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#' @title GEEModuleUI: shiny modulde UI for generalized estimating equation(GEE).
#' @description Shiny modulde UI for generalized estimating equation(GEE).
#' @param id id
#' @return Shiny modulde UI for generalized estimating equation(GEE).
#' @details Shiny modulde UI for generalized estimating equation(GEE).
#' @examples
#' library(shiny)
#' library(DT)
#' library(data.table)
#' library(jstable)
#' ui <- fluidPage(
#' sidebarLayout(
#' sidebarPanel(
#' GEEModuleUI("linear")
#' ),
#' mainPanel(
#' DTOutput("lineartable")
#' )
#' )
#' )
#'
#' server <- function(input, output, session) {
#' data <- reactive(mtcars)
#' data.label <- reactive(jstable::mk.lev(mtcars))
#' id.gee <- reactive("mpg")
#'
#' out_linear <- callModule(GEEModuleLinear, "linear",
#' data = data, data_label = data.label,
#' data_varStruct = NULL, id.gee = id.gee
#' )
#'
#' output$lineartable <- renderDT({
#' hide <- which(colnames(out_linear()$table) == "sig")
#' datatable(out_linear()$table,
#' rownames = T, extension = "Buttons", caption = out_linear()$caption,
#' options = c(
#' opt.tbreg(out_linear()$caption),
#' list(columnDefs = list(list(visible = FALSE, targets = hide))),
#' list(scrollX = TRUE)
#' )
#' ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
#' })
#' }
#' @rdname GEEModuleUI
#' @export
GEEModuleUI <- function(id) {
# Create a namespace function using the provided id
ns <- NS(id)
tagList(
uiOutput(ns("dep")),
uiOutput(ns("indep")),
sliderInput(ns("decimal"), "Digits",
min = 1, max = 4, value = 2
),
checkboxInput(ns("regressUI_subcheck"), "Sub-group analysis"),
uiOutput(ns("regressUI_subvar")),
uiOutput(ns("regressUI_subval"))
)
}
#' @title GEEModuleLinear: shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.
#' @description Shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.
#' @param input input
#' @param output output
#' @param session session
#' @param data reactive data, ordered by id.
#' @param data_label reactive data label
#' @param data_varStruct List of variable structure, Default: NULL
#' @param nfactor.limit nlevels limit in factor variable, Default: 10
#' @param id.gee reactive repeated measure variable
#' @return Shiny modulde server for gaussian generalized estimating equation(GEE).
#' @details Shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.
#' @examples
#' library(shiny)
#' library(DT)
#' library(data.table)
#' library(jstable)
#' ui <- fluidPage(
#' sidebarLayout(
#' sidebarPanel(
#' GEEModuleUI("linear")
#' ),
#' mainPanel(
#' DTOutput("lineartable")
#' )
#' )
#' )
#'
#' server <- function(input, output, session) {
#' data <- reactive(mtcars)
#' data.label <- reactive(jstable::mk.lev(mtcars))
#' id.gee <- reactive("mpg")
#'
#' out_linear <- callModule(GEEModuleLinear, "linear",
#' data = data, data_label = data.label,
#' data_varStruct = NULL, id.gee = id.gee
#' )
#'
#' output$lineartable <- renderDT({
#' hide <- which(colnames(out_linear()$table) == "sig")
#' datatable(out_linear()$table,
#' rownames = T, extension = "Buttons", caption = out_linear()$caption,
#' options = c(
#' opt.tbreg(out_linear()$caption),
#' list(columnDefs = list(list(visible = FALSE, targets = hide))),
#' list(scrollX = TRUE)
#' )
#' ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
#' })
#' }
#' @rdname GEEModuleLinear
#' @export
#' @import shiny
#' @importFrom data.table data.table .SD :=
#' @importFrom labelled var_label<-
#' @importFrom stats glm as.formula model.frame complete.cases
#' @importFrom purrr map_lgl
#' @importFrom geepack geeglm
GEEModuleLinear <- function(input, output, session, data, data_label, data_varStruct = NULL, nfactor.limit = 10, id.gee) {
## To remove NOTE.
level <- val_label <- variable <- NULL
if (is.null(data_varStruct)) {
data_varStruct <- reactive(list(variable = names(data())))
}
vlist <- reactive({
mklist <- function(varlist, vars) {
lapply(
varlist,
function(x) {
inter <- intersect(x, vars)
if (length(inter) == 1) {
inter <- c(inter, "")
}
return(inter)
}
)
}
id <- id.gee()
factor_vars <- names(data())[data()[, lapply(.SD, class) %in% c("factor", "character")]]
factor_vars <- setdiff(factor_vars, id)
# factor_vars <- names(data())[sapply(names(data()), function(x){class(data()[[x]]) %in% c("factor", "character")})]
factor_list <- mklist(data_varStruct(), factor_vars)
conti_vars <- setdiff(names(data()), factor_vars)
conti_vars <- setdiff(conti_vars, id)
conti_list <- mklist(data_varStruct(), conti_vars)
nclass_factor <- unlist(data()[, lapply(.SD, function(x) {
length(levels(x))
}), .SDcols = factor_vars])
# nclass_factor <- sapply(factor_vars, function(x){length(unique(data()[[x]]))})
group_vars <- factor_vars[nclass_factor >= 2 & nclass_factor <= nfactor.limit & nclass_factor < nrow(data())]
group_list <- mklist(data_varStruct(), group_vars)
except_vars <- factor_vars[nclass_factor > nfactor.limit | nclass_factor == 1 | nclass_factor == nrow(data())]
return(list(
factor_vars = factor_vars, factor_list = factor_list, conti_vars = conti_vars, conti_list = conti_list,
group_vars = group_vars, group_list = group_list, except_vars = except_vars
))
})
output$dep <- renderUI({
tagList(
selectInput(session$ns("dep_vars"), "Dependent variable",
choices = vlist()$conti_list, multiple = F,
selected = vlist()$conti_vars[1]
)
)
})
output$indep <- renderUI({
id <- id.gee()
req(!is.null(input$dep_vars))
vars <- setdiff(setdiff(names(data()), vlist()$except_vars), c(input$dep_vars, id))
# varsIni <- sapply(vars,
# function(v){
# forms <- as.formula(paste(input$dep_vars, "~", v))
# coef <- summary(geepack::geeglm(forms, data = data()[!is.na(get(v))], family = "gaussian", id = get(id), corstr = "exchangeable"))$coef
# sigOK <- !all(coef[-1, "Pr(>|W|)"] > 0.05)
# return(sigOK)
# })
tagList(
selectInput(session$ns("indep_vars"), "Independent variables",
choices = mklist(data_varStruct(), vars), multiple = T,
selected = vars[1]
)
)
})
observeEvent(input$regressUI_subcheck, {
output$regressUI_subvar <- renderUI({
req(input$regressUI_subcheck == T)
var_subgroup <- setdiff(names(data()), c(input$dep_vars, input$indep_vars, id.gee()))
var_subgroup_list <- mklist(data_varStruct(), var_subgroup)
validate(
need(length(var_subgroup) > 0, "No variables for sub-group analysis")
)
tagList(
selectInput(session$ns("subvar_regress"), "Sub-group variables",
choices = var_subgroup_list, multiple = T,
selected = var_subgroup[1]
)
)
})
output$regressUI_subval <- renderUI({
req(input$regressUI_subcheck == T)
req(length(input$subvar_regress) > 0)
outUI <- tagList()
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
outUI[[v]] <- selectInput(session$ns(paste0("subval_regress", v)), paste0("Sub-group value: ", input$subvar_regress[[v]]),
choices = data_label()[variable == input$subvar_regress[[v]], level], multiple = T,
selected = data_label()[variable == input$subvar_regress[[v]], level][1]
)
} else {
val <- stats::quantile(data()[[input$subvar_regress[[v]]]], na.rm = T)
outUI[[v]] <- sliderInput(session$ns(paste0("subval_regress", v)), paste0("Sub-group range: ", input$subvar_regress[[v]]),
min = val[1], max = val[5],
value = c(val[2], val[4])
)
}
}
outUI
})
})
out <- reactive({
data.regress <- data()
label.regress <- data_label()
idgee_Plz_Noduplicate <- id.gee()
if (input$regressUI_subcheck == T) {
validate(
need(length(input$subvar_regress) > 0, "No variables for subsetting"),
need(all(sapply(1:length(input$subvar_regress), function(x) {
length(input[[paste0("subval_regress", x)]])
})), "No value for subsetting")
)
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
data.regress <- data.regress[get(input$subvar_regress[[v]]) %in% input[[paste0("subval_regress", v)]]]
} else {
data.regress <- data.regress[get(input$subvar_regress[[v]]) >= input[[paste0("subval_regress", v)]][1] & get(input$subvar_regress[[v]]) <= input[[paste0("subval_regress", v)]][2]]
}
}
data.regress[, (vlist()$factor_vars) := lapply(.SD, factor), .SDcols = vlist()$factor_vars]
label.regress2 <- mk.lev(data.regress)[, c("variable", "level")]
data.table::setkey(data_label(), "variable", "level")
data.table::setkey(label.regress2, "variable", "level")
label.regress <- data_label()[label.regress2]
}
y <- input$dep_vars
xs <- input$indep_vars
validate(
need(!is.null(input$indep_vars), "Please select at least 1 variable")
)
form <- as.formula(paste(y, "~", paste(xs, collapse = " + "), sep = " "))
mf <- model.frame(form, data.regress)
validate(
need(nrow(mf) > 0, paste("No complete data due to missingness. Please remove some variables from independent variables"))
)
lgl.1level <- purrr::map_lgl(mf, ~ length(unique(.x)) == 1)
validate(
need(sum(lgl.1level) == 0, paste(paste(names(lgl.1level)[lgl.1level], collapse = " ,"), "has(have) a unique value. Please remove that from independent variables"))
)
nomiss <- stats::complete.cases(data.regress[, c(y, xs), with = F])
res.gee <- geepack::geeglm(form, data = data.regress[nomiss, ], family = "gaussian", id = get(idgee_Plz_Noduplicate), corstr = "exchangeable")
info.gee <- jstable::geeglm.display(res.gee, decimal = input$decimal)
info.gee$caption <- gsub("idgee_Plz_Noduplicate", idgee_Plz_Noduplicate, info.gee$caption)
ltb.gee <- jstable::LabeljsGeeglm(info.gee, ref = label.regress)
out.tb <- rbind(ltb.gee$table, ltb.gee$metric)
cap.gee <- ltb.gee$caption
if (input$regressUI_subcheck == T) {
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
cap.gee <- paste(cap.gee, ", ", label.regress[variable == input$subvar_regress[[v]], var_label][1], ": ", paste(label.regress[variable == input$subvar_regress[[v]] & level %in% input[[paste0("subval_regress", v)]], val_label], collapse = ", "), sep = "")
} else {
cap.gee <- paste(cap.gee, ", ", label.regress[variable == input$subvar_regress[[v]], var_label][1], ": ", paste(input[[paste0("subval_regress", v)]], collapse = "~"), sep = "")
}
}
}
sig <- ifelse(out.tb[, ncol(out.tb)] == "< 0.001", "**", ifelse(as.numeric(out.tb[, ncol(out.tb)]) <= 0.05, "**", NA))
out.gee <- cbind(out.tb, sig)
return(list(table = out.gee, caption = cap.gee))
})
return(out)
}
#' @title GEEModuleLogistic: shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.
#' @description Shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.
#' @param input input
#' @param output output
#' @param session session
#' @param data reactive data, ordered by id.
#' @param data_label reactive data label
#' @param data_varStruct List of variable structure, Default: NULL
#' @param nfactor.limit nlevels limit in factor variable, Default: 10
#' @param id.gee reactive repeated measure variable
#' @return Shiny modulde server for binomial gaussian generalized estimating equation(GEE).
#' @details Shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.
#' @examples
#' library(shiny)
#' library(DT)
#' library(data.table)
#' library(jstable)
#' ui <- fluidPage(
#' sidebarLayout(
#' sidebarPanel(
#' GEEModuleUI("logistic")
#' ),
#' mainPanel(
#' DTOutput("logistictable")
#' )
#' )
#' )
#'
#' server <- function(input, output, session) {
#' data <- reactive(mtcars)
#' data.label <- reactive(jstable::mk.lev(mtcars))
#' id.gee <- reactive("mpg")
#'
#' out_logistic <- callModule(GEEModuleLogistic, "logistic",
#' data = data, data_label = data.label,
#' data_varStruct = NULL, id.gee = id.gee
#' )
#'
#' output$logistictable <- renderDT({
#' hide <- which(colnames(out_logistic()$table) == "sig")
#' datatable(out_logistic()$table,
#' rownames = T, extension = "Buttons",
#' caption = out_logistic()$caption,
#' options = c(
#' opt.tbreg(out_logistic()$caption),
#' list(columnDefs = list(list(visible = FALSE, targets = hide))),
#' list(scrollX = TRUE)
#' )
#' ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
#' })
#' }
#' @rdname GEEModuleLogistic
#' @export
#' @import shiny
#' @importFrom data.table data.table .SD :=
#' @importFrom labelled var_label<-
#' @importFrom stats glm as.formula model.frame complete.cases
#' @importFrom purrr map_lgl
#' @importFrom geepack geeglm
GEEModuleLogistic <- function(input, output, session, data, data_label, data_varStruct = NULL, nfactor.limit = 10, id.gee) {
## To remove NOTE.
level <- val_label <- variable <- NULL
if (is.null(data_varStruct)) {
data_varStruct <- reactive(list(variable = names(data())))
}
vlist <- reactive({
mklist <- function(varlist, vars) {
lapply(
varlist,
function(x) {
inter <- intersect(x, vars)
if (length(inter) == 1) {
inter <- c(inter, "")
}
return(inter)
}
)
}
id <- id.gee()
factor_vars <- names(data())[data()[, lapply(.SD, class) %in% c("factor", "character")]]
factor_vars <- setdiff(factor_vars, id)
# data[, (factor_vars) := lapply(.SD, as.factor), .SDcols= factor_vars]
factor_list <- mklist(data_varStruct(), factor_vars)
nclass_factor <- unlist(data()[, lapply(.SD, function(x) {
length(levels(x))
}), .SDcols = factor_vars])
class01_factor <- unlist(data()[, lapply(.SD, function(x) {
identical(levels(x), c("0", "1"))
}), .SDcols = factor_vars])
validate(
need(length(class01_factor) >= 1, "No categorical variables coded as 0, 1 in data")
)
factor_01vars <- factor_vars[class01_factor]
factor_01_list <- mklist(data_varStruct(), factor_01vars)
except_vars <- factor_vars[nclass_factor > nfactor.limit | nclass_factor == 1 | nclass_factor == nrow(data())]
return(list(
factor_vars = factor_vars, factor_list = factor_list, nclass_factor = nclass_factor, factor_01vars = factor_01vars,
factor_01_list = factor_01_list, except_vars = except_vars
))
})
output$dep <- renderUI({
validate(
need(length(vlist()$factor_01vars) >= 1, "No candidate dependent variable coded as 0, 1")
)
tagList(
selectInput(session$ns("dep_vars"), "Dependent variable",
choices = vlist()$factor_01_list, multiple = F,
selected = vlist()$factor_01vars[1]
)
)
})
output$indep <- renderUI({
id <- id.gee()
req(!is.null(input$dep_vars))
vars <- setdiff(setdiff(names(data()), vlist()$except_vars), c(input$dep_vars, id))
# varsIni <- sapply(vars,
# function(v){
# forms <- as.formula(paste(input$dep_vars, "~", v))
# coef <- summary(geepack::geeglm(forms, data = data()[!is.na(get(v))], family = "gaussian", id = get(id), corstr = "exchangeable"))$coef
# sigOK <- !all(coef[-1, "Pr(>|W|)"] > 0.05)
# return(sigOK)
# })
tagList(
selectInput(session$ns("indep_vars"), "Independent variables",
choices = mklist(data_varStruct(), vars), multiple = T,
selected = vars[1]
)
)
})
observeEvent(input$regressUI_subcheck, {
output$regressUI_subvar <- renderUI({
req(input$regressUI_subcheck == T)
var_subgroup <- setdiff(names(data()), c(input$dep_vars, input$indep_vars, id.gee()))
var_subgroup_list <- mklist(data_varStruct(), var_subgroup)
validate(
need(length(var_subgroup) > 0, "No variables for sub-group analysis")
)
tagList(
selectInput(session$ns("subvar_regress"), "Sub-group variables",
choices = var_subgroup_list, multiple = T,
selected = var_subgroup[1]
)
)
})
output$regressUI_subval <- renderUI({
req(input$regressUI_subcheck == T)
req(length(input$subvar_regress) > 0)
outUI <- tagList()
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
outUI[[v]] <- selectInput(session$ns(paste0("subval_regress", v)), paste0("Sub-group value: ", input$subvar_regress[[v]]),
choices = data_label()[variable == input$subvar_regress[[v]], level], multiple = T,
selected = data_label()[variable == input$subvar_regress[[v]], level][1]
)
} else {
val <- stats::quantile(data()[[input$subvar_regress[[v]]]], na.rm = T)
outUI[[v]] <- sliderInput(session$ns(paste0("subval_regress", v)), paste0("Sub-group range: ", input$subvar_regress[[v]]),
min = val[1], max = val[5],
value = c(val[2], val[4])
)
}
}
outUI
})
})
out <- reactive({
req(input$dep_vars)
req(input$indep_vars)
data.logistic <- data()
label.regress <- data_label()
idgee_Plz_Noduplicate <- id.gee()
if (input$regressUI_subcheck == T) {
validate(
need(length(input$subvar_regress) > 0, "No variables for subsetting"),
need(all(sapply(1:length(input$subvar_regress), function(x) {
length(input[[paste0("subval_regress", x)]])
})), "No value for subsetting")
)
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
data.logistic <- data.logistic[get(input$subvar_regress[[v]]) %in% input[[paste0("subval_regress", v)]]]
} else {
data.logistic <- data.logistic[get(input$subvar_regress[[v]]) >= input[[paste0("subval_regress", v)]][1] & get(input$subvar_regress[[v]]) <= input[[paste0("subval_regress", v)]][2]]
}
}
data.logistic[, (vlist()$factor_vars) := lapply(.SD, factor), .SDcols = vlist()$factor_vars]
label.regress2 <- mk.lev(data.logistic)[, c("variable", "level")]
data.table::setkey(data_label(), "variable", "level")
data.table::setkey(label.regress2, "variable", "level")
label.regress <- data_label()[label.regress2]
}
y <- input$dep_vars
xs <- input$indep_vars
data.logistic[[y]] <- as.numeric(as.vector(data.logistic[[y]]))
validate(
need(!is.null(input$indep_vars), "Please select at least 1 variable")
)
form <- as.formula(paste(y, "~", paste(xs, collapse = " + "), sep = " "))
mf <- model.frame(form, data.logistic)
validate(
need(nrow(mf) > 0, paste("No complete data due to missingness. Please remove some variables from independent variables"))
)
lgl.1level <- purrr::map_lgl(mf, ~ length(unique(.x)) == 1)
validate(
need(sum(lgl.1level) == 0, paste(paste(names(lgl.1level)[lgl.1level], collapse = " ,"), "has(have) a unique value. Please remove that from independent variables"))
)
nomiss <- stats::complete.cases(data.logistic[, c(y, xs), with = F])
res.gee <- geepack::geeglm(form, data = data.logistic[nomiss, ], family = "binomial", id = get(idgee_Plz_Noduplicate), corstr = "exchangeable")
info.gee <- jstable::geeglm.display(res.gee, decimal = input$decimal)
info.gee$caption <- gsub("idgee_Plz_Noduplicate", idgee_Plz_Noduplicate, info.gee$caption)
ltb.gee <- jstable::LabeljsGeeglm(info.gee, ref = label.regress)
out.tb <- rbind(ltb.gee$table, ltb.gee$metric)
cap.gee <- ltb.gee$caption
if (input$regressUI_subcheck == T) {
for (v in seq_along(input$subvar_regress)) {
if (input$subvar_regress[[v]] %in% vlist()$factor_vars) {
cap.gee <- paste(cap.gee, ", ", label.regress[variable == input$subvar_regress[[v]], var_label][1], ": ", paste(label.regress[variable == input$subvar_regress[[v]] & level %in% input[[paste0("subval_regress", v)]], val_label], collapse = ", "), sep = "")
} else {
cap.gee <- paste(cap.gee, ", ", label.regress[variable == input$subvar_regress[[v]], var_label][1], ": ", paste(input[[paste0("subval_regress", v)]], collapse = "~"), sep = "")
}
}
}
sig <- ifelse(out.tb[, ncol(out.tb)] == "< 0.001", "**", ifelse(as.numeric(out.tb[, ncol(out.tb)]) <= 0.05, "**", NA))
out.gee <- cbind(out.tb, sig)
return(list(table = out.gee, caption = cap.gee))
})
return(out)
}
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