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app.R
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app.R
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####################################### WELCOME TO THE SHINY APP AdRI_GAMLSS ######################
####################################### from Sandra K. (2023) #####################################
###################################################################################################
####################################### Scripts ###################################################
source("R/analysis.R")
source("R/gamlss.R")
####################################### Libraries #################################################
if("boot" %in% rownames(installed.packages())){
library(boot)} else{
install.packages("boot")
library(boot)}
if("dplyr" %in% rownames(installed.packages())){
library(dplyr)} else{
install.packages("dplyr")
library(dplyr)}
if("DT" %in% rownames(installed.packages())){
library(DT)} else{
install.packages("DT")
library(DT)}
if("gamlss" %in% rownames(installed.packages())){
library(gamlss)} else{
install.packages("gamlss")
library(gamlss)}
if("gamlss.add" %in% rownames(installed.packages())){
library(gamlss.add)} else{
install.packages("gamlss.add")
library(gamlss.add)}
if("plotly" %in% rownames(installed.packages())){
library(plotly)} else{
install.packages("plotly")
library(plotly)}
if("rpart" %in% rownames(installed.packages())){
library(rpart)} else{
install.packages("rpart")
library(rpart)}
if("rpart.plot" %in% rownames(installed.packages())){
library(rpart.plot)} else{
install.packages("rpart.plot")
library(rpart.plot)}
if("shinydashboard" %in% rownames(installed.packages())){
library(shinydashboard)} else{
install.packages("shinydashboard")
library(shinydashboard)}
if("zoo" %in% rownames(installed.packages())){
library(zoo)} else{
install.packages("zoo")
library(zoo)}
####################################### USER INTERFACE ############################################
ui <- dashboardPage(
dashboardHeader(title = "AdRI_GAMLSS", titleWidth = 175),
dashboardSidebar(width = 175,
sidebarMenu(
menuItem("Analysis", tabName = "analysis", icon = icon("database")),
menuItem("GAMLSS", tabName = "gamlss", icon = icon("chart-line"), startExpanded = FALSE,
menuSubItem("GAMLSS and LMS", tabName = "gamlsslms", icon = icon("chart-line")),
menuSubItem("Comparison", tabName = "comparison", icon = icon("balance-scale")),
menuSubItem("Percentiles", tabName = "percentiles", icon = icon("table")))
)),
dashboardBody(
tags$style("html, body {overflow: visible !important;"),
tabItems(
tabItem(tabName = "analysis",
fillPage(fluidRow(
### Sidebar - Analysis ###
box(
title = tagList(shiny::icon("gear"), "Settings"),
width = 3,
solidHeader = TRUE,
status = "primary",
helpText("Data Upload:"),
selectInput("dataset", "Select preinstalled dataset:", choice = list.files(pattern = c(".csv"), recursive = TRUE)),
uiOutput("dataset_file"),
actionButton('reset', 'Reset Input', icon = icon("trash")),
hr(),
helpText("Data Preprocessing:"),
radioButtons(
"days_or_years",
"Unit for the age:",
c("year" = "age", "day" = "age_days")),
conditionalPanel(
condition = "input.days_or_years == 'age'",
sliderInput(
"age_end",
"Select age-range:",
min = 0 ,
max = 100,
value = c(0, 18))),
conditionalPanel(
condition = "input.days_or_years == 'age_days'",
numericInput(
"age_input_min",
"Select age-range from:",
0,
min = 0,
max = 100 * 365)),
conditionalPanel(
condition = "input.days_or_years == 'age_days'",
numericInput("age_input", "to:", 100, min = 1, max = 100 * 365)),
selectInput("sex", "Select the sex:",
choices = list(
"Male + Female" = "t",
"Male" = "m",
"Female" = "f")),
textInput("text_unit", "Unit of the analyte:", value = "Unit"),
checkboxInput("unique", "First unique values", value = TRUE)
),
### MainPanel - Analysis ###
column(
width = 9,
tabsetPanel(
tabPanel("Overview", icon = icon("home"),
p(br(), strong("Shiny App for calculating age-dependent Reference Intervals!"), br(), br(),
"This Shiny App was developed to create age-dependent Reference Intervals using
Generalized Additive Models for Location, Scale and Shape (GAMLSS).", br(),
"For further information visit our", a("Wiki", href = "https://github.com/SandraKla/AdRI_GAMLSS/wiki"),"!"),
plotlyOutput("scatterplot_plotly", height ="700px")
),
tabPanel("Dataset", icon = icon("table"),
p(br(), strong("Shiny App for calculating age-dependent Reference Intervals!"), br(), br(),
"This Shiny App was developed to create age-dependent Reference Intervals using
Generalized Additive Models for Location, Scale and Shape (GAMLSS).", br(),
"For further information visit our", a("Wiki", href = "https://github.com/SandraKla/AdRI_GAMLSS/wiki"),"!"),
DT::dataTableOutput("datatable")),
tabPanel("Barplots", icon = icon("chart-bar"),
p(br(), strong("Shiny App for calculating age-dependent Reference Intervals!"), br(), br(),
"This Shiny App was developed to create age-dependent Reference Intervals using
Generalized Additive Models for Location, Scale and Shape (GAMLSS).", br(),
"For further information visit our", a("Wiki", href = "https://github.com/SandraKla/AdRI_GAMLSS/wiki"),"!"),
plotOutput("barplot_sex", height = "375px"),
plotOutput("barplot_value", height = "375px")),
tabPanel("Statistics", icon = icon("calculator"),
p(br(), strong("Shiny App for calculating age-dependent Reference Intervals!"), br(), br(),
"This Shiny App was developed to create age-dependent Reference Intervals using
Generalized Additive Models for Location, Scale and Shape (GAMLSS).", br(),
"For further information visit our", a("Wiki", href = "https://github.com/SandraKla/AdRI_GAMLSS/wiki"),"!"),
plotOutput("qqplot", height = "375px"),
plotOutput("lognorm", height = "375px"))
)
)
))),
tabItem(tabName = "gamlsslms",
fillPage(fluidRow(
### Sidebar - GAMLSS ###
box(
title = tagList(shiny::icon("gear"), "Settings"),
width = 3,
solidHeader = TRUE,
status = "primary",
actionButton("button_lms", "Start LMS", icon("calculator"), onclick = "$(tab).removeClass('disabled')"),
htmlOutput("buttons_lms"),
hr(),
selectInput(
"distribtion_gamlss",
"Distribution for GAMLSS:",
choices = list(
"Log-Normal Distribution" = "LOGNO",
"Normal Distribution" = "NO",
"Box-Cox" = c(
#"Box-Cole Green Distribution" = "BCCG",
"Box-Cole Green Distribution (orginal)" = "BCCGo",
#"Box-Cole Green Exp. Distribution" = "BCPE",
"Box-Cole Green Exp. Distribution (orginal)" = "BCPEo",
#"Box-Cole Green T-Distribution" = "BCT",
"Box-Cole Green T-Distribution (orginal)" = "BCTo"
)
)
),
checkboxInput("checkbox", "Distribution proposed by the LMS", value = FALSE),
actionButton("button_gamlss", "Start GAMLSS", icon("calculator"), onclick = "$(tabs).removeClass('disabled')"),
htmlOutput("buttons_gamlss")
),
### MainPanel - GAMLSS ###
column(width = 9,
tabsetPanel(
tabPanel(
"LMS",
icon = icon("chart-line"),
value = "nav_lms",
fluidRow(
box(title = tagList(shiny::icon("chart-line"), "Plot"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("lms", height = "475px")
),
box(title = tagList(shiny::icon("calculator"), "Statistics"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
verbatimTextOutput("lms_text"),
plotOutput("lms_plot"),
plotOutput("lms_fitted"),
plotOutput("lms_wormplots"))
)),
tabPanel(
"GAMLSS (Splines)",
icon = icon("chart-line"),
value = "nav_gamlss",
fluidRow(
box(title = tagList(shiny::icon("chart-line"), "Plot"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("gamlss_models_splines", height = "475px")
),
box(title = tagList(shiny::icon("calculator"), "Statistics"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
p("GAMLSS with P-Splines:"),
verbatimTextOutput("gamlss_text_psplines"),
plotOutput("gamlss_term_pb"),
plotOutput("gamlss_fitted_pb_"),
p("GAMLSS with Cubic-Splines:"),
verbatimTextOutput("gamlss_text_splines"),
plotOutput("gamlss_term_cs"),
plotOutput("gamlss_fitted_cs_"),
p("Wormplots for GAMLSS with the P-Splines and Cubic Splines:"),
plotOutput("wormplots_splines"))
)),
tabPanel(
"GAMLSS (Polynomials)",
icon = icon("chart-line"),
value = "nav_gamlss",
fluidRow(
box(title = tagList(shiny::icon("chart-line"), "Plot"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("gamlss_models_poly", height ="475px")
),
box(title = tagList(shiny::icon("calculator"), "Statistics"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
p("GAMLSS with Polynomial Degree 3:"),
verbatimTextOutput("gamlss_text_poly"),
plotOutput("gamlss_term_poly"),
plotOutput("gamlss_fitted_poly_"),
p("GAMLSS with Polynomial Degree 4:"),
verbatimTextOutput("gamlss_text_poly4"),
plotOutput("gamlss_term_poly4"),
plotOutput("gamlss_fitted_poly4_"),
p("Wormplots for GAMLSS with the Polynomial Degree 3 and 4:"),
plotOutput("wormplots_poly"))
)),
tabPanel(
"GAMLSS (Neural Network)",
icon = icon("brain"),
value = "nav_gamlss",
fluidRow(
box(title = tagList(shiny::icon("chart-line"), "Plot"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("gamlss_net", height = "475px")),
box(title = tagList(shiny::icon("calculator"), "Statistics"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
verbatimTextOutput("net_text"),
# Plot neural network with term.plot(nn_)
plotOutput("network_term"),
plotOutput("network_fitted"),
plotOutput("nn_wormplots"))
)),
tabPanel(
"GAMLSS (Decision Tree)",
icon = icon("tree"),
value = "nav_gamlss",
fluidRow(
box(title = tagList(shiny::icon("chart-line"), "Plot"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("gamlss_tree", height = "475px")
),
box(title = tagList(shiny::icon("calculator"), "Statistics"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
verbatimTextOutput("tree_text"),
plotOutput("rpart_tree"),
plotOutput("tree_term"),
plotOutput("tree_fitted"),
plotOutput("tr_wormplots"))
)
)
)
), tags$script(src = 'tabs_enabled.js')))),
### GAMLSS - Comparison ###
### GAMLSS - Comparison ###
tabItem(tabName = "comparison",
fluidRow(
box(
title = tagList(shiny::icon("gear"), "Settings"),
width = 3,
solidHeader = TRUE,
status = "primary",
p("Models can be compared visually or with the Akaike Information Criterion (AIC),
Generalized Information Criterion (GAIC), Bayesian Information Criterion (BIC),
or Pseudo R-Squared (R^2). The model with the smallest value for AIC, BIC and GAIC is the best model for the data.
The Pseudo R-Squared (R^2) should be as large as possible for a good model. These values are colored.")
),
box(
title = tagList(shiny::icon("balance-scale"), "Comparison"),
width = 9,
solidHeader = TRUE,
status = "primary",
DT::dataTableOutput("table_compare"),
plotOutput("metrics", height = "475px")
))),
### GAMLSS - Prediction ###
tabItem(tabName = "percentiles",
fluidRow(column(
width = 3,
fluidRow(
box(
title = tagList(shiny::icon("gear"), "Settings"),
width = 12,
solidHeader = TRUE,
status = "primary",
selectInput(
"select_model",
"Select model:",
choices = list(
"Splines" = c("P-Splines" = "pb_ri",
"Cubic Splines" = "cs_ri"),
"LMS" = c(LMS = "lms_ri"),
"Polynomial" = c(
"Polynomial (Degree 3)" = "poly_ri",
"Polynomial (Degree 4)" = "poly4_ri"
),
"Machine Learning" = c("Neural Network" = "nn_ri",
"Decision Tree" = "tr_ri")
)
)
))),
column(width = 9,
box(title = tagList(shiny::icon("chart-line"), "Percentiles"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "primary",
plotOutput("gamlss_prediction", height = "475px")),
box(title = tagList(shiny::icon("table"), "Table"),
width = 12,
solidHeader = TRUE,
collapsible = TRUE,
status = "info",
DT::dataTableOutput("gamlss_tables"))
)
))
))
)
####################################### SERVER ####################################################
server <- function(input, output, session) {
options(shiny.sanitize.errors = TRUE)
##################################### Reactive Expressions ######################################
##################################### Reactive Dataset ##########################################
########### Data is subset with the function select_data() with the given age interval ##########
#################################################################################################
values <- reactiveValues(
upload_state = NULL
)
observeEvent(input$dataset_file1, {
values$upload_state <- 'uploaded'
})
observeEvent(input$reset, {
values$upload_state <- 'reset'
})
dataset_input <- reactive({
if (is.null(values$upload_state)) {
return(NULL)
} else if (values$upload_state == 'uploaded') {
return(input$dataset_file1)
} else if (values$upload_state == 'reset') {
return(NULL)
}
})
output$dataset_file <- renderUI({
input$reset ## Create a dependency with the reset button
fileInput('dataset_file1', label = NULL)
})
values_lis <- reactiveValues(
upload_state_lis = NULL
)
data_analyte <- reactive({
progress <- shiny::Progress$new()
progress$set(message = "Load data...", detail = "", value = 2)
cat(paste("*** Welcome to the Shiny App AdRI_GAMLSS! ***\n"))
req(input$dataset, input$age_end)
lms_ready <<- FALSE # To check if the lms method was used
modelsprediction <<- FALSE # To check if the models are build
residuals_ready <<- FALSE # Check if the residuals are calculated
# Read the data (from the CALIPER study or from the generator)
if(is.null(dataset_input())){
data_data <- read.csv2(input$dataset, header = TRUE,
stringsAsFactors = FALSE, sep = ";", dec = ",", na.strings = "")}
if(!is.null(dataset_input()))
{data_data <- read.csv2(dataset_input()[["datapath"]], header = TRUE,
stringsAsFactors = FALSE, sep = ";", dec = ",", na.strings = "")}
################################### Age is given by days ######################################
if(input$days_or_years == "age_days"){
# Preprocessing the data
data_analyte <- select_data_days(data_data, input$age_input_min, input$age_input, input$sex)
################################# First samples #############################################
rows_table_ <- nrow(data_analyte)
# Take only the first and unique samples from the data if ID is given
if(input$unique == TRUE){
data_analyte <-
data_analyte %>%
group_by(patient) %>%
filter(row_number()==1)
# Convert into tibble so as.data.frame()
data_analyte <- as.data.frame(data_analyte)
if(!(rows_table_ == nrow(data_analyte))){
cat(paste("*** Information!", rows_table_ - nrow(data_analyte), "values of the patients were present several times and were deleted. ***\n"))}
}
}
else{
################################# Age in years (default) ####################################
# Preprocessing the data
data_analyte <- select_data(data_data,input$age_end[1] ,input$age_end[2], input$sex)
################################# First samples #############################################
rows_table_ <- nrow(data_analyte)
if(input$unique == TRUE){
data_analyte <-
data_analyte %>%
group_by(patient) %>%
filter(row_number()==1)
# Convert into tibble so as.data.frame()
data_analyte <- as.data.frame(data_analyte)
if(!(rows_table_ == nrow(data_analyte))){
cat(paste("*** Information!", rows_table_ - nrow(data_analyte), "values of the patients were present several times and were deleted. ***\n"))}
}
}
cat("\n")
data_analyte_short <<- data_analyte
on.exit(progress$close())
data_analyte
})
##################################### Build rpart Decision Tree #################################
build_rpart <- reactive({
rpart_ready <- make_rpart(data_analyte(), as.numeric(input$tree_minsplit))
})
##################################### Reactive GAMLSS ###########################################
# make_gamlss() is used with six different smooth additive terms for the GAMLLSS models #########
# with different distributions. #################################################################
build_gamlss_model <- eventReactive(input$button_gamlss, {
req(input$dataset, input$age_end, input$distribtion_gamlss, 50, "RS")
progress <- shiny::Progress$new()
progress$set(message = "Calculate Percentiles with GAMLSS-Models...", detail = "", value = 2)
# Error message
if(input$checkbox == TRUE){
validate(need(lms_ready == TRUE,
"Please make first the LMS-Method to get the proposed distribution!"))
gamlss_model_read <- make_gamlss(data_analyte(), input$age_end[2], lms_$family[1], 100, "RS")}
else{gamlss_model_read <- make_gamlss(data_analyte(), input$age_end[2], input$distribtion_gamlss, 100, "RS")}
# Save global value to check later if the models are already calculated
modelsprediction <<- TRUE
on.exit(progress$close())
})
##################################### Reactive LMS ##############################################
lms_reactive <- eventReactive(input$button_lms, {
progress <- shiny::Progress$new()
progress$set(message = "Calculate Percentiles with LMS...", detail = "", value = 2)
lms_model <- make_lms(data_analyte())
lms_ready <<- TRUE # Value to check if lms is accomplished
on.exit(progress$close())
})
##################################### Overview ##################################################
# Scatterplot from the data_analyte() with plotly
output$scatterplot_plotly <- renderPlotly({
ylab_ <<- paste0(data_analyte()[1,7]," [", input$text_unit,"]")
#if(input$fast == FALSE){
fig <- plot_ly(data_analyte(), x = ~age_days, y = ~value,
text = ~ paste('</br>Patient: ', patient,
'</br>Station: ', code,
'</br>Age [Years]: ', age,
'</br>Age [Days]: ', age_days,
'</br>Value: ', value),
type = "scatter",
symbol = ~sex,
symbols = c('circle', 17),
color = ~sex,
colors = c("indianred", "cornflowerblue"),
mode = "markers",
marker = list(size = 10)) %>%
layout(xaxis = list(title="Age [Days]", titlefont=list(size=20), tickfont = list(size = 15)),
yaxis = list(title=ylab_, titlefont=list(size=20), tickfont = list(size = 15)))
})
# Barplot with the distribution of the sex
output$barplot_sex <- renderPlot({
ylab_ <<- paste0(data_analyte()[1,7]," [", input$text_unit,"]")
if(!(nrow(data_analyte())) == 0){
hist_data_w <- subset(data_analyte(), sex == "F", select = age)
hist_data_m <- subset(data_analyte(), sex == "M", select = age)
hist_w <- hist(hist_data_w$age, breaks=seq(min(data_analyte()[,3])-1,max(data_analyte()[,3]),by=1))$counts
hist_m <- hist(hist_data_m$age, breaks=seq(min(data_analyte()[,3])-1,max(data_analyte()[,3]),by=1))$counts
barplot(rbind(hist_m,hist_w), col = c("cornflowerblue","indianred"),
names.arg=seq(min(data_analyte()[,3]), max(data_analyte()[,3]), by=1), xlab = "AGE_YEARS", las = 1, beside = TRUE, ylab = "Number of data")
abline(h=0)
legend("topright", legend = c(paste0("Men: ", nrow(hist_data_m)), paste0("Female: ", nrow(hist_data_w))), col = c("cornflowerblue","indianred"), pch = c(17, 20))
par(new = TRUE)
boxplot(data_analyte()[,3], horizontal = TRUE, axes = FALSE, col = rgb(0, 0, 0, alpha = 0.15))
}
})
output$barplot_value <- renderPlot({
ylab_ <<- paste0(data_analyte()[1,7]," [", input$text_unit,"]")
if(!(nrow(data_analyte())) == 0){
boxplot(data_analyte()[,5]~interaction(data_analyte()[,2], data_analyte()[,3]), xlab = "Age",
ylab = ylab_, col = c("indianred", "cornflowerblue"), las = 2)
}
})
# QQ-Plot for the complete dataset
output$qqplot <- renderPlot({
if(!(nrow(data_analyte())) == 0){
qqnorm(data_analyte()[,5], pch = 20, col = "grey")
qqline(data_analyte()[,5])
}
})
# Bowley and Lognormfunction
output$lognorm <- renderPlot({
if(!(nrow(data_analyte())) == 0){
try(def.distribution(data_analyte()[,5]))
}
})
# Data-Table
output$datatable <- DT::renderDataTable({
data_table <- data_analyte()
colnames(data_table) <- c("ID", "SEX", "AGE_YEARS", "AGE_DAYS", "VALUE", "STATION", "ANALYTE")
DT::datatable(data_table, extensions = 'Buttons', rownames= FALSE,
options = list(dom = 'Blfrtip', pageLength = 15, buttons = c('copy', 'csv', 'pdf', 'print')),
caption = htmltools::tags$caption(style = 'caption-side: bottom; text-align: center;', 'Table: Overview from Dataset'))
})
################################ LMS #############################################
# LMS-Percentile Plot
output$lms <- renderPlot({
lms_reactive()
centiles(lms_, cent=c(2.5,50,97.5), xlab = "Age [Days]", ylab = ylab_, pch = 20, cex = 1,
col.cent=c("indianred","black","cornflowerblue"), lty.centiles=c(3,1,3),lwd.centiles = 2,
legend = FALSE, col = "lightgrey")
})
# Analysis LMS
output$lms_plot <- renderPlot({
lms_reactive()
plot(lms_, parameters = par(mfrow = c(2,2), mar = par("mar") + c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5))
})
# Analysis LMS
output$lms_fitted <- renderPlot({
lms_reactive()
new_lms_data <- data.frame(value_lms = data_analyte()[[5]], age_lms = data_analyte()[[4]])
fittedPlot(lms_ ,x = new_lms_data$age_lms, xlab = "Age [Days]")
})
# Analysis LMS-Text
output$lms_text <- renderPrint({
lms_reactive()
print(lms_)
cat("Power:")
print(lms_$power)
cat("\n")
centiles(lms_, cent=c(2.5,50,97.5), plot=FALSE)
})
# Wormplots LMS
output$lms_wormplots <- renderPlot({
lms_reactive()
try(wp(lms_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
})
##################################### GAMLSS ####################################################
output$all_gamlss <- renderPlot({
centiles.com(pb_, cs_, poly_, poly4_, nn_, tr_, cent=c(2.5,50,97.5), xlab = "Age [Days]", ylab = ylab_,
legend = TRUE, main = "GAMLSS")
})
output$buttons_gamlss <- renderUI({
build_gamlss_model()
print("*** Your GAMLSS are ready! ***")
})
output$buttons_lms <- renderUI({
lms_reactive()
print("*** Your LMS model is ready! ***")
})
# Centiles Plot with gamlss (P-Splines, Cubic Splines) ######
output$gamlss_models_splines <- renderPlot({
build_gamlss_model()
par(mfrow=c(1,2))
centiles(pb_, main = "GAMLSS with P-Splines", cent=c(2.5,50,97.5), xlab = "Age [Days]",
ylab = ylab_, pch = 20, cex = 1, col.cent=c("indianred","black","cornflowerblue"),
lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
centiles(cs_, main = "GAMLSS with Cubic Splines", cent=c(2.5,50,97.5), xlab = "Age [Days]",
ylab = ylab_, pch = 20, cex = 1, col.cent=c("indianred","black","cornflowerblue"),
lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
})
# Centiles Plot with gamlss (Polynomials Degree 3 and 4) ######
output$gamlss_models_poly <- renderPlot({
build_gamlss_model()
par(mfrow=c(1,2))
centiles(poly_, main = "GAMLSS with Polynomials (Degree 3)", cent=c(2.5,50,97.5), xlab = "Age [Days]",
ylab = ylab_, pch = 20, cex = 1, col.cent=c("indianred","black","cornflowerblue"),
lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
centiles(poly4_, main = "GAMLSS with Polynomials (Degree 4)",cent=c(2.5,50,97.5), xlab = "Age [Days]",
ylab = ylab_, pch = 20, cex = 1, col.cent=c("indianred","black","cornflowerblue"),
lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
})
# Plot fitted models for P-Splines
output$gamlss_fitted_pb_ <- renderPlot({
build_gamlss_model()
fittedPlot(pb_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# Plot fitted models for Cubic Splines
output$gamlss_fitted_cs_ <- renderPlot({
build_gamlss_model()
fittedPlot(cs_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# Plot fitted models for Polynomials Degree 3
output$gamlss_fitted_poly_ <- renderPlot({
build_gamlss_model()
fittedPlot(poly_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# Plot fitted models for Polynomials Degree 4
output$gamlss_fitted_poly4_ <- renderPlot({
build_gamlss_model()
fittedPlot(poly4_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# GAMLSS - Analysis Text
output$gamlss_text_psplines <- renderPrint({
build_gamlss_model()
suppressWarnings({summary(pb_)})
cat("\n")
centiles(pb_, cent=c(2.5,50,97.5), plot=FALSE)
})
# GAMLSS - Analysis Text
output$gamlss_text_splines <- renderPrint({
build_gamlss_model()
suppressWarnings({summary(cs_)})
cat("\n")
centiles(cs_, cent=c(2.5,50,97.5), plot=FALSE)
})
# GAMLSS - Analysis Text
output$gamlss_text_poly <- renderPrint({
build_gamlss_model()
suppressWarnings({summary(poly_)})
cat("\n")
centiles(poly_,cent=c(2.5,50,97.5), plot=FALSE)
})
# GAMLSS - Analysis Text
output$gamlss_text_poly4 <- renderPrint({
build_gamlss_model()
suppressWarnings({summary(poly4_)})
cat("\n")
centiles(poly4_, cent=c(2.5,50,97.5), plot=FALSE)
})
# Plot the changed terms for P-Splines
output$gamlss_term_pb <- renderPlot({
build_gamlss_model()
try(plot(pb_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Plot the changed terms for Cubic Splines
output$gamlss_term_cs <- renderPlot({
build_gamlss_model()
try(plot(cs_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Plot the changed terms for Polynomial Degree 3
output$gamlss_term_poly <- renderPlot({
build_gamlss_model()
try(plot(poly_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Plot the changed terms for Polynomial Degree 4
output$gamlss_term_poly4 <- renderPlot({
build_gamlss_model()
try(plot(poly4_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Wormplots from P-Splines and Cubic Splines
output$wormplots_splines <- renderPlot({
build_gamlss_model()
par(mfrow = c(1,2))
try(wp(pb_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
try(wp(cs_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
})
# Wormplots from Polynomials Degree 3 and 4
output$wormplots_ploy <- renderPlot({
build_gamlss_model()
par(mfrow = c(1,2))
try(wp(poly_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
try(wp(poly4_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
})
# Neural Network (machine learning) ##############################################
# Centiles Plot with the Neural Network
output$gamlss_net <- renderPlot({
build_gamlss_model()
centiles(nn_, main = "GAMLSS with Neural Network", cent=c(2.5,50,97.5), xlab = "Age [Days]", ylab = ylab_, pch = 20, cex = 1,
col.cent=c("indianred","black","cornflowerblue"), lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
})
# Neural Network - Analysis
output$network_term <- renderPlot({
build_gamlss_model()
try(plot(nn_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Neural Network - Analysis
output$network_fitted <- renderPlot({
build_gamlss_model()
fittedPlot(nn_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# Neural Network - Analysis
output$net_text<- renderPrint({
build_gamlss_model()
suppressWarnings({summary(nn_)})
centiles(nn_,cent=c(2.5,50,97.5), plot=FALSE)
})
#Wormplots from the Neural Network
output$nn_wormplots <- renderPlot({
build_gamlss_model()
try(wp(nn_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
})
# Decision Tree #################################################################################
# Centiles Plot with the Decision Tree
output$gamlss_tree <- renderPlot({
build_gamlss_model()
centiles(tr_, main = "GAMLSS with Decision Tree", cent=c(2.5,50,97.5), xlab = "Age [Days]", ylab = ylab_, pch = 20, cex = 1,
col.cent=c("indianred","black","cornflowerblue"), lty.centiles=c(3,1,3), lwd.centiles = 2, legend = FALSE, col = "lightgrey")
})
# Decision Tree - Analysis
output$tree_term <- renderPlot({
build_gamlss_model()
try(plot(tr_, parameters = par(mfrow = c(2,2), mar = par("mar")+
c(0,1,0,0), col.axis = "black", col = "grey", col.main = "black",
col.lab = "black", pch = 20, cex = 0.5, cex.lab = 1.5, cex.axis = 1, cex.main = 1.5)))
})
# Decision Tree - Analysis
output$tree_fitted <- renderPlot({
build_gamlss_model()
fittedPlot(nn_, x=data_analyte_short$age_days, xlab = "Age [Days]")
})
# Decision Tree - Analysis
output$tree_text <- renderPrint({
build_gamlss_model()
print(getSmo(tr_))
suppressWarnings({summary(tr_)})
centiles(tr_,cent=c(2.5,50,97.5), plot=FALSE)
})
# Plotted Decision Tree
output$rpart_tree <- renderPlot({
build_gamlss_model()
rpart.plot(getSmo(tr_), roundint=FALSE, box.palette = "RdBu")
})
# Wormplots from Decision Tree
output$tr_wormplots <- renderPlot({
build_gamlss_model()
try(wp(tr_, ylim.all = 3, col = "cornflowerblue", n.inter= 9))
})
# Comparism #####################################################################################
# Comparison Table for all GAMLSS and LMS
output$table_compare <- DT::renderDataTable({