Nothing
R/mod_prob.R
In apisensr: Interface to 'episensr' for Sensitivity Analysis of Epidemiological Results
Defines functions
mod_prob_server
mod_prob_ui
# Probabilistic Analysis Module UI
#' @title UI Module for Probabilistic Analysis tab
#' @description A shiny Module to render the Probabilistic Analysis tab (selection bias
#' analysis `probsens.sel`, bias analysis for unmeasured confounder `probsens.conf`, and
#' misclassification bias analysis `probsens`).
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_prob
#'
#' @keywords internal
#' @noRd
#' @import episensr
#' @import ggplot2
#' @importFrom shiny NS tagList
#' @importFrom shinyjs runjs
#' @importFrom rhandsontable hot_to_r rHandsontableOutput renderRHandsontable rhandsontable
mod_prob_ui <- function(id, label = "tab_prob") {
ns <- NS(id)
material_tab_content(
tab_id = id,
material_row(
material_column(
width = 4,
material_card(
material_dropdown(
ns("prob_type"),
label = "Choose bias analysis:",
choices = c(
"Selection bias" = "probsens_sel",
"Unmeasured confounder" = "probsens_conf",
"Misclassification bias" = "probsens"
),
color = "#d50000"
),
"Observed data",
div(id = "obs-table-prob",
rhandsontable::rHandsontableOutput(ns('two_by_two_prob')),
material_button(
input_id = ns("reset_table"),
label = "Table back to example",
icon = "restore",
color = "red accent-4"
)
),
br(),
div(
id = "side-panel-prob",
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_radio_button(
input_id = ns("misclassProb_type"),
label = "Misclassification of:",
choices = c("exposure", "outcome"),
selected = "exposure",
color = "#ff5131"),
br(),
material_number_box(
input_id = ns("chosen_seed"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_switch(
input_id = ns("diff"),
off_label = "Non-differential misclassification",
on_label = "Differential misclassification",
initial_value = FALSE,
color = "#ff5131"),
br(),
material_checkbox(
input_id = ns("discard"),
label = "Discard draws of negative adjusted counts.",
initial_value = TRUE,
color = "#9b0000"),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_slider(
input_id = ns("corr_se"),
label = "Correlation between case and non-case sensitivities:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "#ff5131"
),
material_slider(
input_id = ns("corr_sp"),
label = "Correlation between case and non-case specificities:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "9b0000"
)
),
material_button(
input_id = "help_probsens",
label = "Help",
icon = "help",
color = "orange"),
br(),
material_dropdown(
input_id = ns("plot_probsens"),
label = "Plot of output/parameters:",
choices = c(
"Relative risk - systematic error" = "rr",
"Odds ratio - systematic error" = "or",
"Relative risk - systematic and random error" = "rr_tot",
"Odds ratio - systematic and random error" = "or_tot",
"Sensitivity for cases" = "seca",
"Sensitivity of exposed" = "seexp",
"Specificity for cases" = "spca",
"Specificity of exposed" = "spexp"),
selected = "rr",
color = "#ff5131")
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel"',
ns = ns,
material_number_box(
input_id = ns("chosen_seed_sel"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps_sel"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_switch(
input_id = ns("or_case"),
off_label = "Using selection bias odds",
on_label = "Using selection probabilities",
initial_value = FALSE,
color = "#ff5131"),
br(),
material_button(
input_id = "help_probsens_sel",
label = "Help",
icon = "help",
color = "orange"),
material_dropdown(
input_id = ns("plot_probsens_sel"),
label = "Plot of output/parameters",
choices = c(
"Odds ratio - systematic error" = "or",
"Odds ratio - systematic and random error" = "or_tot",
"Selection odds ratio" = "or_sel"),
selected = "or",
color = "#ff5131")
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_number_box(
input_id = ns("chosen_seed_conf"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps_conf"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_slider(
input_id = ns("corr_conf"),
label = "Correlation between the exposure-specific confounder prevalences:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "#ff5131"),
material_checkbox(
input_id = ns("discard_conf"),
label = "Discard draws of negative adjusted counts.",
initial_value = TRUE,
color = "#ff5131"),
br(),
material_button(
input_id = "help_probsens_conf",
label = "Help",
icon = "help",
color = "orange"),
material_dropdown(
input_id = ns("plot_probsens_conf"),
label = "Plot of output/parameters",
choices = c(
"Relative risk - systematic error" = "rr",
"Odds ratio - systematic error" = "or",
"Relative risk - systematic and random error" = "rr_tot",
"Odds ratio - systematic and random error" = "or_tot",
"Distribution of prevalence of exposure among the exposed" = "prev.exp",
"Distribution of prevalence of exposure among the unexposed" = "prev.nexp",
"Distribution of confounder-disease relative risk (or confounder-exposure odds ratio)" = "risk"),
selected = "rr",
color = "#9b0000")
),
## Alpha level
material_slider(
ns("alpha"),
HTML("α-level:"),
min_value = 0.01,
max_value = 0.2,
step_size = 0.01,
initial_value = 0.05,
color = "#ff5131")
)
)
),
material_column(
width = 4,
material_card(
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_dropdown(
input_id = ns("seca_parms"),
label = "Distribution, sensitivity of exposure classification among those with the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.seca_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_seca_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.seca_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_seca_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.seca_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_seca_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.seca_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_seca_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seca_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seca_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_seca_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seca_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seca_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "beta"',
ns = ns,
material_number_box(ns("parms_seca_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_seca_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_dropdown(
input_id = ns("seexp_parms"),
label = "Distribution, sensitivity of exposure classification among those without the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.seexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_seexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.seexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_seexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.seexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_seexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.seexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_seexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_seexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_seexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_seexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 0',
ns = ns,
material_dropdown(
input_id = ns("or_parms"),
label = "Distribution of selection bias odds:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Log-logistic" = "log-logistic",
"Log-normal" = "log-normal"
),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.or_parms == "constant"',
ns = ns,
mod_parms2_ui(ns("parms_or_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.or_parms == "uniform"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_U"), "Minimum and maximum:",
0.35, 1.0, 0.01)
),
conditionalPanel(
condition = 'input.or_parms == "triangular"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_Tr1"),
"Lower and upper limit:", 0.35, 1.0, 0.01),
mod_parms2_ui(ns("parms_or_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.or_parms == "trapezoidal"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_Tz1"),
"Minimum and maximum:", 0.3, 1, 0.01),
mod_parmsrge2_ui(ns("parms_or_Tz2"),
"Lower and upper mode:", 0.4, 1, 0.01)
),
conditionalPanel(
condition = 'input.or_parms == "log-logistic"',
ns = ns,
mod_parms2b_ui(ns("parms_or_Ll1"), "Shape:", 4),
mod_parms2b_ui(ns("parms_or_Ll2"), "Rate:", 0.5)
),
conditionalPanel(
condition = 'input.or_parms == "log-normal"',
ns = ns,
mod_parms2c_ui(ns("parms_or_Ln1"), "Mean log:", 0),
mod_parms2c_ui(ns("parms_or_Ln2"), "SD log:", 0.5)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 1',
ns = ns,
material_dropdown(
input_id = ns("cexp_parms"),
label = "Distribution of selection probability among cases exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.cexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_cexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.cexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.cexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_cexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.cexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_cexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.cexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_cexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_cexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_cexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_cexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
material_dropdown(
input_id = ns("cnexp_parms"),
label = "Distribution of selection probability among cases unexposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.cnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_cnexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.cnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.cnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_cnexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.cnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_cnexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.cnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_cnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_cnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_cnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_cnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_dropdown(
input_id = ns("prevexp_parms"),
label = "Distribution of prevalence of exposure among exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.prevexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_prevexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.prevexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_prevexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.prevexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_prevexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.prevexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_prevexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_prevexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_prevexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 200, color = "#ff5131"),
material_number_box(ns("parms_prevexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
),
material_dropdown(
input_id = ns("prevnexp_parms"),
label = "Distribution of prevalence of exposure among non-exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.prevnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_prevnexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_prevnexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_Tz1"),
"Minimum and maximum:", 0.03, 0.06),
mod_parmsrge_ui(ns("parms_prevnexp_Tz2"),
"Lower and upper mode:", 0.04, 0.05)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_prevnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_prevnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_prevnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 10, color = "#ff5131"),
material_number_box(ns("parms_prevnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 16, color = "#ff5131")
)
)
)
),
material_column(
width = 4,
material_card(
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_dropdown(
input_id = ns("spca_parms"),
label = "Distribution, specificity of exposure classification among those with the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#9b0000"),
conditionalPanel(
condition = 'input.spca_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_spca_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.spca_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_spca_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.spca_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_spca_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.spca_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_spca_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spca_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spca_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_spca_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spca_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spca_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "beta"',
ns = ns,
material_number_box(ns("parms_spca_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_spca_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_dropdown(
input_id = ns("spexp_parms"),
label = "Distribution, specificity of exposure classification among those without the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#9b0000"),
conditionalPanel(
condition = 'input.spexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_spexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.spexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_spexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.spexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_spexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.spexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_spexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_spexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_spexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_spexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 1',
ns = ns,
material_dropdown(
input_id = ns("ncexp_parms"),
label = "Distribution of selection probability among noncases exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.ncexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_ncexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.ncexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.ncexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_ncexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.ncexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_ncexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.ncexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_ncexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_ncexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_ncexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_ncexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
material_dropdown(
input_id = ns("ncnexp_parms"),
label = "Distribution of selection probability among noncases unexposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.ncnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_ncnexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_ncnexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_ncnexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_ncnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_ncnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_ncnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_ncnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_dropdown(
input_id = ns("risk_parms"),
label = "Distribution of confounder-disease relative risk or confounder-exposure odds ratio:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal"),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.risk_parms == "constant"',
ns = ns,
mod_parms2_ui(ns("parms_risk_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.risk_parms == "uniform"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_U"), "Minimum and maximum:",
0.7, 0.9, 0.01)
),
conditionalPanel(
condition = 'input.risk_parms == "triangular"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_Tr1"),
"Lower and upper limit:", 0.6, 0.7, 0.01),
mod_parms2_ui(ns("parms_risk_Tr2"), "Mode:", 0.63)
),
conditionalPanel(
condition = 'input.risk_parms == "trapezoidal"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_Tz1"),
"Minimum and maximum:", 0.75, 1, 0.01),
mod_parmsrge2_ui(ns("parms_risk_Tz2"),
"Lower and upper mode:", 0.85, 0.95, 0.01)
),
conditionalPanel(
condition = 'input.risk_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_risk_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_risk_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_risk_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.risk_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_risk_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_risk_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_risk_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
)
)
)
),
material_column(
width = 8,
material_card(
verbatimTextOutput(ns("summary_prob")),
verbatimTextOutput(ns("warnings_prob")),
verbatimTextOutput(ns("message_prob")),
plotOutput(ns("plot_res"), width = "600px")
)
)
)
)
}
# Module Server
#' @rdname mod_prob
#' @noRd
#' @keywords internal
mod_prob_server <- function(input, output, session) {
ns <- session$ns
misclass_seed = reactive(input$chosen_seed)
## reactives can only be in observers or other reactives.
## setting priority to 5 helps set seed before `renderPrint` is executed
observe({
set.seed(misclass_seed())
}, priority = 5)
sel_seed = reactive(input$chosen_seed_sel)
observe({
set.seed(sel_seed())
}, priority = 5)
conf_seed = reactive(input$chosen_seed_conf)
observe({
set.seed(conf_seed())
}, priority = 5)
DF = reactive({
if (input$prob_type == "probsens") {
data.frame(Exposed = c(45, 257), Unexposed = c(94, 945),
row.names = c("Cases", "Noncases"))
} else if (input$prob_type == "probsens_sel") {
data.frame(Exposed = c(136, 297), Unexposed = c(107, 165),
row.names = c("Cases", "Noncases"))
} else if (input$prob_type == "probsens_conf") {
data.frame(Exposed = c(105, 527), Unexposed = c(85, 93),
row.names = c("Cases", "Noncases"))
}
})
output$two_by_two_prob = rhandsontable::renderRHandsontable({
input$reset_table # trigger rendering on reset
rhandsontable::rhandsontable(DF(), stretchH = "all", rowHeaderWidth = 75)
})
episensrout = reactive({
mat <- as.matrix(rhandsontable::hot_to_r(req({input$two_by_two_prob})))
if (input$seca_parms == "trapezoidal") {
dist_seca <- c(callModule(mod_parmsrge_server,
"parms_seca_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_seca_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_seca_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_seca_Tz1")[2])
} else if (input$seca_parms == "logit-logistic") {
dist_seca <- c(callModule(mod_parms2a_server,
"parms_seca_Ll1"),
callModule(mod_parms2a_server,
"parms_seca_Ll2"),
callModule(mod_parmsrge_server,
"parms_seca_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_seca_Ll3")[2])
} else if (input$seca_parms == "logit-normal") {
dist_seca <- c(callModule(mod_parms2a_server,
"parms_seca_Ln1"),
callModule(mod_parms2a_server,
"parms_seca_Ln2"),
callModule(mod_parmsrge_server,
"parms_seca_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_seca_Ln3")[2])
} else if (input$seca_parms == "triangular") {
dist_seca <- c(callModule(mod_parmsrge_server,
"parms_seca_Tr1"),
callModule(mod_parms_server,
"parms_seca_Tr2"))
} else if (input$seca_parms == "uniform") {
dist_seca <- callModule(mod_parmsrge_server,
"parms_seca_U")
} else if (input$seca_parms == "constant") {
dist_seca <- callModule(mod_parms_server, "parms_seca_C")
} else if (input$seca_parms == "beta") {
dist_seca <- c(input$parms_seca_B1, input$parms_seca_B2)
}
if (input$seexp_parms == "trapezoidal") {
dist_seexp <- c(callModule(mod_parmsrge_server,
"parms_seexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_seexp_Tz1")[2])
} else if (input$seexp_parms == "logit-logistic") {
dist_seexp <- c(callModule(mod_parms2a_server,
"parms_seexp_Ll1"),
callModule(mod_parms2a_server,
"parms_seexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_seexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Ll3")[2])
} else if (input$seexp_parms == "logit-normal") {
dist_seexp <- c(callModule(mod_parms2a_server,
"parms_seexp_Ln1"),
callModule(mod_parms2a_server,
"parms_seexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_seexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Ln3")[2])
} else if (input$seexp_parms == "triangular") {
dist_seexp <- c(callModule(mod_parmsrge_server,
"parms_seexp_Tr1"),
callModule(mod_parms_server,
"parms_seexp_Tr2"))
} else if (input$seexp_parms == "uniform") {
dist_seexp <- callModule(mod_parmsrge_server,
"parms_seexp_U")
} else if (input$seexp_parms == "constant") {
dist_seexp <- callModule(mod_parms_server,
"parms_seexp_C")
} else if (input$seexp_parms == "beta") {
dist_seexp <- c(input$parms_seexp_B1,
input$parms_seexp_B2)
}
if (input$spca_parms == "trapezoidal") {
dist_spca <- c(callModule(mod_parmsrge_server,
"parms_spca_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_spca_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_spca_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_spca_Tz1")[2])
} else if (input$spca_parms == "logit-logistic") {
dist_spca <- c(callModule(mod_parms2a_server,
"parms_spca_Ll1"),
callModule(mod_parms2a_server,
"parms_spca_Ll2"),
callModule(mod_parmsrge_server,
"parms_spca_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_spca_Ll3")[2])
} else if (input$spca_parms == "logit-normal") {
dist_spca <- c(callModule(mod_parms2a_server,
"parms_spca_Ln1"),
callModule(mod_parms2a_server,
"parms_spca_Ln2"),
callModule(mod_parmsrge_server,
"parms_spca_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_spca_Ln3")[2])
} else if (input$spca_parms == "triangular") {
dist_spca <- c(callModule(mod_parmsrge_server,
"parms_spca_Tr1"),
callModule(mod_parms_server,
"parms_spca_Tr2"))
} else if (input$spca_parms == "uniform") {
dist_spca <- callModule(mod_parmsrge_server,
"parms_spca_U")
} else if (input$spca_parms == "constant") {
dist_spca <- callModule(mod_parms_server, "parms_spca_C")
} else if (input$spca_parms == "beta") {
dist_spca <- c(input$parms_spca_B1, input$parms_spca_B2)
}
if (input$spexp_parms == "trapezoidal") {
dist_spexp <- c(callModule(mod_parmsrge_server,
"parms_spexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_spexp_Tz1")[2])
} else if (input$spexp_parms == "logit-logistic") {
dist_spexp <- c(callModule(mod_parms2a_server,
"parms_spexp_Ll1"),
callModule(mod_parms2a_server,
"parms_spexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_spexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Ll3")[2])
} else if (input$spexp_parms == "logit-normal") {
dist_spexp <- c(callModule(mod_parms2a_server,
"parms_spexp_Ln1"),
callModule(mod_parms2a_server,
"parms_spexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_spexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Ln3")[2])
} else if (input$spexp_parms == "triangular") {
dist_spexp <- c(callModule(mod_parmsrge_server,
"parms_spexp_Tr1"),
callModule(mod_parms_server,
"parms_spexp_Tr2"))
} else if (input$spexp_parms == "uniform") {
dist_spexp <- callModule(mod_parmsrge_server,
"parms_spexp_U")
} else if (input$spexp_parms == "constant") {
dist_spexp <- callModule(mod_parms_server,
"parms_spexp_C")
} else if (input$spexp_parms == "beta") {
dist_spexp <- c(input$parms_spexp_B1,
input$parms_spexp_B2)
}
if (input$or_parms == "constant") {
dist_orparms <- callModule(mod_parms_server,
"parms_or_C")
} else if (input$or_parms == "uniform") {
dist_orparms <- callModule(mod_parmsrge2_server,
"parms_or_U")
} else if (input$or_parms == "triangular") {
dist_orparms <- c(callModule(mod_parmsrge2_server,
"parms_or_Tr1"),
callModule(mod_parms2_server,
"parms_or_Tr2"))
} else if (input$or_parms == "trapezoidal") {
dist_orparms <- c(callModule(mod_parmsrge2_server,
"parms_or_Tz1")[1],
callModule(mod_parmsrge2_server,
"parms_or_Tz2")[1],
callModule(mod_parmsrge2_server,
"parms_or_Tz2")[2],
callModule(mod_parmsrge2_server,
"parms_or_Tz1")[2])
} else if (input$or_parms == "log-logistic") {
dist_orparms <- c(callModule(mod_parms2_server,
"parms_or_Ll1"),
callModule(mod_parms2_server,
"parms_or_Ll2"))
} else if (input$or_parms == "log-normal") {
dist_orparms <- c(callModule(mod_parms2c_server,
"parms_or_Ln1"),
callModule(mod_parms2c_server,
"parms_or_Ln2"))
}
if (input$cexp_parms == "constant") {
dist_cexp <- callModule(mod_parms_server,
"parms_cexp_C")
} else if (input$cexp_parms == "uniform") {
dist_cexp <- callModule(mod_parmsrge_server,
"parms_cexp_U")
} else if (input$cexp_parms == "triangular") {
dist_cexp <- c(callModule(mod_parmsrge_server,
"parms_cexp_Tr1"),
callModule(mod_parms_server,
"parms_cexp_Tr2"))
} else if (input$cexp_parms == "trapezoidal") {
dist_cexp <- c(callModule(mod_parmsrge_server,
"parms_cexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_cexp_Tz1")[2])
} else if (input$cexp_parms == "logit-logistic") {
dist_cexp <- c(callModule(mod_parms2a_server,
"parms_cexp_Ll1"),
callModule(mod_parms2a_server,
"parms_cexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_cexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Ll3")[2])
} else if (input$cexp_parms == "logit-normal") {
dist_cexp <- c(callModule(mod_parms2a_server,
"parms_cexp_Ln1"),
callModule(mod_parms2a_server,
"parms_cexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_cexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Ln3")[2])
} else if (input$cexp_parms == "beta") {
dist_cexp <- c(input$parms_cexp_B1,
input$parms_cexp_B2)
}
if (input$cnexp_parms == "constant") {
dist_cnexp <- callModule(mod_parms_server,
"parms_cnexp_C")
} else if (input$cnexp_parms == "uniform") {
dist_cnexp <- callModule(mod_parmsrge_server,
"parms_cnexp_U")
} else if (input$cnexp_parms == "triangular") {
dist_cnexp <- c(callModule(mod_parmsrge_server,
"parms_cnexp_Tr1"),
callModule(mod_parms_server,
"parms_cnexp_Tr2"))
} else if (input$cnexp_parms == "trapezoidal") {
dist_cnexp <- c(callModule(mod_parmsrge_server,
"parms_cnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz1")[2])
} else if (input$cnexp_parms == "logit-logistic") {
dist_cnexp <- c(callModule(mod_parms2a_server,
"parms_cnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_cnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_cnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Ll3")[2])
} else if (input$cnexp_parms == "logit-normal") {
dist_cnexp <- c(callModule(mod_parms2a_server,
"parms_cnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_cnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_cnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Ln3")[2])
} else if (input$cnexp_parms == "beta") {
dist_cnexp <- c(input$parms_cnexp_B1,
input$parms_cnexp_B2)
}
if (input$ncexp_parms == "constant") {
dist_ncexp <- callModule(mod_parms_server,
"parms_ncexp_C")
} else if (input$ncexp_parms == "uniform") {
dist_ncexp <- callModule(mod_parmsrge_server,
"parms_ncexp_U")
} else if (input$ncexp_parms == "triangular") {
dist_ncexp <- c(callModule(mod_parmsrge_server,
"parms_ncexp_Tr1"),
callModule(mod_parms_server,
"parms_ncexp_Tr2"))
} else if (input$ncexp_parms == "trapezoidal") {
dist_ncexp <- c(callModule(mod_parmsrge_server,
"parms_ncexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz1")[2])
} else if (input$ncexp_parms == "logit-logistic") {
dist_ncexp <- c(callModule(mod_parms2a_server,
"parms_ncexp_Ll1"),
callModule(mod_parms2a_server,
"parms_ncexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_ncexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Ll3")[2])
} else if (input$ncexp_parms == "logit-normal") {
dist_ncexp <- c(callModule(mod_parms2a_server,
"parms_ncexp_Ln1"),
callModule(mod_parms2a_server,
"parms_ncexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_ncexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Ln3")[2])
} else if (input$ncexp_parms == "beta") {
dist_ncexp <- c(input$parms_ncexp_B1,
input$parms_ncexp_B2)
}
if (input$ncnexp_parms == "constant") {
dist_ncnexp <- callModule(mod_parms_server,
"parms_ncnexp_C")
} else if (input$ncnexp_parms == "uniform") {
dist_ncnexp <- callModule(mod_parmsrge_server,
"parms_ncnexp_U")
} else if (input$ncnexp_parms == "triangular") {
dist_ncnexp <- c(callModule(mod_parmsrge_server,
"parms_ncnexp_Tr1"),
callModule(mod_parms_server,
"parms_ncnexp_Tr2"))
} else if (input$ncnexp_parms == "trapezoidal") {
dist_ncnexp <- c(callModule(mod_parmsrge_server,
"parms_ncnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz1")[2])
} else if (input$ncnexp_parms == "logit-logistic") {
dist_ncnexp <- c(callModule(mod_parms2a_server,
"parms_ncnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_ncnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_ncnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Ll3")[2])
} else if (input$ncnexp_parms == "logit-normal") {
dist_ncnexp <- c(callModule(mod_parms2a_server,
"parms_ncnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_ncnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_ncnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Ln3")[2])
} else if (input$ncnexp_parms == "beta") {
dist_ncnexp <- c(input$parms_ncnexp_B1,
input$parms_ncnexp_B2)
}
if (input$prevexp_parms == "constant") {
dist_prevexp <- callModule(mod_parms_server,
"parms_prevexp_C")
} else if (input$prevexp_parms == "uniform") {
dist_prevexp <- callModule(mod_parmsrge_server,
"parms_prevexp_U")
} else if (input$prevexp_parms == "triangular") {
dist_prevexp <- c(callModule(mod_parmsrge_server,
"parms_prevexp_Tr1"),
callModule(mod_parms_server,
"parms_prevexp_Tr2"))
} else if (input$prevexp_parms == "trapezoidal") {
dist_prevexp <- c(callModule(mod_parmsrge_server,
"parms_prevexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz1")[2])
} else if (input$prevexp_parms == "logit-logistic") {
dist_prevexp <- c(callModule(mod_parms2a_server,
"parms_prevexp_Ll1"),
callModule(mod_parms2a_server,
"parms_prevexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_prevexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Ll3")[2])
} else if (input$prevexp_parms == "logit-normal") {
dist_prevexp <- c(callModule(mod_parms2a_server,
"parms_prevexp_Ln1"),
callModule(mod_parms2a_server,
"parms_prevexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_prevexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Ln3")[2])
} else if (input$prevexp_parms == "beta") {
dist_prevexp <- c(input$parms_prevexp_B1,
input$parms_prevexp_B2)
}
if (input$prevnexp_parms == "constant") {
dist_prevnexp <- callModule(mod_parms_server,
"parms_prevnexp_C")
} else if (input$prevnexp_parms == "uniform") {
dist_prevnexp <- callModule(mod_parmsrge_server,
"parms_prevnexp_U")
} else if (input$prevnexp_parms == "triangular") {
dist_prevnexp <- c(callModule(mod_parmsrge_server,
"parms_prevnexp_Tr1"),
callModule(mod_parms_server,
"parms_prevnexp_Tr2"))
} else if (input$prevnexp_parms == "trapezoidal") {
dist_prevnexp <- c(callModule(mod_parmsrge_server,
"parms_prevnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz1")[2])
} else if (input$prevnexp_parms == "logit-logistic") {
dist_prevnexp <- c(callModule(mod_parms2a_server,
"parms_prevnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_prevnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_prevnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Ll3")[2])
} else if (input$prevnexp_parms == "logit-normal") {
dist_prevnexp <- c(callModule(mod_parms2a_server,
"parms_prevnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_prevnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_prevnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Ln3")[2])
} else if (input$prevnexp_parms == "beta") {
dist_prevnexp <- c(input$parms_prevnexp_B1,
input$parms_prevnexp_B2)
}
if (input$risk_parms == "constant") {
dist_risk <- callModule(mod_parms2_server,
"parms_risk_C")
} else if (input$risk_parms == "uniform") {
dist_risk <- callModule(mod_parmsrge2_server,
"parms_risk_U")
} else if (input$risk_parms == "triangular") {
dist_risk <- c(callModule(mod_parmsrge2_server,
"parms_risk_Tr1"),
callModule(mod_parms2_server,
"parms_risk_Tr2"))
} else if (input$risk_parms == "trapezoidal") {
dist_risk <- c(callModule(mod_parmsrge2_server,
"parms_risk_Tz1")[1],
callModule(mod_parmsrge2_server,
"parms_risk_Tz2")[1],
callModule(mod_parmsrge2_server,
"parms_risk_Tz2")[2],
callModule(mod_parmsrge2_server,
"parms_risk_Tz1")[2])
} else if (input$risk_parms == "logit-logistic") {
dist_risk <- c(callModule(mod_parms2a_server,
"parms_risk_Ll1"),
callModule(mod_parms2a_server,
"parms_risk_Ll2"),
callModule(mod_parmsrge_server,
"parms_risk_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_risk_Ll3")[2])
} else if (input$risk_parms == "logit-normal") {
dist_risk <- c(callModule(mod_parms2a_server,
"parms_risk_Ln1"),
callModule(mod_parms2a_server,
"parms_risk_Ln2"),
callModule(mod_parmsrge_server,
"parms_risk_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_risk_Ln3")[2])
}
if (input$discard == 0) {
throw_away <- FALSE
} else throw_away <- TRUE
if (input$discard_conf == 0) {
throw_away_conf <- FALSE
} else throw_away_conf <- TRUE
if (input$prob_type == "probsens" & input$diff == 0) {
set.seed(misclass_seed())
episensr::probsens(mat,
type = input$misclassProb_type,
reps = input$reps,
seca.parms = list(input$seca_parms,
dist_seca),
spca.parms = list(input$spca_parms,
dist_spca),
discard = throw_away,
alpha = input$alpha)
} else if (input$prob_type == "probsens" & input$diff == 1) {
set.seed(misclass_seed())
episensr::probsens(mat,
type = input$misclassProb_type,
reps = input$reps,
seca.parms = list(input$seca_parms,
dist_seca),
seexp.parms = list(input$seexp_parms,
dist_seexp),
spca.parms = list(input$spca_parms,
dist_spca),
spexp.parms = list(input$spexp_parms,
dist_spexp),
corr.se = input$corr_se,
corr.sp = input$corr_sp,
discard = throw_away,
alpha = input$alpha)
} else if (input$prob_type == "probsens_sel" &
input$or_case == 0) {
set.seed(sel_seed())
episensr::probsens.sel(mat,
reps = input$reps_sel,
or.parms = list(input$or_parms,
dist_orparms),
alpha = input$alpha)
} else if (input$prob_type == "probsens_sel" &
input$or_case == 1) {
set.seed(sel_seed())
episensr::probsens.sel(mat,
reps = input$reps_sel,
case.exp = list(input$cexp_parms,
dist_cexp),
case.nexp = list(input$cnexp_parms,
dist_cnexp),
ncase.exp = list(input$ncexp_parms,
dist_ncexp),
ncase.nexp = list(input$ncnexp_parms,
dist_ncnexp),
alpha = input$alpha)
} else if (input$prob_type == "probsens_conf") {
set.seed(conf_seed())
episensr::probsens.conf(mat,
reps = input$reps_conf,
prev.exp = list(input$prevexp_parms,
dist_prevexp),
prev.nexp = list(input$prevnexp_parms,
dist_prevnexp),
risk = list(input$risk_parms,
dist_risk),
corr.p = input$corr_conf,
discard = throw_away_conf,
alpha = input$alpha)
}
})
plotout = reactive({
if (input$prob_type == "probsens") {
plot(episensrout(), input$plot_probsens)
} else if (input$prob_type == "probsens_sel") {
plot(episensrout(), input$plot_probsens_sel)
} else if (input$prob_type == "probsens_conf") {
plot(episensrout(), input$plot_probsens_conf)
}
})
## Output
output$summary_prob = renderPrint({
episensrout()
})
output$warnings_prob = renderText({
invisible(episensrout()$warnings)
})
output$message_prob = renderText({
invisible(episensrout()$message)
})
output$plot_res <- renderPlot({
plotout()
})
shinyjs::runjs("document.getElementById('help_probsens').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.html', '_blank');
};"
)
shinyjs::runjs("document.getElementById('help_probsens_sel').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.sel.html', '_blank');
};"
)
shinyjs::runjs("document.getElementById('help_probsens_conf').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.conf.html', '_blank');
};"
)
}
## To be copied in the UI
# mod_prob_ui("tab_prob")
## To be copied in the server
# callModule(mod_prob_server, "tab_prob")
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Defines functions mod_prob_server mod_prob_ui
# Probabilistic Analysis Module UI
#' @title UI Module for Probabilistic Analysis tab
#' @description A shiny Module to render the Probabilistic Analysis tab (selection bias
#' analysis `probsens.sel`, bias analysis for unmeasured confounder `probsens.conf`, and
#' misclassification bias analysis `probsens`).
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_prob
#'
#' @keywords internal
#' @noRd
#' @import episensr
#' @import ggplot2
#' @importFrom shiny NS tagList
#' @importFrom shinyjs runjs
#' @importFrom rhandsontable hot_to_r rHandsontableOutput renderRHandsontable rhandsontable
mod_prob_ui <- function(id, label = "tab_prob") {
ns <- NS(id)
material_tab_content(
tab_id = id,
material_row(
material_column(
width = 4,
material_card(
material_dropdown(
ns("prob_type"),
label = "Choose bias analysis:",
choices = c(
"Selection bias" = "probsens_sel",
"Unmeasured confounder" = "probsens_conf",
"Misclassification bias" = "probsens"
),
color = "#d50000"
),
"Observed data",
div(id = "obs-table-prob",
rhandsontable::rHandsontableOutput(ns('two_by_two_prob')),
material_button(
input_id = ns("reset_table"),
label = "Table back to example",
icon = "restore",
color = "red accent-4"
)
),
br(),
div(
id = "side-panel-prob",
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_radio_button(
input_id = ns("misclassProb_type"),
label = "Misclassification of:",
choices = c("exposure", "outcome"),
selected = "exposure",
color = "#ff5131"),
br(),
material_number_box(
input_id = ns("chosen_seed"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_switch(
input_id = ns("diff"),
off_label = "Non-differential misclassification",
on_label = "Differential misclassification",
initial_value = FALSE,
color = "#ff5131"),
br(),
material_checkbox(
input_id = ns("discard"),
label = "Discard draws of negative adjusted counts.",
initial_value = TRUE,
color = "#9b0000"),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_slider(
input_id = ns("corr_se"),
label = "Correlation between case and non-case sensitivities:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "#ff5131"
),
material_slider(
input_id = ns("corr_sp"),
label = "Correlation between case and non-case specificities:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "9b0000"
)
),
material_button(
input_id = "help_probsens",
label = "Help",
icon = "help",
color = "orange"),
br(),
material_dropdown(
input_id = ns("plot_probsens"),
label = "Plot of output/parameters:",
choices = c(
"Relative risk - systematic error" = "rr",
"Odds ratio - systematic error" = "or",
"Relative risk - systematic and random error" = "rr_tot",
"Odds ratio - systematic and random error" = "or_tot",
"Sensitivity for cases" = "seca",
"Sensitivity of exposed" = "seexp",
"Specificity for cases" = "spca",
"Specificity of exposed" = "spexp"),
selected = "rr",
color = "#ff5131")
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel"',
ns = ns,
material_number_box(
input_id = ns("chosen_seed_sel"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps_sel"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_switch(
input_id = ns("or_case"),
off_label = "Using selection bias odds",
on_label = "Using selection probabilities",
initial_value = FALSE,
color = "#ff5131"),
br(),
material_button(
input_id = "help_probsens_sel",
label = "Help",
icon = "help",
color = "orange"),
material_dropdown(
input_id = ns("plot_probsens_sel"),
label = "Plot of output/parameters",
choices = c(
"Odds ratio - systematic error" = "or",
"Odds ratio - systematic and random error" = "or_tot",
"Selection odds ratio" = "or_sel"),
selected = "or",
color = "#ff5131")
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_number_box(
input_id = ns("chosen_seed_conf"),
label = "Please select a seed:",
min_value = 1,
max_value = 1000000,
initial_value = 1,
color = "#ff5131"
),
material_slider(
input_id = ns("reps_conf"),
label = "Number of replications to run:",
min_value = 10000,
max_value = 100000,
step_size = 5000,
initial_value = 25000,
color = "#ff5131"),
material_slider(
input_id = ns("corr_conf"),
label = "Correlation between the exposure-specific confounder prevalences:",
min_value = 0,
max_value = 1,
step_size = 0.1,
initial_value = 0.8,
color = "#ff5131"),
material_checkbox(
input_id = ns("discard_conf"),
label = "Discard draws of negative adjusted counts.",
initial_value = TRUE,
color = "#ff5131"),
br(),
material_button(
input_id = "help_probsens_conf",
label = "Help",
icon = "help",
color = "orange"),
material_dropdown(
input_id = ns("plot_probsens_conf"),
label = "Plot of output/parameters",
choices = c(
"Relative risk - systematic error" = "rr",
"Odds ratio - systematic error" = "or",
"Relative risk - systematic and random error" = "rr_tot",
"Odds ratio - systematic and random error" = "or_tot",
"Distribution of prevalence of exposure among the exposed" = "prev.exp",
"Distribution of prevalence of exposure among the unexposed" = "prev.nexp",
"Distribution of confounder-disease relative risk (or confounder-exposure odds ratio)" = "risk"),
selected = "rr",
color = "#9b0000")
),
## Alpha level
material_slider(
ns("alpha"),
HTML("α-level:"),
min_value = 0.01,
max_value = 0.2,
step_size = 0.01,
initial_value = 0.05,
color = "#ff5131")
)
)
),
material_column(
width = 4,
material_card(
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_dropdown(
input_id = ns("seca_parms"),
label = "Distribution, sensitivity of exposure classification among those with the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.seca_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_seca_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.seca_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_seca_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.seca_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_seca_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_seca_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.seca_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_seca_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seca_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seca_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_seca_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seca_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seca_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seca_parms == "beta"',
ns = ns,
material_number_box(ns("parms_seca_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_seca_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_dropdown(
input_id = ns("seexp_parms"),
label = "Distribution, sensitivity of exposure classification among those without the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.seexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_seexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.seexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_seexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.seexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_seexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_seexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.seexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_seexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_seexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_seexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_seexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.seexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_seexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_seexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 0',
ns = ns,
material_dropdown(
input_id = ns("or_parms"),
label = "Distribution of selection bias odds:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Log-logistic" = "log-logistic",
"Log-normal" = "log-normal"
),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.or_parms == "constant"',
ns = ns,
mod_parms2_ui(ns("parms_or_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.or_parms == "uniform"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_U"), "Minimum and maximum:",
0.35, 1.0, 0.01)
),
conditionalPanel(
condition = 'input.or_parms == "triangular"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_Tr1"),
"Lower and upper limit:", 0.35, 1.0, 0.01),
mod_parms2_ui(ns("parms_or_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.or_parms == "trapezoidal"',
ns = ns,
mod_parmsrge2_ui(ns("parms_or_Tz1"),
"Minimum and maximum:", 0.3, 1, 0.01),
mod_parmsrge2_ui(ns("parms_or_Tz2"),
"Lower and upper mode:", 0.4, 1, 0.01)
),
conditionalPanel(
condition = 'input.or_parms == "log-logistic"',
ns = ns,
mod_parms2b_ui(ns("parms_or_Ll1"), "Shape:", 4),
mod_parms2b_ui(ns("parms_or_Ll2"), "Rate:", 0.5)
),
conditionalPanel(
condition = 'input.or_parms == "log-normal"',
ns = ns,
mod_parms2c_ui(ns("parms_or_Ln1"), "Mean log:", 0),
mod_parms2c_ui(ns("parms_or_Ln2"), "SD log:", 0.5)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 1',
ns = ns,
material_dropdown(
input_id = ns("cexp_parms"),
label = "Distribution of selection probability among cases exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.cexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_cexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.cexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.cexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_cexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.cexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_cexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_cexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.cexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_cexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_cexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_cexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_cexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
material_dropdown(
input_id = ns("cnexp_parms"),
label = "Distribution of selection probability among cases unexposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.cnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_cnexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.cnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.cnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_cnexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.cnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_cnexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_cnexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.cnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_cnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_cnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_cnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_cnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.cnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_cnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_cnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_dropdown(
input_id = ns("prevexp_parms"),
label = "Distribution of prevalence of exposure among exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "triangular",
color = "#ff5131"),
conditionalPanel(
condition = 'input.prevexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_prevexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.prevexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_prevexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.prevexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_prevexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.prevexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_prevexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_prevexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_prevexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 200, color = "#ff5131"),
material_number_box(ns("parms_prevexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
),
material_dropdown(
input_id = ns("prevnexp_parms"),
label = "Distribution of prevalence of exposure among non-exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#ff5131"),
conditionalPanel(
condition = 'input.prevnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_prevnexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_prevnexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_prevnexp_Tz1"),
"Minimum and maximum:", 0.03, 0.06),
mod_parmsrge_ui(ns("parms_prevnexp_Tz2"),
"Lower and upper mode:", 0.04, 0.05)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_prevnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_prevnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_prevnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_prevnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.prevnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_prevnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 10, color = "#ff5131"),
material_number_box(ns("parms_prevnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 16, color = "#ff5131")
)
)
)
),
material_column(
width = 4,
material_card(
conditionalPanel(
condition = 'input.prob_type == "probsens"',
ns = ns,
material_dropdown(
input_id = ns("spca_parms"),
label = "Distribution, specificity of exposure classification among those with the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#9b0000"),
conditionalPanel(
condition = 'input.spca_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_spca_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.spca_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_spca_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.spca_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_spca_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_spca_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.spca_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_spca_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spca_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spca_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_spca_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spca_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spca_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spca_parms == "beta"',
ns = ns,
material_number_box(ns("parms_spca_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_spca_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
conditionalPanel(
condition = 'input.diff == 1',
ns = ns,
material_dropdown(
input_id = ns("spexp_parms"),
label = "Distribution, specificity of exposure classification among those without the outcome:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"),
selected = "trapezoidal",
color = "#9b0000"),
conditionalPanel(
condition = 'input.spexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_spexp_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.spexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_U"), "Minimum and maximum:",
0.7, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_Tr1"),
"Lower and upper limit:", 0.7, 0.9),
mod_parms_ui(ns("parms_spexp_Tr2"), "Mode:", 0.8)
),
conditionalPanel(
condition = 'input.spexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_spexp_Tz1"),
"Minimum and maximum:", 0.75, 1),
mod_parmsrge_ui(ns("parms_spexp_Tz2"),
"Lower and upper mode:", 0.85, 0.95)
),
conditionalPanel(
condition = 'input.spexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_spexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_spexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_spexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_spexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.spexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_spexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 908, color = "#ff5131"),
material_number_box(ns("parms_spexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 56, color = "#ff5131")
)
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_sel" & input.or_case == 1',
ns = ns,
material_dropdown(
input_id = ns("ncexp_parms"),
label = "Distribution of selection probability among noncases exposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.ncexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_ncexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.ncexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.ncexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_ncexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.ncexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_ncexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.ncexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_ncexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_ncexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_ncexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_ncexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
),
material_dropdown(
input_id = ns("ncnexp_parms"),
label = "Distribution of selection probability among noncases unexposed:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal",
"Beta" = "beta"
),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.ncnexp_parms == "constant"',
ns = ns,
mod_parms_ui(ns("parms_ncnexp_C"), "Constant value:", 0.43)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "uniform"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_U"), "Minimum and maximum:",
0.35, 1.0)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "triangular"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_Tr1"),
"Lower and upper limit:", 0.35, 1.0),
mod_parms_ui(ns("parms_ncnexp_Tr2"), "Mode:", 0.43)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "trapezoidal"',
ns = ns,
mod_parmsrge_ui(ns("parms_ncnexp_Tz1"),
"Minimum and maximum:", 0.3, 1),
mod_parmsrge_ui(ns("parms_ncnexp_Tz2"),
"Lower and upper mode:", 0.4, 1)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_ncnexp_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncnexp_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncnexp_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_ncnexp_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_ncnexp_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_ncnexp_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.ncnexp_parms == "beta"',
ns = ns,
material_number_box(ns("parms_ncnexp_B1"),
"alpha:", min_value = 0, max_value = 10^6,
initial_value = 153, color = "#9b0000"),
material_number_box(ns("parms_ncnexp_B2"),
"beta:", min_value = 0, max_value = 10^6,
initial_value = 6, color = "#9b0000")
)
),
conditionalPanel(
condition = 'input.prob_type == "probsens_conf"',
ns = ns,
material_dropdown(
input_id = ns("risk_parms"),
label = "Distribution of confounder-disease relative risk or confounder-exposure odds ratio:",
choices = c(
"Constant" = "constant",
"Uniform" = "uniform",
"Triangular" = "triangular",
"Trapezoidal" = "trapezoidal",
"Logit-logistic" = "logit-logistic",
"Logit-normal" = "logit-normal"),
selected = "triangular",
color = "#9b0000"),
conditionalPanel(
condition = 'input.risk_parms == "constant"',
ns = ns,
mod_parms2_ui(ns("parms_risk_C"), "Constant value:", 0.8)
),
conditionalPanel(
condition = 'input.risk_parms == "uniform"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_U"), "Minimum and maximum:",
0.7, 0.9, 0.01)
),
conditionalPanel(
condition = 'input.risk_parms == "triangular"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_Tr1"),
"Lower and upper limit:", 0.6, 0.7, 0.01),
mod_parms2_ui(ns("parms_risk_Tr2"), "Mode:", 0.63)
),
conditionalPanel(
condition = 'input.risk_parms == "trapezoidal"',
ns = ns,
mod_parmsrge2_ui(ns("parms_risk_Tz1"),
"Minimum and maximum:", 0.75, 1, 0.01),
mod_parmsrge2_ui(ns("parms_risk_Tz2"),
"Lower and upper mode:", 0.85, 0.95, 0.01)
),
conditionalPanel(
condition = 'input.risk_parms == "logit-logistic"',
ns = ns,
mod_parms2a_ui(ns("parms_risk_Ll1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_risk_Ll2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_risk_Ll3"),
"Lower and upper bound shift:", 0.5, 0.9)
),
conditionalPanel(
condition = 'input.risk_parms == "logit-normal"',
ns = ns,
mod_parms2a_ui(ns("parms_risk_Ln1"), "Location:", 0, -5, 5),
mod_parms2a_ui(ns("parms_risk_Ln2"), "Scale:", 0.8, -10, 10),
mod_parmsrge_ui(ns("parms_risk_Ln3"),
"Lower and upper bound shift:", 0.5, 0.9)
)
)
)
),
material_column(
width = 8,
material_card(
verbatimTextOutput(ns("summary_prob")),
verbatimTextOutput(ns("warnings_prob")),
verbatimTextOutput(ns("message_prob")),
plotOutput(ns("plot_res"), width = "600px")
)
)
)
)
}
# Module Server
#' @rdname mod_prob
#' @noRd
#' @keywords internal
mod_prob_server <- function(input, output, session) {
ns <- session$ns
misclass_seed = reactive(input$chosen_seed)
## reactives can only be in observers or other reactives.
## setting priority to 5 helps set seed before `renderPrint` is executed
observe({
set.seed(misclass_seed())
}, priority = 5)
sel_seed = reactive(input$chosen_seed_sel)
observe({
set.seed(sel_seed())
}, priority = 5)
conf_seed = reactive(input$chosen_seed_conf)
observe({
set.seed(conf_seed())
}, priority = 5)
DF = reactive({
if (input$prob_type == "probsens") {
data.frame(Exposed = c(45, 257), Unexposed = c(94, 945),
row.names = c("Cases", "Noncases"))
} else if (input$prob_type == "probsens_sel") {
data.frame(Exposed = c(136, 297), Unexposed = c(107, 165),
row.names = c("Cases", "Noncases"))
} else if (input$prob_type == "probsens_conf") {
data.frame(Exposed = c(105, 527), Unexposed = c(85, 93),
row.names = c("Cases", "Noncases"))
}
})
output$two_by_two_prob = rhandsontable::renderRHandsontable({
input$reset_table # trigger rendering on reset
rhandsontable::rhandsontable(DF(), stretchH = "all", rowHeaderWidth = 75)
})
episensrout = reactive({
mat <- as.matrix(rhandsontable::hot_to_r(req({input$two_by_two_prob})))
if (input$seca_parms == "trapezoidal") {
dist_seca <- c(callModule(mod_parmsrge_server,
"parms_seca_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_seca_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_seca_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_seca_Tz1")[2])
} else if (input$seca_parms == "logit-logistic") {
dist_seca <- c(callModule(mod_parms2a_server,
"parms_seca_Ll1"),
callModule(mod_parms2a_server,
"parms_seca_Ll2"),
callModule(mod_parmsrge_server,
"parms_seca_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_seca_Ll3")[2])
} else if (input$seca_parms == "logit-normal") {
dist_seca <- c(callModule(mod_parms2a_server,
"parms_seca_Ln1"),
callModule(mod_parms2a_server,
"parms_seca_Ln2"),
callModule(mod_parmsrge_server,
"parms_seca_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_seca_Ln3")[2])
} else if (input$seca_parms == "triangular") {
dist_seca <- c(callModule(mod_parmsrge_server,
"parms_seca_Tr1"),
callModule(mod_parms_server,
"parms_seca_Tr2"))
} else if (input$seca_parms == "uniform") {
dist_seca <- callModule(mod_parmsrge_server,
"parms_seca_U")
} else if (input$seca_parms == "constant") {
dist_seca <- callModule(mod_parms_server, "parms_seca_C")
} else if (input$seca_parms == "beta") {
dist_seca <- c(input$parms_seca_B1, input$parms_seca_B2)
}
if (input$seexp_parms == "trapezoidal") {
dist_seexp <- c(callModule(mod_parmsrge_server,
"parms_seexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_seexp_Tz1")[2])
} else if (input$seexp_parms == "logit-logistic") {
dist_seexp <- c(callModule(mod_parms2a_server,
"parms_seexp_Ll1"),
callModule(mod_parms2a_server,
"parms_seexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_seexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Ll3")[2])
} else if (input$seexp_parms == "logit-normal") {
dist_seexp <- c(callModule(mod_parms2a_server,
"parms_seexp_Ln1"),
callModule(mod_parms2a_server,
"parms_seexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_seexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_seexp_Ln3")[2])
} else if (input$seexp_parms == "triangular") {
dist_seexp <- c(callModule(mod_parmsrge_server,
"parms_seexp_Tr1"),
callModule(mod_parms_server,
"parms_seexp_Tr2"))
} else if (input$seexp_parms == "uniform") {
dist_seexp <- callModule(mod_parmsrge_server,
"parms_seexp_U")
} else if (input$seexp_parms == "constant") {
dist_seexp <- callModule(mod_parms_server,
"parms_seexp_C")
} else if (input$seexp_parms == "beta") {
dist_seexp <- c(input$parms_seexp_B1,
input$parms_seexp_B2)
}
if (input$spca_parms == "trapezoidal") {
dist_spca <- c(callModule(mod_parmsrge_server,
"parms_spca_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_spca_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_spca_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_spca_Tz1")[2])
} else if (input$spca_parms == "logit-logistic") {
dist_spca <- c(callModule(mod_parms2a_server,
"parms_spca_Ll1"),
callModule(mod_parms2a_server,
"parms_spca_Ll2"),
callModule(mod_parmsrge_server,
"parms_spca_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_spca_Ll3")[2])
} else if (input$spca_parms == "logit-normal") {
dist_spca <- c(callModule(mod_parms2a_server,
"parms_spca_Ln1"),
callModule(mod_parms2a_server,
"parms_spca_Ln2"),
callModule(mod_parmsrge_server,
"parms_spca_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_spca_Ln3")[2])
} else if (input$spca_parms == "triangular") {
dist_spca <- c(callModule(mod_parmsrge_server,
"parms_spca_Tr1"),
callModule(mod_parms_server,
"parms_spca_Tr2"))
} else if (input$spca_parms == "uniform") {
dist_spca <- callModule(mod_parmsrge_server,
"parms_spca_U")
} else if (input$spca_parms == "constant") {
dist_spca <- callModule(mod_parms_server, "parms_spca_C")
} else if (input$spca_parms == "beta") {
dist_spca <- c(input$parms_spca_B1, input$parms_spca_B2)
}
if (input$spexp_parms == "trapezoidal") {
dist_spexp <- c(callModule(mod_parmsrge_server,
"parms_spexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_spexp_Tz1")[2])
} else if (input$spexp_parms == "logit-logistic") {
dist_spexp <- c(callModule(mod_parms2a_server,
"parms_spexp_Ll1"),
callModule(mod_parms2a_server,
"parms_spexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_spexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Ll3")[2])
} else if (input$spexp_parms == "logit-normal") {
dist_spexp <- c(callModule(mod_parms2a_server,
"parms_spexp_Ln1"),
callModule(mod_parms2a_server,
"parms_spexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_spexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_spexp_Ln3")[2])
} else if (input$spexp_parms == "triangular") {
dist_spexp <- c(callModule(mod_parmsrge_server,
"parms_spexp_Tr1"),
callModule(mod_parms_server,
"parms_spexp_Tr2"))
} else if (input$spexp_parms == "uniform") {
dist_spexp <- callModule(mod_parmsrge_server,
"parms_spexp_U")
} else if (input$spexp_parms == "constant") {
dist_spexp <- callModule(mod_parms_server,
"parms_spexp_C")
} else if (input$spexp_parms == "beta") {
dist_spexp <- c(input$parms_spexp_B1,
input$parms_spexp_B2)
}
if (input$or_parms == "constant") {
dist_orparms <- callModule(mod_parms_server,
"parms_or_C")
} else if (input$or_parms == "uniform") {
dist_orparms <- callModule(mod_parmsrge2_server,
"parms_or_U")
} else if (input$or_parms == "triangular") {
dist_orparms <- c(callModule(mod_parmsrge2_server,
"parms_or_Tr1"),
callModule(mod_parms2_server,
"parms_or_Tr2"))
} else if (input$or_parms == "trapezoidal") {
dist_orparms <- c(callModule(mod_parmsrge2_server,
"parms_or_Tz1")[1],
callModule(mod_parmsrge2_server,
"parms_or_Tz2")[1],
callModule(mod_parmsrge2_server,
"parms_or_Tz2")[2],
callModule(mod_parmsrge2_server,
"parms_or_Tz1")[2])
} else if (input$or_parms == "log-logistic") {
dist_orparms <- c(callModule(mod_parms2_server,
"parms_or_Ll1"),
callModule(mod_parms2_server,
"parms_or_Ll2"))
} else if (input$or_parms == "log-normal") {
dist_orparms <- c(callModule(mod_parms2c_server,
"parms_or_Ln1"),
callModule(mod_parms2c_server,
"parms_or_Ln2"))
}
if (input$cexp_parms == "constant") {
dist_cexp <- callModule(mod_parms_server,
"parms_cexp_C")
} else if (input$cexp_parms == "uniform") {
dist_cexp <- callModule(mod_parmsrge_server,
"parms_cexp_U")
} else if (input$cexp_parms == "triangular") {
dist_cexp <- c(callModule(mod_parmsrge_server,
"parms_cexp_Tr1"),
callModule(mod_parms_server,
"parms_cexp_Tr2"))
} else if (input$cexp_parms == "trapezoidal") {
dist_cexp <- c(callModule(mod_parmsrge_server,
"parms_cexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_cexp_Tz1")[2])
} else if (input$cexp_parms == "logit-logistic") {
dist_cexp <- c(callModule(mod_parms2a_server,
"parms_cexp_Ll1"),
callModule(mod_parms2a_server,
"parms_cexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_cexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Ll3")[2])
} else if (input$cexp_parms == "logit-normal") {
dist_cexp <- c(callModule(mod_parms2a_server,
"parms_cexp_Ln1"),
callModule(mod_parms2a_server,
"parms_cexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_cexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_cexp_Ln3")[2])
} else if (input$cexp_parms == "beta") {
dist_cexp <- c(input$parms_cexp_B1,
input$parms_cexp_B2)
}
if (input$cnexp_parms == "constant") {
dist_cnexp <- callModule(mod_parms_server,
"parms_cnexp_C")
} else if (input$cnexp_parms == "uniform") {
dist_cnexp <- callModule(mod_parmsrge_server,
"parms_cnexp_U")
} else if (input$cnexp_parms == "triangular") {
dist_cnexp <- c(callModule(mod_parmsrge_server,
"parms_cnexp_Tr1"),
callModule(mod_parms_server,
"parms_cnexp_Tr2"))
} else if (input$cnexp_parms == "trapezoidal") {
dist_cnexp <- c(callModule(mod_parmsrge_server,
"parms_cnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_cnexp_Tz1")[2])
} else if (input$cnexp_parms == "logit-logistic") {
dist_cnexp <- c(callModule(mod_parms2a_server,
"parms_cnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_cnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_cnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Ll3")[2])
} else if (input$cnexp_parms == "logit-normal") {
dist_cnexp <- c(callModule(mod_parms2a_server,
"parms_cnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_cnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_cnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_cnexp_Ln3")[2])
} else if (input$cnexp_parms == "beta") {
dist_cnexp <- c(input$parms_cnexp_B1,
input$parms_cnexp_B2)
}
if (input$ncexp_parms == "constant") {
dist_ncexp <- callModule(mod_parms_server,
"parms_ncexp_C")
} else if (input$ncexp_parms == "uniform") {
dist_ncexp <- callModule(mod_parmsrge_server,
"parms_ncexp_U")
} else if (input$ncexp_parms == "triangular") {
dist_ncexp <- c(callModule(mod_parmsrge_server,
"parms_ncexp_Tr1"),
callModule(mod_parms_server,
"parms_ncexp_Tr2"))
} else if (input$ncexp_parms == "trapezoidal") {
dist_ncexp <- c(callModule(mod_parmsrge_server,
"parms_ncexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_ncexp_Tz1")[2])
} else if (input$ncexp_parms == "logit-logistic") {
dist_ncexp <- c(callModule(mod_parms2a_server,
"parms_ncexp_Ll1"),
callModule(mod_parms2a_server,
"parms_ncexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_ncexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Ll3")[2])
} else if (input$ncexp_parms == "logit-normal") {
dist_ncexp <- c(callModule(mod_parms2a_server,
"parms_ncexp_Ln1"),
callModule(mod_parms2a_server,
"parms_ncexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_ncexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_ncexp_Ln3")[2])
} else if (input$ncexp_parms == "beta") {
dist_ncexp <- c(input$parms_ncexp_B1,
input$parms_ncexp_B2)
}
if (input$ncnexp_parms == "constant") {
dist_ncnexp <- callModule(mod_parms_server,
"parms_ncnexp_C")
} else if (input$ncnexp_parms == "uniform") {
dist_ncnexp <- callModule(mod_parmsrge_server,
"parms_ncnexp_U")
} else if (input$ncnexp_parms == "triangular") {
dist_ncnexp <- c(callModule(mod_parmsrge_server,
"parms_ncnexp_Tr1"),
callModule(mod_parms_server,
"parms_ncnexp_Tr2"))
} else if (input$ncnexp_parms == "trapezoidal") {
dist_ncnexp <- c(callModule(mod_parmsrge_server,
"parms_ncnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_ncnexp_Tz1")[2])
} else if (input$ncnexp_parms == "logit-logistic") {
dist_ncnexp <- c(callModule(mod_parms2a_server,
"parms_ncnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_ncnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_ncnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Ll3")[2])
} else if (input$ncnexp_parms == "logit-normal") {
dist_ncnexp <- c(callModule(mod_parms2a_server,
"parms_ncnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_ncnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_ncnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_ncnexp_Ln3")[2])
} else if (input$ncnexp_parms == "beta") {
dist_ncnexp <- c(input$parms_ncnexp_B1,
input$parms_ncnexp_B2)
}
if (input$prevexp_parms == "constant") {
dist_prevexp <- callModule(mod_parms_server,
"parms_prevexp_C")
} else if (input$prevexp_parms == "uniform") {
dist_prevexp <- callModule(mod_parmsrge_server,
"parms_prevexp_U")
} else if (input$prevexp_parms == "triangular") {
dist_prevexp <- c(callModule(mod_parmsrge_server,
"parms_prevexp_Tr1"),
callModule(mod_parms_server,
"parms_prevexp_Tr2"))
} else if (input$prevexp_parms == "trapezoidal") {
dist_prevexp <- c(callModule(mod_parmsrge_server,
"parms_prevexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_prevexp_Tz1")[2])
} else if (input$prevexp_parms == "logit-logistic") {
dist_prevexp <- c(callModule(mod_parms2a_server,
"parms_prevexp_Ll1"),
callModule(mod_parms2a_server,
"parms_prevexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_prevexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Ll3")[2])
} else if (input$prevexp_parms == "logit-normal") {
dist_prevexp <- c(callModule(mod_parms2a_server,
"parms_prevexp_Ln1"),
callModule(mod_parms2a_server,
"parms_prevexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_prevexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_prevexp_Ln3")[2])
} else if (input$prevexp_parms == "beta") {
dist_prevexp <- c(input$parms_prevexp_B1,
input$parms_prevexp_B2)
}
if (input$prevnexp_parms == "constant") {
dist_prevnexp <- callModule(mod_parms_server,
"parms_prevnexp_C")
} else if (input$prevnexp_parms == "uniform") {
dist_prevnexp <- callModule(mod_parmsrge_server,
"parms_prevnexp_U")
} else if (input$prevnexp_parms == "triangular") {
dist_prevnexp <- c(callModule(mod_parmsrge_server,
"parms_prevnexp_Tr1"),
callModule(mod_parms_server,
"parms_prevnexp_Tr2"))
} else if (input$prevnexp_parms == "trapezoidal") {
dist_prevnexp <- c(callModule(mod_parmsrge_server,
"parms_prevnexp_Tz1")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz2")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz2")[2],
callModule(mod_parmsrge_server,
"parms_prevnexp_Tz1")[2])
} else if (input$prevnexp_parms == "logit-logistic") {
dist_prevnexp <- c(callModule(mod_parms2a_server,
"parms_prevnexp_Ll1"),
callModule(mod_parms2a_server,
"parms_prevnexp_Ll2"),
callModule(mod_parmsrge_server,
"parms_prevnexp_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Ll3")[2])
} else if (input$prevnexp_parms == "logit-normal") {
dist_prevnexp <- c(callModule(mod_parms2a_server,
"parms_prevnexp_Ln1"),
callModule(mod_parms2a_server,
"parms_prevnexp_Ln2"),
callModule(mod_parmsrge_server,
"parms_prevnexp_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_prevnexp_Ln3")[2])
} else if (input$prevnexp_parms == "beta") {
dist_prevnexp <- c(input$parms_prevnexp_B1,
input$parms_prevnexp_B2)
}
if (input$risk_parms == "constant") {
dist_risk <- callModule(mod_parms2_server,
"parms_risk_C")
} else if (input$risk_parms == "uniform") {
dist_risk <- callModule(mod_parmsrge2_server,
"parms_risk_U")
} else if (input$risk_parms == "triangular") {
dist_risk <- c(callModule(mod_parmsrge2_server,
"parms_risk_Tr1"),
callModule(mod_parms2_server,
"parms_risk_Tr2"))
} else if (input$risk_parms == "trapezoidal") {
dist_risk <- c(callModule(mod_parmsrge2_server,
"parms_risk_Tz1")[1],
callModule(mod_parmsrge2_server,
"parms_risk_Tz2")[1],
callModule(mod_parmsrge2_server,
"parms_risk_Tz2")[2],
callModule(mod_parmsrge2_server,
"parms_risk_Tz1")[2])
} else if (input$risk_parms == "logit-logistic") {
dist_risk <- c(callModule(mod_parms2a_server,
"parms_risk_Ll1"),
callModule(mod_parms2a_server,
"parms_risk_Ll2"),
callModule(mod_parmsrge_server,
"parms_risk_Ll3")[1],
callModule(mod_parmsrge_server,
"parms_risk_Ll3")[2])
} else if (input$risk_parms == "logit-normal") {
dist_risk <- c(callModule(mod_parms2a_server,
"parms_risk_Ln1"),
callModule(mod_parms2a_server,
"parms_risk_Ln2"),
callModule(mod_parmsrge_server,
"parms_risk_Ln3")[1],
callModule(mod_parmsrge_server,
"parms_risk_Ln3")[2])
}
if (input$discard == 0) {
throw_away <- FALSE
} else throw_away <- TRUE
if (input$discard_conf == 0) {
throw_away_conf <- FALSE
} else throw_away_conf <- TRUE
if (input$prob_type == "probsens" & input$diff == 0) {
set.seed(misclass_seed())
episensr::probsens(mat,
type = input$misclassProb_type,
reps = input$reps,
seca.parms = list(input$seca_parms,
dist_seca),
spca.parms = list(input$spca_parms,
dist_spca),
discard = throw_away,
alpha = input$alpha)
} else if (input$prob_type == "probsens" & input$diff == 1) {
set.seed(misclass_seed())
episensr::probsens(mat,
type = input$misclassProb_type,
reps = input$reps,
seca.parms = list(input$seca_parms,
dist_seca),
seexp.parms = list(input$seexp_parms,
dist_seexp),
spca.parms = list(input$spca_parms,
dist_spca),
spexp.parms = list(input$spexp_parms,
dist_spexp),
corr.se = input$corr_se,
corr.sp = input$corr_sp,
discard = throw_away,
alpha = input$alpha)
} else if (input$prob_type == "probsens_sel" &
input$or_case == 0) {
set.seed(sel_seed())
episensr::probsens.sel(mat,
reps = input$reps_sel,
or.parms = list(input$or_parms,
dist_orparms),
alpha = input$alpha)
} else if (input$prob_type == "probsens_sel" &
input$or_case == 1) {
set.seed(sel_seed())
episensr::probsens.sel(mat,
reps = input$reps_sel,
case.exp = list(input$cexp_parms,
dist_cexp),
case.nexp = list(input$cnexp_parms,
dist_cnexp),
ncase.exp = list(input$ncexp_parms,
dist_ncexp),
ncase.nexp = list(input$ncnexp_parms,
dist_ncnexp),
alpha = input$alpha)
} else if (input$prob_type == "probsens_conf") {
set.seed(conf_seed())
episensr::probsens.conf(mat,
reps = input$reps_conf,
prev.exp = list(input$prevexp_parms,
dist_prevexp),
prev.nexp = list(input$prevnexp_parms,
dist_prevnexp),
risk = list(input$risk_parms,
dist_risk),
corr.p = input$corr_conf,
discard = throw_away_conf,
alpha = input$alpha)
}
})
plotout = reactive({
if (input$prob_type == "probsens") {
plot(episensrout(), input$plot_probsens)
} else if (input$prob_type == "probsens_sel") {
plot(episensrout(), input$plot_probsens_sel)
} else if (input$prob_type == "probsens_conf") {
plot(episensrout(), input$plot_probsens_conf)
}
})
## Output
output$summary_prob = renderPrint({
episensrout()
})
output$warnings_prob = renderText({
invisible(episensrout()$warnings)
})
output$message_prob = renderText({
invisible(episensrout()$message)
})
output$plot_res <- renderPlot({
plotout()
})
shinyjs::runjs("document.getElementById('help_probsens').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.html', '_blank');
};"
)
shinyjs::runjs("document.getElementById('help_probsens_sel').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.sel.html', '_blank');
};"
)
shinyjs::runjs("document.getElementById('help_probsens_conf').onclick = function() {
window.open('https://dhaine.github.io/episensr/reference/probsens.conf.html', '_blank');
};"
)
}
## To be copied in the UI
# mod_prob_ui("tab_prob")
## To be copied in the server
# callModule(mod_prob_server, "tab_prob")
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