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m_SG_p1.R
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# Model diagnostics tab - UI ("master") module ---------------------------------- ---------------------------------------
SG_model_diagnostics_tab_UI <- function(id) {
ns <- NS(id)
tagList(
sampler_diagnostics_table_UI(id = "SG_model_id"),
br(),
h3("R-hat statistics"),
SG_rhat_table_UI(id = "SG_model_id"),
br(),
h3("Posterior density plots"),
dropdownButton(
model_posterior_density_plots_settings_UI(id = "SG_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
model_posterior_density_plots_UI(id = "SG_model_id"),
br(),
h3("Trace plots"),
dropdownButton(
SG_model_trace_plots_settings_UI(id = "SG_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
SG_model_trace_plots_UI(id = "SG_model_id")
)
}
# UI function to select subgroup
SG_input_UI <- function(id) {
ns <- NS(id)
uiOutput(ns("covariate_subgroup_ui"))
}
# covariate to use for SG analysis
SG_covariate_model_server <- function(id,
data) {
moduleServer(
id,
function(input, output, session) {
X <- reactive({req(data())})
C <- reactive({ncol(X())})
Names <- reactive({colnames(X())})
j <- reactive({
if (C() > 8 & Names()[7] != "rob_PS") {
j <- 6
} else {
j <- 13
}
return(j)
})
choicesCov_obj <- reactive({
initial <- c("None")
if (( C() > 8 & Names()[7] != "rob_PS") |
( C() > 15 & Names()[13] == "ac_RS")) {
if (C() > 8 & Names()[7] != "rob_PS") {
if (C() == 9) {
covariates <- colnames(X()[7])
}
else {
covariates <- colnames(X()[,7:C()])
}
}
if (C() > 15 & Names()[13] == "ac_RS") {
if (C() == 16) {
covariates <- colnames(X()[14])
}
else {
covariates <- colnames(X()[,14:C()])
}
}
combined <- c(initial, covariates)
number <- 1:length(combined)
choicesCov <- setNames(number, combined)
choicesCov
}
else { }
})
output$covariate_subgroup_ui <- renderUI({
validate(
need(j() %in% c(6,13), "Please select a dataset with covariates to use meta-regression")
)
ns <- session$ns
selectInput(inputId = ns("covariate_subgroup"),
label= "Please select subgroup",
choices = choicesCov_obj(),
selected = 2)
})
output$progress_prior_model_ui <- renderUI({
ns <- session$ns
verbatimTextOutput(ns("progress_prior_model"))
})
output$progress_main_model_ui <- renderUI({
ns <- session$ns
verbatimTextOutput(ns("progress_main_model"))
})
}
)
}
# prior distribution options ------------------------------------------------- ----------------------------------------
SG_priors_options_UI <- function(id) {
ns <- NS(id)
uiOutput(ns("priors_options"))
}
# server-side function to input prior dist. options
SG_priors_options_server <- function(id, data) {
moduleServer(
id,
function(input, output, session) {
X <- reactive({req(data())})
C <-reactive({ ncol(X()) })
Names <- reactive({ colnames(X()) })
priors_options_toggle_obj <- reactive({
req(data(), input$covariate_subgroup)
if (C() > 8 & Names()[7] != "rob_PS") {
j <<- 6
} else {
j <<- 13
}
if ( (str_sub(colnames(X())[j + as.integer(as.double(input$covariate_subgroup)) - 1 ], start = -3) != 'cts') &&
is.null(input$covcheck_model) != FALSE ) {
cov_cts_indicator <<- 0
}
else {
cov_cts_indicator <<- 1
}
return(cov_cts_indicator)
})
reset_obj <- reactive({
input$reset
})
output$priors_options <- renderUI({
ns <- session$ns
if (( C() > 8 & Names()[7] != "rob_PS") | (C() > 15 & Names()[13] == "ac_RS")) {
if (C() > 8 & Names()[7] != "rob_PS") {
j <<- 6
} else {
j <<- 13
}
} else {
j <<- 0
}
validate(
need( (j %in% c(6,13) ) ,
"To use subgroup analysis, please select a dataset which contains categorical or discrete covariates")
)
validate(
need( priors_options_toggle_obj() == 0 ,
"To use subgroup analysis, please select a categorical covariate")
)
reset <- reset_obj()
div(id = ns("priors_options_div"),
if (input$p_scale_priors_indicator == FALSE) {
tagList(
h4("Prior Distributions (For each subgroup):"),
numericInput(inputId = ns("SG_prior_mean_sens_mu"), label=h5("Prior mean of pooled sensitivities - mean"), value = 0),
numericInput(inputId = ns("SG_prior_mean_sens_sd"), label=h5("Prior mean of pooled sensitivities - SD"), value = 1.5),
numericInput(inputId = ns("SG_prior_mean_spec_mu"), label=h5("Prior mean of pooled specificities - mean"), value = 0),
numericInput(inputId = ns("SG_prior_mean_spec_sd"), label=h5("Prior mean of pooled specificities - SD"), value = 1.5),
numericInput(inputId = ns("SG_prior_SD_sens_sd"), label=h5("Prior for between-study SD of logit(sensitivity) - SD"), value = 1),
numericInput(inputId = ns("SG_prior_SD_spec_sd"), label=h5("Prior for between-study SD of logit(specificity) - SD"), value = 1)
)
}
else {
tagList(
h4("Prior Distributions (For each subgroup):"),
h5("Sensitivity - 95% credible interval:"),
numericInputRow(inputId = ns("SG_prior_sens_lower95"), label=h5("Lower interval (2.5th percentile)"), value = 0.05, min = 0, max = 1),
numericInputRow(inputId = ns("SG_prior_sens_upper95"), label=h5("Upper interval (97.5th percentile)"), value = 0.95, min = 0, max = 1),
h5("Specificity - 95% credible interval:"),
numericInputRow(inputId = ns("SG_prior_spec_lower95"), label=h5("Lower interval (2.5th percentile)"), value = 0.05, min = 0, max = 1),
numericInputRow(inputId = ns("SG_prior_spec_upper95"), label=h5("Upper interval (97.5th percentile)"), value = 0.95, min = 0, max = 1),
h5("Standard deviation (SD) priors:"),
numericInput(inputId = ns("SG_prior_SD_sens_sd"), label=h5("Prior for between-study SD of logit(sensitivity) - SD"), value = 1),
numericInput(inputId = ns("SG_prior_SD_spec_sd"), label=h5("Prior for between-study SD of logit(specificity) - SD"), value = 1)
)
}
)
})
observeEvent(input$reset,{
shinyjs::reset("priors_options_div")
})
outputOptions(output, "priors_options", suspendWhenHidden = FALSE)
}
)
}
# Plot of prior distributions --------------------------------------------------- ---------------------------------------
# server function
SG_model_priors_plot_server <- function(id, draws_PO, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
priors_plot_obj <- reactive({ # can just refer to the plot name ("model_priors_plot") without using "ns" here in the server function.
req(data(), draws_PO(), input$covariate_subgroup, cancelOutput = TRUE)
params <- rstan::extract(draws_PO())
X <- data()
N <- nrow(X)
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
n_samps <- (2000 - 200)*4
if (cov_index == 0) {
print("Please select a covariate")
} else {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical / discrete meta-reg
se <- params$Se[, 1] # chose 1st group (since priors are the same for each subgroup / discrete covariate)
sp <- params$Sp[, 1] # chose 1st group (since priors are the same for each subgroup / discrete covariate)
corr <- params$Omega[, 1 , 1, 2]
sd_se <- params$sigma[, 1, 1]
sd_sp <- params$sigma[, 1, 2]
data <- tibble(
Samples = c(se, sp, corr, sd_se, sd_sp),
Parameter = c(rep("Sensitivity (for each subgroup)", n_samps),
rep("Specificity (for each subgroup)", n_samps),
rep("Between-study correlation (for each subgroup)", n_samps),
rep("Between-study SD for logit(sensitivity) (for each subgroup)", n_samps),
rep("Between-study SD for logit(specificity) (for each subgroup)", n_samps)))
g <- ggplot(data = data, aes(x=Samples)) +
geom_density(alpha = 0.50) +
theme_bw() +
theme(legend.position = "none") +
facet_wrap( ~ Parameter, scales = "free") +
xlab(" ") +
ylab(" ")
g
}
}
})
C <- reactive({
ncol(data())
})
Names <- reactive({
colnames(data())
})
priors_options_toggle_obj <- reactive({
req(data(), input$covariate_subgroup)
if (C() > 8 & Names()[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
if ( (str_sub(colnames(X())[j + as.integer(as.double(input$covariate_subgroup)) - 1 ], start = -3) != 'cts') &&
is.null(input$covcheck_model) != FALSE ) {
cov_cts_indicator <<- 0
}
else {
cov_cts_indicator <<- 1
}
return(cov_cts_indicator)
})
# output plot
observe({
output$model_priors_plot <- renderPlot({
if (( C() > 8 & Names()[7] != "rob_PS") | (C() > 15 & Names()[13] == "ac_RS")) {
if (C() > 8 & Names()[7] != "rob_PS") {
j <<- 6
} else {
j <<- 13
}
}
else {
j <<- 0
}
validate(
need(!(is.null(draws_PO())), "Please run prior model to display plot")
)
validate(
need( (j %in% c(6,13)),
"To use subgroup analysis, please select a dataset which contains categorical or discrete covariates")
)
validate(
need( priors_options_toggle_obj() == 0,
"To use subgroup analysis, please select a categorical covariate")
)
req(data(), draws_PO(), cancelOutput = TRUE)
priors_plot_obj()
},
height = input$model_priors_plot_dimension_slider_height,
width = input$model_priors_plot_dimension_slider_width)
})
# Download ggplot object
output$download_priors_plot <- downloadHandler(
filename = function(){
paste("priors_plot.png")
},
content = function(file) {
{ggsave(file,
priors_plot_obj(),
width = input$priors_plot_dl_width,
height = input$priors_plot_dl_height,
dpi = input$priors_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# Table of prior distributions ---------------------------------------------------------------- -------------------------------
# UI function
SG_model_priors_table_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(outputId = ns("model_priors_table")),
p("NOTE: The default priors are:"),
p("For logit sensitivities and specificities - normal distribution with mean 0 and SD of 1, equivalent to a 95% prior interval of (0.05, 0.95) on the probability scale"),
p("For between-study SD's of logit sensitivities and specificities - truncated (at 0) normal distribution with mean 0 and SD of 1, equivalent to a 95% prior interval of (0.03, 2.25) "),
p("For between-study correlation between study-specific logit sensitivities and specificities - LKJ(2) prior, equivalent to 95% prior interval of (-0.8, 0.8)")
)
}
# server function
SG_model_priors_table_server <- function(id, draws_PO, data) {
moduleServer(
id,
function(input, output, session) {
C <- reactive({
ncol(data())
})
Names <- reactive({
colnames(data())
})
priors_options_toggle_obj <- reactive({
req(data(), input$covariate_subgroup)
if (C() > 8 & Names()[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
if ( (str_sub(colnames(X())[j + as.integer(as.double(input$covariate_subgroup)) - 1 ], start = -3) != 'cts') &&
is.null(input$covcheck_model) != FALSE ) {
cov_cts_indicator <<- 0
}
else {
cov_cts_indicator <<- 1
}
return(cov_cts_indicator)
})
output$model_priors_table <- renderTable({
validate(
need(!(is.null(draws_PO())), "Please run prior model to display table")
)
validate(
need( (j %in% c(6,13) ) ,
"To use subgroup analysis, please select a dataset which contains categorical or discrete covariates")
)
validate(
need( priors_options_toggle_obj() == 0 ,
"To use subgroup analysis, please select a categorical covariate")
)
if (( C() > 8 & Names()[7] != "rob_PS") | (C() > 15 & Names()[13] == "ac_RS")) {
if (C() > 8 & Names()[7] != "rob_PS") {
j <<- 6
} else {
j <<- 13
}
}
else {
j <<- 0
}
req(data(), draws_PO(), cancelOutput = TRUE)
mod <- draws_PO()
X <- data()
N <- nrow(X)
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
num_levels <- length(rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
if (cov_index == 0) {
print("Please select a covariate")
} else {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical only
Sens <- rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary
Spec <- rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary
correlation <- rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega"))$summary
between_study_sd <- rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary
Z = data.frame(estimate = c(Sens[, "50%"][1], Spec[, "50%"][1],
1 - Spec[, "50%"][1],
correlation[, "50%"][3],
between_study_sd[, "50%"][1], between_study_sd[, "50%"][2]),
sd = c(Sens[, "sd"][1], Spec[, "sd"][1],
Spec[, "sd"][1],
correlation[, "sd"][3],
between_study_sd[, "sd"][1], between_study_sd[, "sd"][2]),
lci = c(Sens[, "2.5%"][1], Spec[, "2.5%"][1],
1 - Spec[, "97.5%"][1],
correlation[, "2.5%"][3],
between_study_sd[, "2.5%"][1], between_study_sd[, "2.5%"][2]),
uci = c(Sens[, "97.5%"][1], Spec[, "97.5%"][1],
1 - Spec[, "2.5%"][1],
correlation[, "97.5%"][3],
between_study_sd[, "97.5%"][1], between_study_sd[, "97.5%"][2]),
row.names=c("Sensitivity", "Specifcity",
"FPR",
"Correlation",
"sd_sens", "sd_spec")
)
# Create a matrix to store the parameter estimates
nrow <- 7
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- paste0("Sensitivity [same prior across subgroups]", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>1</sub>"), ")", ")")
s.matrix[2,1] <- paste0("Specificity [same prior across subgroups]", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>0</sub>"), ")", ")")
s.matrix[3,1] <- "False positive rate (1 - specificity) [same prior across subgroups]"
s.matrix[4,1] <- paste0("Between-study Correlation [same prior across subgroups] " , "( ", HTML("ρ"), " )")
s.matrix[5,1] <- paste0("Between-study SD for logit(Sensitivity) [same prior across subgroups] ", "( ", HTML("σ<sub>1</sub>"), " )")
s.matrix[6,1] <- paste0("Between-study SD for logit(Specificity) [same prior across subgroups] ", "( ", HTML("σ<sub>0</sub>"), " )")
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3, 6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Prior Median", "Standard Deviation", "95% Prior Interval")
s.matrix
}
}
}, sanitize.text.function = function(x) x)
}
)
}
# Trace plots ------------------------------------------- ---------------------------------------------------------------------------------------------
SG_model_trace_plots_UI <- function(id) {
ns <- NS(id)
plotOutput(outputId = ns("trace_plots"))
}
SG_model_trace_plots_settings_UI <- function(id) {
ns <- NS(id)
tagList(
numericInput(ns("trace_plot_nrow"),
label = "Number of rows",
value = 10),
numericInput(ns("trace_plot_ncol"),
label = "Number of columns",
value = 10),
sliderInput(inputId = ns("trace_plot_dimention_slider_height"),
label = "Change size of plot - height",
min = 1, max = 2000,
value = 300),
sliderInput(inputId = ns("trace_plot_dimention_slider_width"),
label = "Change size of plot - width",
min = 1, max = 2000,
value = 500),
# Download plot:
h5("Download plot:"),
numericInput(inputId = ns("trace_plot_dl_width"), label=h5("Plot width"), value = 10),
numericInput(inputId = ns("trace_plot_dl_height"), label=h5("Plot height"), value = 5),
numericInput(inputId = ns("trace_plot_dl_dpi"), label=h5("Plot DPI"), value = 600),
downloadButton(outputId = ns("download_trace_plot"), label = "Download Plot")
)
}
SG_model_trace_plots_server <- function(id, draws, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
trace_plots_obj <- reactive({
req(data(), draws(), input$covariate_subgroup, cancelOutput = TRUE)
X <- data()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
mod <- draws()
if (cov_index == 0) {
print("Please select a covariate")
}
if (cov_index != 0) {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical / discrete meta-reg
plot <- stan_trace(mod,
nrow = input$trace_plot_nrow,
ncol = input$trace_plot_ncol,
pars = c("Se", "Sp", "Sigma", "se", "sp"))
}
else {
}
}
plot
})
observe({
output$trace_plots <- renderPlot({
trace_plots_obj()
}, height = input$trace_plot_dimention_slider_height,
width = input$trace_plot_dimention_slider_width)
})
# Download ggplot object
output$download_trace_plot <- downloadHandler(
filename = function(){
paste("trace_plot.png")
},
content = function(file) {
{ggsave(file, trace_plots_obj(),
width = input$trace_plot_dl_width,
height = input$trace_plot_dl_height,
dpi = input$trace_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# Posterior density plots ------------------------------------------------------------- -----------------------------------------------------------------
SG_model_posterior_density_plots_UI <- function(id) {
ns <- NS(id)
plotOutput(outputId = ns("posterior_density_plots"))
}
SG_model_posterior_density_plots_settings_UI <- function(id) {
ns <- NS(id)
tagList(
numericInput(ns("posterior_density_plot_nrow"),
label = "Number of rows",
value = 10),
numericInput(ns("posterior_density_plot_ncol"),
label = "Number of columns",
value = 10),
sliderInput(inputId = ns("posterior_density_plot_dimention_slider_height"),
label = "Change size of plot - height",
min = 1, max = 2000,
value = 300,
ticks = FALSE),
sliderInput(inputId = ns("posterior_density_plot_dimention_slider_width"),
label = "Change size of plot - width",
min = 1, max = 2000,
value = 500,
ticks = FALSE),
# Download plot:
h5("Download plot:"),
numericInput(inputId = ns("dens_plot_dl_width"), label=h5("Plot width"), value = 10),
numericInput(inputId = ns("dens_plot_dl_height"), label=h5("Plot height"), value = 5),
numericInput(inputId = ns("dens_plot_dl_dpi"), label=h5("Plot DPI"), value = 600),
downloadButton(outputId = ns("download_dens_plot"), label = "Download Plot")
)
}
SG_model_posterior_density_plots_server <- function(id, draws, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
posterior_density_plots_obj <- reactive({
req(data(), draws(), input$covariate_subgroup, cancelOutput = TRUE)
X <- data()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
mod <- draws()
if (cov_index == 0) {
print("Please select a covariate")
}
if (cov_index != 0) {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical / discrete meta-reg
plot <- stan_dens(mod,
nrow = input$posterior_density_plot_nrow,
ncol = input$posterior_density_plot_ncol,
pars = c("Se", "Sp", "Sigma", "se", "sp"))
}
else { }
}
return(plot)
})
observe({
output$posterior_density_plots <- renderPlot({
posterior_density_plots_obj()
}, height = as.numeric(input$posterior_density_plot_dimension_slider_height),
width = as.numeric(input$posterior_density_plot_dimension_slider_width))
})
# Download ggplot object
output$download_dens_plot <- downloadHandler(
filename = function(){
paste("posterior_density_plot.png")
},
content = function(file) {
{ggsave(file, posterior_density_plots_obj(),
width = input$dens_plot_dl_width,
height = input$dens_plot_dl_height,
dpi = input$dens_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# Table which displays observed data and study weights from model (study-level outcomes) ------------------- -----------------------------------------
SG_data_table_UI <- function(id) {
ns <- NS(id)
DT::dataTableOutput(outputId = ns("data_table"))
}
SG_data_table_settings_UI <- function(id) {
ns <- NS(id)
downloadButton(outputId = ns("data_table_download"), label = "Download Table")
}
SG_data_table_server <- function(id, data, draws) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
data_table_obj <- reactive({
X <- data()
mod <- draws()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
N <- nrow(X)
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
if (cov_index == 0) {
print("Please select a covariate")
}
if (cov_index != 0) {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical / discrete meta-reg
num_levels <- length(rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
# add study weights
X <- SG_weights(X, mod, cov_index)
# observed values
ss <- tibble(
Study =as.numeric(as.factor(X$author)),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index]),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
prev = round((TP+FN)/N, 2),
pctse = X$pctse$pctse,
pctsp = X$pctsp$pctsp
)
ss2 <- left_join(ss, X, by = c("TP", "FN", "FP", "TN"))
no_covariates <- length(colnames(X[,(j+1):C])) # total number of covariates
# make cols without .cts and .cat endings for display
for (i in 1:no_covariates) {
ss2 <- dplyr::mutate(ss2,
!!as.name( str_sub(colnames(X)[j + i], end = -5) ) := !!as.name( colnames(X)[j + i]) )
}
ss2 <- dplyr::mutate(ss2,
Weight_Sens = round(pctse.y$pctse, 2),
Weight_Spec = round(pctsp.y$pctsp, 2),
Sensitivity = round(Sensitivity, 2),
Specificity = round(Specificity, 2))
# select columns to display
cols <- c("Study", "author", "year", str_sub( colnames(X)[c((j+1):(j+no_covariates))] , end = -5),
"Sensitivity", "Specificity", "Weight_Sens", "Weight_Spec")
# arrange data by the selected covariate
data <- dplyr::select(ss2, cols) %>%
dplyr::arrange( !!as.name( str_sub(colnames(X)[j + cov_index], end = -5) ) ) %>%
dplyr::mutate(Weight_Sens = ifelse(Weight_Sens == 100, "-", Weight_Sens),
Weight_Spec = ifelse(Weight_Spec == 100, "-", Weight_Spec))
data <- data.frame(data)
return(data)
}
else { }
}
})
output$data_table <- DT::renderDataTable({
options(DT.options = list(pageLength = 30,
autoWidth = TRUE,
scrollX=T))
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(DT::datatable( data_table_obj() ))
})
output$data_table_download <- downloadHandler(
filename = function(){
paste("table.csv")
},
content = function(file) {
write.csv(data_table_obj(), file, sep=",", row.names=FALSE)
}
)
}
)
}
# Parameter estimates table --------------------------------------------------- ---------------------------------------------------------
# UI function for parameter estimates table
SG_parameter_estimates_table_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(ns("table")),
downloadButton(ns("download_table"), "Download Table")
)
}
# Server function for parameter estimates tables
SG_parameter_estimates_table_server <- function(id,
data,
draws) {
moduleServer(
id,
function(input, output, session) {
table_obj <- reactive({
req(data(), draws(), input$covariate_subgroup, cancelOutput = TRUE)
mod <- draws()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
X <- data()
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { m <<- 6 } else { m <<- 13 }
num_levels <- length(rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
if (cov_index != 0) {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # cat only
logit_Sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSe"))$summary
logit_Spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSp"))$summary
Sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary
Spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary
DOR <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("DOR"))$summary
LRp <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("LRp"))$summary
LRn <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("LRn"))$summary
Theta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("theta"))$summary
Lambda <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lambda"))$summary
correlation <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega"))$summary
beta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("beta"))$summary
sigma2_theta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_sq_theta"))$summary
sigma2_alpha <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_sq_alpha"))$summary
between_study_sd <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary
# identify subgroups w/ N = 1
X2 <- dplyr::mutate(X, numeric_cov = as.numeric(as.factor(!!as.name(colnames(X)[m + cov_index]))))
X_counts <- X2 %>%
group_by(numeric_cov) %>%
summarise(no_rows = length(numeric_cov))
X_counts_one <- filter(X_counts, no_rows != 1)
X_counts_one_df <- data.frame(X_counts_one)
vec <- X_counts_one_df[,1] # vector of factor levels s.t. N > 1
Z_group <- list()
# make vector which indicates the groups s.t. #(studies in group) > 1
for (i in vec) {
Z_group[[i]] <- data.frame(estimate = c(logit_Sens[, "50%"][i],
logit_Spec[, "50%"][i],
Sens[, "50%"][i],
Spec[, "50%"][i],
1 - Spec[, "50%"][i],
DOR[, "50%"][i],
LRp[, "50%"][i],
LRn[, "50%"][i],
correlation[, "50%"][2+4*(i-1)],
Theta[, "50%"][i],
Lambda[, "50%"][i],
beta[, "50%"][i],
sigma2_theta[, "50%"][i],
sigma2_alpha[, "50%"][i],
between_study_sd[, "50%"][1 + 2*(i-1)],
between_study_sd[, "50%"][2 + 2*(i-1)]),
sd = c(logit_Sens[, "sd"][i],
logit_Spec[, "sd"][i],
Sens[, "sd"][i],
Spec[, "sd"][i],
1 - Spec[, "sd"][i],
DOR[, "sd"][i],
LRp[, "sd"][i],
LRn[, "sd"][i],
correlation[, "sd"][2+4*(i-1)],
Theta[, "sd"][i],
Lambda[, "sd"][i],
beta[, "sd"][i],
sigma2_theta[, "sd"][i],
sigma2_alpha[, "sd"][i],
between_study_sd[, "sd"][1 + 2*(i-1)],
between_study_sd[, "sd"][2 + 2*(i-1)]),
lci = c(logit_Sens[, "2.5%"][i],
logit_Spec[, "2.5%"][i],
Sens[, "2.5%"][i],
Spec[, "2.5%"][i],
1 - Spec[, "2.5%"][i],
DOR[, "2.5%"][i],
LRp[, "2.5%"][i],
LRn[, "2.5%"][i],
correlation[, "2.5%"][2+4*(i-1)],
Theta[, "2.5%"][i],
Lambda[, "2.5%"][i],
beta[, "2.5%"][i],
sigma2_theta[, "2.5%"][i],
sigma2_alpha[, "2.5%"][i],
between_study_sd[, "2.5%"][1 + 2*(i-1)],
between_study_sd[, "2.5%"][2 + 2*(i-1)]),
uci = c(logit_Sens[, "97.5%"][i],