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m_LCM_p1_v2.R
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# LCM input / model options ------------------------------------------------- ------------------------------------------------
LCM_model_options_UI <- function(id) {
ns <- NS(id)
tagList(
uiOutput(ns("lcm_model_options"))
)
}
# server-side function to input prior dist. options - outputs a list of inputs which can be called within the shiny program
LCM_model_options_inputModule_renderUI_server <- function(id) {
moduleServer(
id,
function(input, output, session) {
output$lcm_model_options <- renderUI({
ns <- session$ns
div(id = session$ns("lcm_model_options_div"),
tagList(
textInput(inputId = ns("index_test_name"),
label = "Enter name of Index test here",
value = "Index"),
h4("Model options:"),
awesomeCheckbox(inputId = ns("LCM_conditional_independence_indicator"), "Assume conditional independence between tests?", FALSE),
awesomeCheckbox(inputId = ns("LCM_SeR_fixed_indicator"), "Assume reference test sensitivity fixed between studies?", TRUE),
awesomeCheckbox(inputId = ns("LCM_SpR_fixed_indicator"), "Assume reference test specificity fixed between studies?", TRUE),
awesomeCheckbox(inputId = ns("LCM_SeI_fixed_indicator"), "Assume index test sensitivity fixed between studies?", FALSE),
awesomeCheckbox(inputId = ns("LCM_SpI_fixed_indicator"), "Assume index test specificity fixed between studies?", FALSE),
h4("Initial values:"),
numericInput(ns("LCM_index_logit_mu_se"), "initial value for mean of logit(Se) for index test",
value = 2),
numericInput(ns("LCM_index_logit_mu_sp"), "initial value for mean of logit(Sp) for index test",
value = 2)
)
)
})
observeEvent(input$reset, {
shinyjs::reset("lcm_model_options_div")
})
outputOptions(output, "lcm_model_options", suspendWhenHidden = FALSE)
}
)
}
LCM_model_options_inputModule_server <- function(id) {
moduleServer(
id,
function(input, output, session) {
vals <- reactiveValues()
observe({vals$LCM_conditional_independence_indicator <- input$LCM_conditional_independence_indicator})
observe({vals$LCM_SeR_fixed_indicator <- input$LCM_SeR_fixed_indicator})
observe({vals$LCM_SpR_fixed_indicator <- input$LCM_SpR_fixed_indicator})
observe({vals$LCM_SeI_fixed_indicator <- input$LCM_SeI_fixed_indicator})
observe({vals$LCM_SpI_fixed_indicator <- input$LCM_SpI_fixed_indicator})
# observe({vals$LCM_custom_inits_indicator <- input$LCM_custom_inits_indicator})
# initial values
observe({
vals$LCM_index_logit_mu_se <- input$LCM_index_logit_mu_se
vals$LCM_index_logit_mu_sp <- input$LCM_index_logit_mu_sp
})
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(vals)
}
)
}
# Correlation residual plot -------------------------------------------------- ----------------------------------------------
LCM_correlation_residual_plot_UI <- function(id) {
ns <- NS(id)
plotOutput(ns("corr_plot"))
}
LCM_correlation_residual_plot_settings_UI <- function(id) {
ns <- NS(id)
tagList(
sliderInput(inputId = ns("correlation_residual_plot_dimension_slider_height"),
label = "Change size of plot - height",
min = 1,
max = 2000,
value = 300,
ticks = FALSE),
sliderInput(inputId = ns("correlation_residual_plot_dimension_slider_width"),
label = "Change size of plot - width",
min = 1,
max = 2000,
value = 500,
ticks = FALSE),
downloadButton(outputId = ns("download_corr_plot"),
label = "Download Plot")
)
}
LCM_correlation_residual_plot_server <- function(id,
draws) {
moduleServer(
id,
function(input, output, session) {
corr_plot_obj <- reactive({
mod <- draws()
# plot
dc <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dc"))$summary[,5],3)
dc_l <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dc"))$summary[,4],3)
dc_u <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dc"))$summary[,6],3)
dc_data <- tibble(dc, dc_l, dc_u, obs = seq(1, length(dc), by = 1))
cutoff <- data.frame( x = c(-Inf, Inf), y = 0, cutoff = factor(0) )
g <- ggplot(data = dc_data, aes(y = dc, x=obs)) + geom_point(size = 3) +
geom_errorbar(aes(ymin=dc_l, ymax=dc_u), width= 0.75, position=position_dodge(.9)) +
geom_hline(yintercept = 0) +
theme_bw() +
# ylim(-0.31, 0.31) +
ylab("Correlation Residuals") +
xlab("Study") +
theme(text = element_text(size=14),
axis.text.x = element_text())
g
})
# output plot
observe({
output$corr_plot <- renderPlot({
req(data(), draws(), cancelOutput = TRUE)
corr_plot_obj()
},
height = input$correlation_residual_plot_dimension_slider_height,
width = input$correlation_residual_plot_dimension_slider_width)
})
# Download ggplot object
output$download_corr_plot <- downloadHandler(
filename = function(){
paste("plot.png")
},
content = function(file) {
{ggsave(file, corr_plot_obj())}
}
)
}
)
}
# table count residual plot -------------------------------------------------- ----------------------------------------------
LCM_table_prob_residual_plot_UI <- function(id) {
ns <- NS(id)
plotOutput(ns("table_prob_resid_plot"))
}
LCM_table_prob_residual_plot_settings_UI <- function(id) {
ns <- NS(id)
tagList(
sliderInput(inputId = ns("table_prob_resid_plot_dimension_slider_height"),
label = "Change size of plot - height",
min = 1,
max = 2000,
value = 300,
ticks = FALSE),
sliderInput(inputId = ns("table_prob_resid_plot_dimension_slider_width"),
label = "Change size of plot - width",
min = 1,
max = 2000,
value = 500,
ticks = FALSE),
downloadButton(outputId = ns("download_table_prob_resid_plot"),
label = "Download Plot")
)
}
LCM_table_prob_resid_plot_server <- function(id,
draws) {
moduleServer(
id,
function(input, output, session) {
table_prob_resid_plot_obj <- reactive({
mod <- draws()
# plot
dt <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dt"))$summary[,5],3)
dt_l <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dt"))$summary[,4],3)
dt_u <- round(summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("dt"))$summary[,6],3)
dt_data <- tibble(dt, dt_l, dt_u, obs = seq(1, length(dt), by = 1), cell = rep(c(1:4), length(dt)/4))
cutoff <- data.frame( x = c(-Inf, Inf), y = 0, cutoff = factor(0) )
g <- ggplot(data = dt_data, aes(y = dt, x=obs)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin=dt_l, ymax=dt_u), width= 0.75, position=position_dodge(.9)) +
geom_hline(yintercept = 0) +
theme_bw() +
# ylim(-0.31, 0.31) +
ylab("Table Probability Residuals") +
xlab("Study") +
theme(text = element_text(size=14),
axis.text.x = element_text()) +
facet_wrap(~ cell)
g
})
# output plot
observe({
output$table_prob_resid_plot <- renderPlot({
req(data(), draws(), cancelOutput = TRUE)
table_prob_resid_plot_obj()
},
height = input$table_prob_resid_plot_dimension_slider_height,
width = input$table_prob_resid_plot_dimension_slider_width)
})
# Download ggplot object
output$download_table_prob_resid_plot <- downloadHandler(
filename = function(){
paste("plot.png")
},
content = function(file) {
{ggsave(file, table_prob_resid_plot_obj())}
}
)
}
)
}
# Forest Plots (for estimated data as assuming IGS) ------------------------- -----------------------------------------------------
LCM_forest_plots_UI <- function(id) {
ns <- NS(id)
tagList(
fluidRow(
splitLayout(cellWidths = c("50%", "50%"), #
plotOutput(ns("forest_plot_sens"))
,plotOutput(ns("forest_plot_spec")))
),
radioButtons(ns("filetype_forest"),
label="Select plot format",
choices=list("png", "PDF")),
downloadButton(ns("download_forestLCM_sens"), "Download Sensitivity Forest Plot"),
downloadButton(ns("download_forestLCM_spec"), "Download Specificity Forest Plot")
)
}
LCM_forest_plots_server <- function(id, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
### forest plot for sens ----------------------------------
forest_plot_sens_obj <- function(){
X <- data()
D <- mada::madad(X, correction.control = "any")
forest(D,
type = "sens",
snames = X$author,
xlab = "Sensitivity",
main = "Forest plot of sensitivity")
}
# Output plot object
output$forest_plot_sens <- renderPlot({
forest_plot_sens_obj()
})
# Download plot object
output$forest_plot_sens_download <- downloadHandler(
filename = function() {
paste("Sensitivity_forest_plot",
input$filetype_forest,
sep=".")
},
content = function(file) {
png(file)
if (input$filetype_forest == "png") {
png(file)
} else {
pdf(file)
}
forest_plot_sens_obj()
dev.off()
}
)
### forest plot for spec ------------------------------------------------------ -----------------------------------------------------
forest_plot_spec_obj <- reactive({
X <- data()
D <- madad(X, correction.control = "any")
plot <- forest(D, type = "spec", snames = X$author, xlab = "Specificity", main = "Forest plot of specificity")
plot
})
# Output plot object
output$forest_plot_spec <- renderPlot({
forest_plot_spec_obj()
})
# Download plot object
output$forest_plot_spec_download <- downloadHandler(
filename = function(){
paste("Specificity Forest Plot", input$filetype_forest, sep=".")
},
content = function(file) {
if (input$filetype_forest == "png") {
png(file)
} else {
pdf(file)
}
forest_plot_spec_obj()
# dev.off()
}
)
}
)
}
# LCM MA - Plot of prior distributions -------------------------------------- -------------------------------------------------------
LCM_model_priors_plot_server <- function(id,
draws,
data) {
moduleServer(
id,
function(input, output, session) {
priors_plot_obj <- reactive({
req(draws(), X()$reference.cat, cancelOutput = TRUE)
X <- data()
mod <- draws()
params <- rstan::extract(draws())
num_refs <- ncol(params$Se_ref)
refs_names <- c(levels(factor(X$reference.cat)))
se_ref <- list()
sp_ref <- list()
# extract summary-level params
for (i in 1:num_refs) {
se_ref[[i]] <- data.frame(params$Se_ref[,i])
sp_ref[[i]] <- data.frame(params$Sp_ref[,i])
}
se_refs <- rlist::list.cbind(se_ref)
colnames(se_refs) <- paste0("Se", ", ", refs_names)
sp_refs <- rlist::list.cbind(sp_ref)
colnames(sp_refs) <- paste0("Sp", ", ", refs_names)
corr_ref <- tibble(!!as.name(paste0("Correlation", ", ", "Reference tests")) := params$Omega_ref[, 1, 2])
sd_se_ref <- tibble(!!as.name(paste0("SD for logit(Se)", ", ", "Reference tests")) := params$sigma_ref[, 1])
sd_sp_ref <- tibble(!!as.name(paste0("SD for logit(Sp)", ", ", "Reference tests")) := params$sigma_ref[, 2])
se_index <- tibble( !!as.name(paste0("Se", ", ", "index test")) := params$Se_index)
sp_index <- tibble( !!as.name(paste0("Sp", ", ", "index test")) := params$Sp_index)
corr_index <- tibble( !!as.name(paste0("Correlation", ", ", "index test")) := params$Omega_index[, 1, 2])
sd_se_index <- tibble(!!as.name(paste0("SD for logit(Se)", ", ", "index test")) := params$sigma_index[, 1])
sd_sp_index <- tibble(!!as.name(paste0("SD for logit(Sp)", ", ", "index test")) := params$sigma_index[, 2])
prev <- tibble(!!as.name("Prevalence") := params$p)
n_samps <- (2000 - 200)*4
Samples_wide <- tibble(cbind(se_refs, sp_refs, corr_ref, sd_se_ref, sd_sp_ref,
se_index, sp_index, corr_index, sd_se_index, sd_sp_index, prev))
Samples <- tidyr::gather(Samples_wide,
key = Parameter,
value = samples,
!!as.name(paste0("Se", ", ", refs_names[[1]])):Prevalence)
g <- ggplot(data = Samples, aes(x=samples)) +
geom_density(alpha = 0.50) +
theme_bw() +
theme(legend.position = "none") +
facet_wrap( ~ Parameter, scales = "free") +
xlab(" ") +
ylab(" ")
g
})
# output plot
observe({
output$model_priors_plot <- renderPlot({
validate(
need(!(is.null(draws())), "Please run prior model to display plot")
)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
req(data(), draws(), 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"
)}
}
)
}
)
}
# LCM - table of prior distributions ------------------------------------------- -----------------------------------------------------
# UI function
LCM_model_priors_table_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(outputId = ns("model_priors_table")),
h4("Priors for index test parameters:"),
tableOutput(ns("priors_table_1")),
# downloadButton(ns("download_priors_table_1"), "Download Table"),
h4("Priors for reference test parameters (test-specific):"),
tableOutput(ns("priors_table_2")),
# downloadButton(ns("download_priors_table_2"), "Download Table"),
h4("Priors for reference test parameters (shared):"),
tableOutput(ns("priors_table_3")),
# downloadButton(ns("download_priors_table_3"), "Download Table"),
h4("Prior for prevelance:"),
tableOutput(ns("priors_table_4")),
# downloadButton(ns("download_priors_table_4"), "Download 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)"),
p("For study specific prevalences - Beta(1,1), equivalent to a 95% prior interval of (0.03, 0.97)")
)
}
# server function
LCM_model_priors_table_server <- function(id,
draws,
data,
LCM_options_indicators) {
moduleServer(
id,
function(input, output, session) {
# LCM: priors table 1 - index test params ------------------------------ ----------------------------------
priors_table_obj_1 <- reactive({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
X <- data()
mod <- draws()
params <- rstan::extract(mod)
Sens_index <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se_index"))$summary
Spec_index <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp_index"))$summary
correlation_index <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega_index"))$summary
between_study_sd_index <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_index"))$summary
prev <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("p"))$summary
Z = data.frame(estimate = c(Sens_index[, "50%"], Spec_index[, "50%"],
1 - Spec_index[, "50%"],
correlation_index[, "50%"][3],
between_study_sd_index[, "50%"][1], between_study_sd_index[, "50%"][2]),
sd = c(Sens_index[, "sd"], Spec_index[, "sd"],
Spec_index[, "sd"],
correlation_index[, "sd"][3],
between_study_sd_index[, "sd"][1], between_study_sd_index[, "sd"][2]),
lci = c(Sens_index[, "2.5%"], Spec_index[, "2.5%"],
1 - Spec_index[, "97.5%"],
correlation_index[, "2.5%"][3],
between_study_sd_index[, "2.5%"][1], between_study_sd_index[, "2.5%"][2]),
uci = c(Sens_index[, "97.5%"], Spec_index[, "97.5%"],
1 - Spec_index[, "2.5%"],
correlation_index[, "97.5%"][3],
between_study_sd_index[, "97.5%"][1], between_study_sd_index[, "97.5%"][2]),
row.names=c("Sensitivity_index", "Specifcity_index", "FPR_index",
"Correlation_index",
"sd_sens_index", "sd_spec_index")
)
# Create a matrix to store the parameter estimates
nrow <- 6 + 1
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- paste0("Sensitivity ", "( ", paste0("logit", HTML("<sup>-1;</sup>")), "(", HTML("μ<sub>1</sub>"),"[",input$index_test_name,"]", ")", ")")
s.matrix[2,1] <- paste0("Specificity ", "( ", paste0("logit", HTML("<sup>-1;</sup>")), "(", HTML("μ<sub>0</sub>"),"[",input$index_test_name,"]", ")", ")")
s.matrix[3,1] <- "False Positive Rate (1 - specificity) "
s.matrix[4,1] <- paste0("Between-study Correlation" , "( ", HTML("ρ"),"[",input$index_test_name,"]", " )")
s.matrix[5,1] <- paste0("Between-study SD for logit(Sensitivity) ", "( ", HTML("σ<sub>1</sub>"),"[",input$index_test_name,"]", " )")
s.matrix[6,1] <- paste0("Between-study SD for logit(Specificity) ", "( ", HTML("σ<sub>0</sub>"),"[",input$index_test_name,"]", " )")
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
# if either Se or Sp for index test. is fixed
if ( ((LCM_options_indicators$LCM_SeI_fixed_indicator == 1) || (LCM_options_indicators$LCM_SpI_fixed_indicator == 1))
) {
s.matrix[4,2:4] <- "-"
if (LCM_options_indicators$LCM_SeI_fixed_indicator == 1) { # if SeI fixed, no between-study SD for SeI
s.matrix[5,2:4] <- "-"
}
if (LCM_options_indicators$LCM_SpI_fixed_indicator == 1) { # if SpI fixed, no between-study SD for SpI
s.matrix[6,2:4] <- "-"
}
}
else {
# do nothing
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3, 6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Prior Median", "Standard Deviation", "95% Prior Interval")
s.matrix
})
# LCM: priors table 2 - reference test params (test-specific) ------------------------------ ----------------------------------
priors_table_obj_2 <- reactive({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
X <- data()
mod <- draws()
params <- rstan::extract(mod)
num_refs <- ncol(params$Se_ref)
refs_names <- c(levels(factor(X$reference.cat)))
for (i in 1:num_refs) {
Sens_refs <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se_ref"))$summary
Spec_refs <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp_ref"))$summary
}
Z_group <- list()
for (i in 1:num_refs) {
Z_group[[i]] <- data.frame(
estimate = c(Sens_refs[, "50%"], Spec_refs[, "50%"],
1 - Spec_refs[, "50%"]),
sd = c(Sens_refs[, "sd"], Spec_refs[, "sd"],
Spec_refs[, "sd"]),
lci = c(Sens_refs[, "2.5%"], Spec_refs[, "2.5%"],
1 - Spec_refs[, "97.5%"]),
uci = c(Sens_refs[, "97.5%"], Spec_refs[, "97.5%"],
1 - Spec_refs[, "2.5%"]),
row.names=c(paste0("Sensitivity_ref","_",refs_names),
paste0("Specificity_ref","_",refs_names),
paste0("FPR_ref","_",refs_names))
)
}
s.matrix.group <- vector("list", num_refs)
statticks <- list()
s.matrix.group.dataframes <- list()
for (i in 1:num_refs) {
nrow <- 3 + 1
s.matrix.group[[i]] <- matrix(nrow=nrow, ncol=6)
s.matrix.group[[i]][1,1] <- paste0("Sensitivity", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>1</sub>"),"[", refs_names[i],"]", ")", ")")
s.matrix.group[[i]][2,1] <- paste0("Specificity", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>0</sub>"),"[", refs_names[i],"]", ")", ")")
s.matrix.group[[i]][3,1] <- paste0("False Positive Rate (1 - specificity)")
for (k in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix.group[[i]][j,k+1] <- sprintf('%4.3f', Z_group[[i]][j,k])
}
}
s.matrix.group[[i]][nrow, 1:5] <- ""
s.matrix.group[[i]][, 6] <- paste0("(", s.matrix.group[[i]][, 4], ", ", s.matrix.group[[i]][, 5], ")")
s.matrix.group[[i]][nrow, 1:6] <- ""
s.matrix.group[[i]] <- s.matrix.group[[i]][, c(1, 2, 3 ,6)]
s.matrix.group.dataframes[[i]] <- data.frame(s.matrix.group[[i]])
colnames(s.matrix.group.dataframes[[i]]) <- c( paste("Parameters for",
refs_names[i]),
"Prior Median",
"Standard Deviation",
"95% Prior Interval")
}
rbindlist(s.matrix.group.dataframes)
s.matrix.group.dataframe_allgroups <- tibble(rbindlist(s.matrix.group.dataframes))
s.matrix.group.dataframe_allgroups2 <- s.matrix.group.dataframe_allgroups %>%
dplyr::mutate( " " = lag(!!as.name(paste("Parameters for", refs_names[1]))),
`Prior Median` = lag(`Prior Median`),
`Standard Deviation` = lag(`Standard Deviation`),
`95% Prior Interval` = lag(`95% Prior Interval`))
s.matrix.group.dataframe_allgroups3 <- s.matrix.group.dataframe_allgroups2 %>%
dplyr::select(" ",
`Prior Median`,
`Standard Deviation`,
`95% Prior Interval` )
s.matrix.group.dataframe_allgroups3
s.matrix.group.dataframe_allgroups3[1,2] <- ""
s.matrix.group.dataframe_allgroups3[1,3] <- ""
for (i in 1:num_refs) {
s.matrix.group.dataframe_allgroups3[(i-1)*nrow+1,1] <- (paste0("<b> Parameters for ", refs_names[i], ":", " </b>"))
}
s.matrix.group.dataframe_allgroups3
})
# LCM: priors table 3 - reference test params (shared across tests) ------------------------------ ----------------------------------
priors_table_obj_3 <- reactive({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
X <- data()
mod <- draws()
params <- rstan::extract(mod)
num_refs <- ncol(params$Se_ref)
refs_names <- c(levels(factor(X$reference.cat)))
correlation_ref <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega_ref"))$summary
between_study_sd_ref <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_ref"))$summary
Z = data.frame(estimate = c(correlation_ref[, "50%"][3],
between_study_sd_ref[, "50%"][1],
between_study_sd_ref[, "50%"][2]),
sd = c(correlation_ref[, "sd"][3],
between_study_sd_ref[, "sd"][1],
between_study_sd_ref[, "sd"][2]),
lci = c(correlation_ref[, "2.5%"][3],
between_study_sd_ref[, "2.5%"][1],
between_study_sd_ref[, "2.5%"][2]),
uci = c(correlation_ref[, "97.5%"][3],
between_study_sd_ref[, "97.5%"][1],
between_study_sd_ref[, "97.5%"][2]),
row.names=c("Correlation_ref",
"sd_sens_ref",
"sd_spec_ref")
)
# Create a matrix to store the parameter estimates
nrow <- 3 + 1
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- paste0("Between-study Correlation " , "( ", HTML("ρ"), "[Refs]", " )")
s.matrix[2,1] <- paste0("Between-study SD for logit(Sensitivity) ", "( ", HTML("σ<sub>1</sub>"), "[Refs]", " )")
s.matrix[3,1] <- paste0("Between-study SD for logit(Specificity) ", "( ", HTML("σ<sub>0</sub>"), "[Refs]", " )")
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
# if either Se or Sp for reference test. is fixed, no HSROC params and no between-study SD
if ( ((LCM_options_indicators$LCM_SeR_fixed_indicator == 1) || (LCM_options_indicators$LCM_SpR_fixed_indicator == 1))
) {
s.matrix[1,2:5] <- "-"
if (LCM_options_indicators$LCM_SeR_fixed_indicator == 1) { # if SeR fixed, no between-study SD for SeR
s.matrix[2,2:5] <- "-"
}
if (LCM_options_indicators$LCM_SpR_fixed_indicator == 1) { # if SpR fixed, no between-study SD for SpR
s.matrix[3,2:5] <- "-"
}
}
else {
# do nothing
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3, 6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Prior Median", "Standard Deviation", "95% Prior Interval")
s.matrix
})
# LCM: priors table 4 - prevelance prior ------------------------------ ----------------------------------
priors_table_obj_4 <- reactive({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
X <- data()
mod <- draws()
prev <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("p"))$summary
Z = data.frame(estimate = c(prev[, "50%"]),
sd = c(prev[, "sd"]),
lci = c(prev[, "2.5%"]),
uci = c(prev[, "97.5%"]),
row.names=c("prev")
)
# Create a matrix to store the parameter estimates
nrow <- 1 + 1
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- "Disease prevalence"
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3 ,6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Prior Median", "Standard Deviation", "95% Prior Interval")
s.matrix
})
# output tables
output$priors_table_1 <- renderTable({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
priors_table_obj_1()
}, sanitize.text.function = function(x) x)
output$priors_table_2 <- renderTable({
req(data(), draws(), cancelOutput = TRUE)
validate(
need(data()$reference.cat, "Please select a dataset with a covariate for
reference test type (inputted as a column named reference.cat)
to use latent class model")
)
priors_table_obj_2()
}, sanitize.text.function = function(x) x)
output$priors_table_3 <- renderTable({