderUI({(tags$script('
$(document).ready(function() {
// put ID of the plot below
$("#SG_model_id-plot_2").mousemove(function(e) {
// put ID of uiOutput below
$("#SG_model_id-my_tooltip_2").show();
$("#SG_model_id-my_tooltip_2").css({
top: (e.pageY + 50) + "px",
left: (e.pageX - 1400) + "px"
});
});
});
'))})
# Latent class model (LCM) --------------------------------------------------------------------------------------------------------------------------------------------------------------
# LCM - load sensitivity analysis (SA) study list and data
SA_list_studies_server(id = "SA_LCM_model_id",
data = data)
SA_data <- SA_data_server(id = "SA_LCM_model_id",
data = data)
SA_data_table_server(id = "SA_LCM_model_id",
data = data)
SA_LCM_indicator <- SA_indicator_out(id = "SA_LCM_model_id")
SA_display_conditional_plot_checkbox_renderUI(id = "LCM_model_id",
SA_indicator = SA_LCM_indicator)
# LCM - Forest plots -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
LCM_forest_plots_server(id = "LCM_model_id",
data = data)
# LCM - server-side module for prior options UI (to be used w/ shinydashboard)
LCM_priors_options_inputModule_renderUI_server(id = "LCM_model_id",
data = data)
LCM_priors <- LCM_priors_options_inputModule_server(id = "LCM_model_id",
data = data)
# LCM - server-side module for Stan sampler options UI (to be used w/ shinydashboard)
sampler_options_server(id = "LCM_model_id")
LCM_sampler_options <- sampler_options_inputModule_server(id = "LCM_model_id") # extract sampler options
# LCM - server functions for progress indicators / loading bars ----------------------------------------------------------------------
progress_prior_model_server(id = "LCM_model_id")
progress_main_model_server(id = "LCM_model_id")
progress_main_model_server(id = "SA_LCM_model_id")
# LCM - Run LCM Model - prior-only model -----------------------------------------------------------------------------------------------------------------
LCM_p_scale_priors_indicator <- p_scale_priors_indicator_checkbox_out(id = "LCM_model_id")
#LCM_model_PO <- readRDS(file ='./models/BLCM_ma_PO.rds')
#LCM_model_PO <- stan_model(file ='./models/BLCM_ma_PO.stan')
#LCM_model_PO_p_scale_priors <- readRDS(file = './models/p_scale_priors/BLCM_ma_PO_p_scale_priors.rds')
#LCM_model_PO_p_scale_priors <- stan_model(file = './models/p_scale_priors/BLCM_ma_PO_p_scale_priors.stan')
LCM_model_PO <- StanModel$new(stan_model_rds_path = "./models/BLCM_ma_PO.rds",
stan_model_path = "./models/BLCM_ma_PO.stan")
LCM_model_PO_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/BLCM_ma_PO_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/BLCM_ma_PO_p_scale_priors.stan")
# LCM - call the server module for button
LCM_model_PO_button <- run_prior_model_button_server(id = "LCM_prior_model_button_id")
# LCM - call server module to run stan model and save posterior draws
LCM_draws_PO <- LCM_run_model_priors_only( id = "LCM_model_id",
data = data,
p_scale_priors_indicator = LCM_p_scale_priors_indicator,
priors = LCM_priors,
button = LCM_model_PO_button,
stan_model = LCM_model_PO,
stan_model_p_scale_priors = LCM_model_PO_p_scale_priors)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# LCM - LCM options
LCM_model_options_inputModule_renderUI_server(id = "LCM_model_id")
LCM_options_indicators <- LCM_model_options_inputModule_server(id = "LCM_model_id") # extract options
# LCM - Run LCM Model - full model -----------------------------------------------------------------------------------------------------------------
#LCM_model <- readRDS(file = './models/BLCM_ma.rds')
#LCM_model <- stan_model(file = './models/BLCM_ma.stan')
#LCM_model_p_scale_priors <- readRDS(file = './models/p_scale_priors/BLCM_ma_p_scale_priors.rds')
#LCM_model_p_scale_priors <- stan_model(file = './models/p_scale_priors/BLCM_ma_p_scale_priors.stan')
LCM_model <- StanModel$new(stan_model_rds_path = "./models/BLCM_ma.rds",
stan_model_path = "./models/BLCM_ma.stan")
LCM_model_p_scale_priors <- StanModel$new(stan_model_rds_path = "./models/p_scale_priors/BLCM_ma_p_scale_priors.rds",
stan_model_path = "./models/p_scale_priors/BLCM_ma_p_scale_priors.stan")
# LCM - call the server module for button
LCM_model_button <- run_model_button_server(id = "LCM_model_button_id")
SA_LCM_model_button <- SA_run_model_button_server(id = "SA_LCM_model_button_id")
## LCM - call server module to run stan model and save posterior draws
LCM_draws <- LCM_run_model( id = "LCM_model_id",
button = LCM_model_button,
stan_model = LCM_model,
stan_model_p_scale_priors = LCM_model_p_scale_priors,
dataset = data,
p_scale_priors_indicator = LCM_p_scale_priors_indicator,
priors = LCM_priors,
sampler_options = LCM_sampler_options,
SA_indicator = SA_LCM_indicator,
SA_indicator_local = 0,
LCM_options_indicators = LCM_options_indicators)$draws
LCM_SA_draws <- LCM_run_model( id = "SA_LCM_model_id",
button = SA_LCM_model_button,
stan_model = LCM_model,
stan_model_p_scale_priors = LCM_model_p_scale_priors,
dataset = data,
p_scale_priors_indicator = LCM_p_scale_priors_indicator,
priors = LCM_priors,
sampler_options = LCM_sampler_options,
SA_indicator = SA_LCM_indicator,
SA_indicator_local = 1,
LCM_options_indicators = LCM_options_indicators)$draws
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
# LCM - Table of model diagnostics ---------------------------------------------------------------------------------------------------
LCM_model_diagnostics_tab_renderUI_server(id = "LCM_model_id",
SA_indicator = SA_LCM_indicator)
sampler_diagnostics_table_server(id = "LCM_model_id",
draws = LCM_draws,
sampler_options = LCM_sampler_options)
# SA
sampler_diagnostics_table_server(id = "SA_LCM_model_id",
draws = LCM_SA_draws,
sampler_options = LCM_sampler_options)
# LCM - Table of r-hat statistics ---------------------------------------------------------------------------------------------------