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R/app_server.R
In fitODBODRshiny: 'Shiny' Application for R Package 'fitODBOD'
Defines functions
app_server
#' The application server-side
#'
#' @param input,output,session Internal parameters for {shiny}.
#' DO NOT REMOVE.
#' @import shiny
#' @import bslib
#' @import ggplot2
#' @import golem
#' @importFrom shinyscreenshot screenshot
#' @import flextable
#' @noRd
app_server <- function(input, output, session) {
get_golem_options("All_Plots")
All_Plots<-fitODBODRshiny::All_Plots
# Your application server logic
output$BinPlot <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Bin_Plot[[1]],
"ADW2" = All_Plots$Bin_Plot[[2]],
"CD" = All_Plots$Bin_Plot[[3]],
"ED" = All_Plots$Bin_Plot[[4]],
"PDID" = All_Plots$Bin_Plot[[5]],
"TDArg" = All_Plots$Bin_Plot[[6]],
"TDUSA" = All_Plots$Bin_Plot[[7]])
})
output$AddBinDistPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Add_Bin_Plot[[1]],
"ADW2"=All_Plots$Add_Bin_Plot[[2]],
"CD"=All_Plots$Add_Bin_Plot[[3]],
"ED"=All_Plots$Add_Bin_Plot[[4]],
"PDID"=All_Plots$Add_Bin_Plot[[5]],
"TDArg"=All_Plots$Add_Bin_Plot[[6]],
"TDUSA"=All_Plots$Add_Bin_Plot[[7]])
})
output$BetaCorrBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Corr_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Beta_Corr_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Beta_Corr_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Beta_Corr_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Beta_Corr_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Beta_Corr_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Beta_Corr_Bin_Freq_Plot[[7]])
})
output$BetaCorrBinParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Corr_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Beta_Corr_Bin_Par_Plot[[2]],
"CD"=All_Plots$Beta_Corr_Bin_Par_Plot[[3]],
"ED"=All_Plots$Beta_Corr_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Beta_Corr_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Beta_Corr_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Beta_Corr_Bin_Par_Plot[[7]])
})
output$COMPBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$COMP_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$COMP_Bin_Freq_Plot[[2]],
"CD"=All_Plots$COMP_Bin_Freq_Plot[[3]],
"ED"=All_Plots$COMP_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$COMP_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$COMP_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$COMP_Bin_Freq_Plot[[7]])
})
output$COMPBinParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$COMP_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$COMP_Bin_Par_Plot[[2]],
"CD"=All_Plots$COMP_Bin_Par_Plot[[3]],
"ED"=All_Plots$COMP_Bin_Par_Plot[[4]],
"PDID"=All_Plots$COMP_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$COMP_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$COMP_Bin_Par_Plot[[7]])
})
output$CorrBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Corr_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Corr_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Corr_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Corr_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Corr_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Corr_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Corr_Bin_Freq_Plot[[7]])
})
output$CorrBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Corr_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Corr_Bin_Par_Plot[[2]],
"CD"=All_Plots$Corr_Bin_Par_Plot[[3]],
"ED"=All_Plots$Corr_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Corr_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Corr_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Corr_Bin_Par_Plot[[7]])
})
output$MultiBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Multi_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Multi_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Multi_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Multi_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Multi_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Multi_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Multi_Bin_Freq_Plot[[7]])
})
output$MultiBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Multi_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Multi_Bin_Par_Plot[[2]],
"CD"=All_Plots$Multi_Bin_Par_Plot[[3]],
"ED"=All_Plots$Multi_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Multi_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Multi_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Multi_Bin_Par_Plot[[7]])
})
output$LovMultiBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$LMulti_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$LMulti_Bin_Freq_Plot[[2]],
"CD"=All_Plots$LMulti_Bin_Freq_Plot[[3]],
"ED"=All_Plots$LMulti_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$LMulti_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$LMulti_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$LMulti_Bin_Freq_Plot[[7]])
})
output$LovMultiBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$LMulti_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$LMulti_Bin_Par_Plot[[2]],
"CD"=All_Plots$LMulti_Bin_Par_Plot[[3]],
"ED"=All_Plots$LMulti_Bin_Par_Plot[[4]],
"PDID"=All_Plots$LMulti_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$LMulti_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$LMulti_Bin_Par_Plot[[7]])
})
output$BinPlot1 <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Bin_Plot[[1]],
"ADW2" = All_Plots$Bin_Plot[[2]],
"CD" = All_Plots$Bin_Plot[[3]],
"ED" = All_Plots$Bin_Plot[[4]],
"PDID" = All_Plots$Bin_Plot[[5]],
"TDArg" = All_Plots$Bin_Plot[[6]],
"TDUSA" = All_Plots$Bin_Plot[[7]])
})
output$TriBinPlot <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Tri_Bin_Plot[[1]],
"ADW2" = All_Plots$Tri_Bin_Plot[[2]],
"CD" = All_Plots$Tri_Bin_Plot[[3]],
"ED" = All_Plots$Tri_Bin_Plot[[4]],
"PDID" = All_Plots$Tri_Bin_Plot[[5]],
"TDArg" = All_Plots$Tri_Bin_Plot[[6]],
"TDUSA" = All_Plots$Tri_Bin_Plot[[7]])
})
output$BetaBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Beta_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Beta_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Beta_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Beta_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Beta_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Beta_Bin_Freq_Plot[[7]])
})
output$BetaBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Beta_Bin_Par_Plot[[2]],
"CD"=All_Plots$Beta_Bin_Par_Plot[[3]],
"ED"=All_Plots$Beta_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Beta_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Beta_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Beta_Bin_Par_Plot[[7]])
})
output$KumBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Kum_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Kum_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Kum_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Kum_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Kum_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Kum_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Kum_Bin_Freq_Plot[[7]])
})
output$KumBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Kum_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Kum_Bin_Par_Plot[[2]],
"CD"=All_Plots$Kum_Bin_Par_Plot[[3]],
"ED"=All_Plots$Kum_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Kum_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Kum_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Kum_Bin_Par_Plot[[7]])
})
output$GamBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Gam_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Gam_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Gam_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Gam_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Gam_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Gam_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Gam_Bin_Freq_Plot[[7]])
})
output$GamBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Gam_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Gam_Bin_Par_Plot[[2]],
"CD"=All_Plots$Gam_Bin_Par_Plot[[3]],
"ED"=All_Plots$Gam_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Gam_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Gam_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Gam_Bin_Par_Plot[[7]])
})
output$GraBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Grassia_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Grassia_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Grassia_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Grassia_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Grassia_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Grassia_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Grassia_Bin_Freq_Plot[[7]])
})
output$GraBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Grassia_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Grassia_Bin_Par_Plot[[2]],
"CD"=All_Plots$Grassia_Bin_Par_Plot[[3]],
"ED"=All_Plots$Grassia_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Grassia_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Grassia_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Grassia_Bin_Par_Plot[[7]])
})
output$GHGBBFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$GHGBeta_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$GHGBeta_Bin_Freq_Plot[[2]],
"CD"=All_Plots$GHGBeta_Bin_Freq_Plot[[3]],
"ED"=All_Plots$GHGBeta_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$GHGBeta_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$GHGBeta_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$GHGBeta_Bin_Freq_Plot[[7]])
})
output$GHGBBParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$GHGBeta_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$GHGBeta_Bin_Par_Plot[[2]],
"CD"=All_Plots$GHGBeta_Bin_Par_Plot[[3]],
"ED"=All_Plots$GHGBeta_Bin_Par_Plot[[4]],
"PDID"=All_Plots$GHGBeta_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$GHGBeta_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$GHGBeta_Bin_Par_Plot[[7]])
})
output$McGBBFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$McGBB_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$McGBB_Bin_Freq_Plot[[2]],
"CD"=All_Plots$McGBB_Bin_Freq_Plot[[3]],
"ED"=All_Plots$McGBB_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$McGBB_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$McGBB_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$McGBB_Bin_Freq_Plot[[7]])
})
output$McGBBParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$McGBB_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$McGBB_Bin_Par_Plot[[2]],
"CD"=All_Plots$McGBB_Bin_Par_Plot[[3]],
"ED"=All_Plots$McGBB_Bin_Par_Plot[[4]],
"PDID"=All_Plots$McGBB_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$McGBB_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$McGBB_Bin_Par_Plot[[7]])
})
output$ABD_Table_Plot<-renderUI({
Results<-switch(input$Datasets,
"ADW1"=All_Plots$ABD_Table[[1]],
"ADW2"=All_Plots$ABD_Table[[2]],
"CD"=All_Plots$ABD_Table[[3]],
"ED"=All_Plots$ABD_Table[[4]],
"PDID"=All_Plots$ABD_Table[[5]],
"TDArg"=All_Plots$ABD_Table[[6]],
"TDUSA"=All_Plots$ABD_Table[[7]])
All_Datas<-switch (input$Datasets,
"ADW1"=All_Plots$All_Data[[1]],
"ADW2"=All_Plots$All_Data[[2]],
"CD"=All_Plots$All_Data[[3]],
"ED"=All_Plots$All_Data[[4]],
"PDID"=All_Plots$All_Data[[5]],
"TDArg"=All_Plots$All_Data[[6]],
"TDUSA"=All_Plots$All_Data[[7]])
flextable::flextable(data=Results,
col_keys = c("Bin_RV","Actual_Freq","EstFreq_BinD",
"EstFreq_AddBinD","EstFreq_BetaCorrBinD",
"EstFreq_COMPBinD","EstFreq_CorrBinD",
"EstFreq_MultiBinD","EstFreq_LMBinD")) |>
flextable::theme_box() |> flextable::autofit() |>
flextable::fontsize(i=c(1:nrow(Results)),j=c(1:ncol(Results)),size = 12,part = "body") |>
flextable::fontsize(i=1,j=c(1:ncol(Results)),size = 13,part = "header") |>
flextable::bold(i=1,part = "header") |>
flextable::bold(i=c((length(All_Datas[,1])+1):nrow(Results)),j=1,part = "body") |>
flextable::align(i=c(1:nrow(Results)),j=c(1:ncol(Results)),align = "center") |>
flextable::set_header_labels(values = c(Bin_RV="Binomial Random Variable",
Actual_Freq="Observed Frequencies",
EstFreq_BinD="Binomial Distribution",
EstFreq_AddBinD="Additive Binomial Distribution",
EstFreq_BetaCorrBinD="Beta-Correlated Binomial Distribution",
EstFreq_COMPBinD="Composite Binomial Distribution",
EstFreq_CorrBinD="Correlated Binomial Distribution",
EstFreq_MultiBinD="Multiplicative Binomial Distribution",
EstFreq_LMBinD="Lovinson Multiplicative Binomial Distribution")) |>
flextable::align(i=1,part = "header",align = "center") |>
flextable::htmltools_value()
})
output$BMD_Table_Plot<-renderUI({
Results<-switch(input$Datasets,
"ADW1"=All_Plots$BMD_Table[[1]],
"ADW2"=All_Plots$BMD_Table[[2]],
"CD"=All_Plots$BMD_Table[[3]],
"ED"=All_Plots$BMD_Table[[4]],
"PDID"=All_Plots$BMD_Table[[5]],
"TDArg"=All_Plots$BMD_Table[[6]],
"TDUSA"=All_Plots$BMD_Table[[7]])
All_Datas<-switch (input$Datasets,
"ADW1"=All_Plots$All_Data[[1]],
"ADW2"=All_Plots$All_Data[[2]],
"CD"=All_Plots$All_Data[[3]],
"ED"=All_Plots$All_Data[[4]],
"PDID"=All_Plots$All_Data[[5]],
"TDArg"=All_Plots$All_Data[[6]],
"TDUSA"=All_Plots$All_Data[[7]])
flextable::flextable(data=Results,
col_keys = c("Bin_RV","Actual_Freq","EstFreq_BinD",
"EstFreq_TriBinD","EstFreq_BetaBinD","EstFreq_KumBinD",
"EstFreq_GammaBinD","EstFreq_GrassiaIIBinD",
"EstFreq_GHGBBD","EstFreq_McGBBD")) |>
flextable::theme_box() |> flextable::autofit()|>
flextable::fontsize(i=c(1:nrow(Results)),j=c(1:ncol(Results)),size = 12,part = "body") |>
flextable::fontsize(i=1,j=c(1:ncol(Results)),size = 13,part = "header") |>
flextable::bold(i=1,part = "header") |>
flextable::bold(i=c((length(All_Datas[,1])+1):nrow(Results)),j=1,part = "body") |>
flextable::align(i=c(1:nrow(Results)),j=c(1:ncol(Results)),align = "center") |>
flextable::set_header_labels(values = c(Bin_RV="Binomial Random Variable",
Actual_Freq="Observed Frequencies",
EstFreq_BinD="Binomial Distribution",
EstFreq_TriBinD="Triangular Binomial Distribution",
EstFreq_BetaBinD="Beta Binomial Distribution",
EstFreq_KumBinD="Kumaraswamy Binomial Distribution",
EstFreq_GammaBinD="Gamma Binomial Distribution",
EstFreq_GrassiaIIBinD="Grassia II Binomial Distribution",
EstFreq_GHGBBD="GHG Beta Binomial Distribution",
EstFreq_McGBBD="McG Beta Binomial Distribution")) |>
flextable::align(i=1,part = "header",align = "center") |>
flextable::htmltools_value()
})
output$Text_ABD<-renderText({
switch(input$Datasets,
"ADW1" = HTML("<li> For the Alcohol Data Week 1, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 6.2538 while the estimated variance is 1.696 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of variance difference
and count difference it seems that Beta Correlated Binomial distribution is the best choice."),
"ADW2" = HTML("<li> For the Alcohol Data Week 2, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.7873 while the estimated variance is 1.6945 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of count difference
and p-value Beta Correlated Binomial distribution is the best choice. <li> Composite binomial, Multiplicative
binomial and Lovinson Multiplicative binomial have smaller variance differences compared to Beta Correlated
Binomial distribution."),
"CD" = HTML("<li> For the Course Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.2562 while the estimated variance is 1.7094 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of count difference
clearly all distributions are suitable. <li> According to p-value the best choice is Beta-Correlated Binomial
distribution. <li> While Composite Binomial, Multiplicative Binomial and Lovinson Multiplicative Binomial
have the least variance differences."),
"ED" = HTML("<li> For the Exam Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 2.8245 while the estimated variance is 1.2156 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the count difference
clearly distributions Multiplicative and Lovinson Multiplicative Binomial are suitable. <li> According
to p-value the above two distributions are the best choices as well and this is the case for variance difference
as well."),
"PDID" = HTML("<li> For the Plant Disease Incidence Data, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.8946 while the estimated variance is 1.2647 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
count difference all distributions are suitable. <li> According to p-value the distributions Multiplicative binomial
and Lovinson Multiplicative binomial are the best choices. <li> Based on variance difference the best choice is
Composite Binomial distribution."),
"TDArg" = HTML("<li> For the Terror Data of Argentina, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.9654 while the estimated variance is 0.6486 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
variance difference Additive Binomial and Correlated Binomial distributions are suitable. <li> According to p-value
the Composite Binomial distribution is the best choice."),
"TDUSA" = HTML("<li> For the Terror Data of USA, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 0.99 while the estimated variance is 0.5689 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
variance difference Composite Binomial distribution is suitable. <li> According to p-value the distributions
the Multiplicative Binomial and Lovinson Multiplicative Binomial distribution are the best choices."))
})
output$Text_BMD<-renderText({
switch(input$Datasets,
"ADW1" = HTML("<li> For the Alcohol Data Week 1, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 6.2538 while the estimated variance is 1.696 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of variance difference the
best choice is Beta-Binomial distribution. <li> According to count difference and p-value it seems that Gaussian
Hypergeometric Generalized Beta Binomial distribution is the best choice."),
"ADW2" = HTML("<li> For the Alcohol Data Week 2, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.7873 while the estimated variance is 1.6945 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of variance difference the
best choice is Gamma Binomial distribution. <li> According to count difference McDonald Generalized Beta Binomial
and based on p-value Gaussian Hypergeometric Generalized Beta Binomial are the best choices."),
"CD" = HTML("<li> For the Course Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.2562 while the estimated variance is 1.7094 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of count difference
clearly all distributions are suitable. <li> According to p-value the best choice is Kumaraswamy Binomial
distribution. <li> While Gaussian Hypergeometric Generalized Beta Binomial distribution has the least variance
difference."),
"ED" = HTML("<li> For the Exam Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 2.8245 while the estimated variance is 1.2156 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the count difference and
p-value clearly Gaussian Hypergeometric Generalized Beta Binomial distribution is suitable. <li> According
to variance difference the distributions Beta Binomial and Grassia II Binomial distributions are suitable."),
"PDID" = HTML("<li> For the Plant Disease Incidence Data, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.8946 while the estimated variance is 1.2647 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on the results of
count difference all distributions are suitable except for Triangular Binomial. <li> According to variance
difference the best choices are Beta Binomial, Kumaraswamy Binomial Grassia II Binomial and Gaussian
Hypergeometric Generalized Beta Binomial distributions."),
"TDArg" = HTML("<li> For the Terror Data of Argentina, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.9654 while the estimated variance is 0.6486 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on the results of
variance difference Gaussian Hypergeometric Generalized Beta Binomial is suitable. <li> Based on p-value
the Gamma Binomial distribution is the best choice and except for Triangular Binomial all other distributions
are a good choice based on count differences."),
"TDUSA" = HTML("<li> For the Terror Data of USA, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 0.99 while the estimated variance is 0.5689 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on
variance difference Gaussian Hypergeometric Generalized Beta Binomial distribution is suitable. <li> According
to p-value the distribution Gamma Binomial is the best choice."))
})
observeEvent(input$download_ABD,{
shinyscreenshot::screenshot()
})
observeEvent(input$download_BMD, {
shinyscreenshot::screenshot()
})
}
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Defines functions app_server
#' The application server-side
#'
#' @param input,output,session Internal parameters for {shiny}.
#' DO NOT REMOVE.
#' @import shiny
#' @import bslib
#' @import ggplot2
#' @import golem
#' @importFrom shinyscreenshot screenshot
#' @import flextable
#' @noRd
app_server <- function(input, output, session) {
get_golem_options("All_Plots")
All_Plots<-fitODBODRshiny::All_Plots
# Your application server logic
output$BinPlot <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Bin_Plot[[1]],
"ADW2" = All_Plots$Bin_Plot[[2]],
"CD" = All_Plots$Bin_Plot[[3]],
"ED" = All_Plots$Bin_Plot[[4]],
"PDID" = All_Plots$Bin_Plot[[5]],
"TDArg" = All_Plots$Bin_Plot[[6]],
"TDUSA" = All_Plots$Bin_Plot[[7]])
})
output$AddBinDistPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Add_Bin_Plot[[1]],
"ADW2"=All_Plots$Add_Bin_Plot[[2]],
"CD"=All_Plots$Add_Bin_Plot[[3]],
"ED"=All_Plots$Add_Bin_Plot[[4]],
"PDID"=All_Plots$Add_Bin_Plot[[5]],
"TDArg"=All_Plots$Add_Bin_Plot[[6]],
"TDUSA"=All_Plots$Add_Bin_Plot[[7]])
})
output$BetaCorrBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Corr_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Beta_Corr_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Beta_Corr_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Beta_Corr_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Beta_Corr_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Beta_Corr_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Beta_Corr_Bin_Freq_Plot[[7]])
})
output$BetaCorrBinParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Corr_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Beta_Corr_Bin_Par_Plot[[2]],
"CD"=All_Plots$Beta_Corr_Bin_Par_Plot[[3]],
"ED"=All_Plots$Beta_Corr_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Beta_Corr_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Beta_Corr_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Beta_Corr_Bin_Par_Plot[[7]])
})
output$COMPBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$COMP_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$COMP_Bin_Freq_Plot[[2]],
"CD"=All_Plots$COMP_Bin_Freq_Plot[[3]],
"ED"=All_Plots$COMP_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$COMP_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$COMP_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$COMP_Bin_Freq_Plot[[7]])
})
output$COMPBinParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$COMP_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$COMP_Bin_Par_Plot[[2]],
"CD"=All_Plots$COMP_Bin_Par_Plot[[3]],
"ED"=All_Plots$COMP_Bin_Par_Plot[[4]],
"PDID"=All_Plots$COMP_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$COMP_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$COMP_Bin_Par_Plot[[7]])
})
output$CorrBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Corr_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Corr_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Corr_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Corr_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Corr_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Corr_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Corr_Bin_Freq_Plot[[7]])
})
output$CorrBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Corr_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Corr_Bin_Par_Plot[[2]],
"CD"=All_Plots$Corr_Bin_Par_Plot[[3]],
"ED"=All_Plots$Corr_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Corr_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Corr_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Corr_Bin_Par_Plot[[7]])
})
output$MultiBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Multi_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Multi_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Multi_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Multi_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Multi_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Multi_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Multi_Bin_Freq_Plot[[7]])
})
output$MultiBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Multi_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Multi_Bin_Par_Plot[[2]],
"CD"=All_Plots$Multi_Bin_Par_Plot[[3]],
"ED"=All_Plots$Multi_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Multi_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Multi_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Multi_Bin_Par_Plot[[7]])
})
output$LovMultiBinDistFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$LMulti_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$LMulti_Bin_Freq_Plot[[2]],
"CD"=All_Plots$LMulti_Bin_Freq_Plot[[3]],
"ED"=All_Plots$LMulti_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$LMulti_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$LMulti_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$LMulti_Bin_Freq_Plot[[7]])
})
output$LovMultiBinDistParPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$LMulti_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$LMulti_Bin_Par_Plot[[2]],
"CD"=All_Plots$LMulti_Bin_Par_Plot[[3]],
"ED"=All_Plots$LMulti_Bin_Par_Plot[[4]],
"PDID"=All_Plots$LMulti_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$LMulti_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$LMulti_Bin_Par_Plot[[7]])
})
output$BinPlot1 <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Bin_Plot[[1]],
"ADW2" = All_Plots$Bin_Plot[[2]],
"CD" = All_Plots$Bin_Plot[[3]],
"ED" = All_Plots$Bin_Plot[[4]],
"PDID" = All_Plots$Bin_Plot[[5]],
"TDArg" = All_Plots$Bin_Plot[[6]],
"TDUSA" = All_Plots$Bin_Plot[[7]])
})
output$TriBinPlot <- renderPlot({
switch(input$Datasets,
"ADW1" = All_Plots$Tri_Bin_Plot[[1]],
"ADW2" = All_Plots$Tri_Bin_Plot[[2]],
"CD" = All_Plots$Tri_Bin_Plot[[3]],
"ED" = All_Plots$Tri_Bin_Plot[[4]],
"PDID" = All_Plots$Tri_Bin_Plot[[5]],
"TDArg" = All_Plots$Tri_Bin_Plot[[6]],
"TDUSA" = All_Plots$Tri_Bin_Plot[[7]])
})
output$BetaBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Beta_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Beta_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Beta_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Beta_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Beta_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Beta_Bin_Freq_Plot[[7]])
})
output$BetaBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Beta_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Beta_Bin_Par_Plot[[2]],
"CD"=All_Plots$Beta_Bin_Par_Plot[[3]],
"ED"=All_Plots$Beta_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Beta_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Beta_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Beta_Bin_Par_Plot[[7]])
})
output$KumBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Kum_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Kum_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Kum_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Kum_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Kum_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Kum_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Kum_Bin_Freq_Plot[[7]])
})
output$KumBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Kum_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Kum_Bin_Par_Plot[[2]],
"CD"=All_Plots$Kum_Bin_Par_Plot[[3]],
"ED"=All_Plots$Kum_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Kum_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Kum_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Kum_Bin_Par_Plot[[7]])
})
output$GamBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Gam_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Gam_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Gam_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Gam_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Gam_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Gam_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Gam_Bin_Freq_Plot[[7]])
})
output$GamBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Gam_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Gam_Bin_Par_Plot[[2]],
"CD"=All_Plots$Gam_Bin_Par_Plot[[3]],
"ED"=All_Plots$Gam_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Gam_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Gam_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Gam_Bin_Par_Plot[[7]])
})
output$GraBinFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Grassia_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$Grassia_Bin_Freq_Plot[[2]],
"CD"=All_Plots$Grassia_Bin_Freq_Plot[[3]],
"ED"=All_Plots$Grassia_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$Grassia_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$Grassia_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$Grassia_Bin_Freq_Plot[[7]])
})
output$GraBinParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$Grassia_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$Grassia_Bin_Par_Plot[[2]],
"CD"=All_Plots$Grassia_Bin_Par_Plot[[3]],
"ED"=All_Plots$Grassia_Bin_Par_Plot[[4]],
"PDID"=All_Plots$Grassia_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$Grassia_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$Grassia_Bin_Par_Plot[[7]])
})
output$GHGBBFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$GHGBeta_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$GHGBeta_Bin_Freq_Plot[[2]],
"CD"=All_Plots$GHGBeta_Bin_Freq_Plot[[3]],
"ED"=All_Plots$GHGBeta_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$GHGBeta_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$GHGBeta_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$GHGBeta_Bin_Freq_Plot[[7]])
})
output$GHGBBParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$GHGBeta_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$GHGBeta_Bin_Par_Plot[[2]],
"CD"=All_Plots$GHGBeta_Bin_Par_Plot[[3]],
"ED"=All_Plots$GHGBeta_Bin_Par_Plot[[4]],
"PDID"=All_Plots$GHGBeta_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$GHGBeta_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$GHGBeta_Bin_Par_Plot[[7]])
})
output$McGBBFreqPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$McGBB_Bin_Freq_Plot[[1]],
"ADW2"=All_Plots$McGBB_Bin_Freq_Plot[[2]],
"CD"=All_Plots$McGBB_Bin_Freq_Plot[[3]],
"ED"=All_Plots$McGBB_Bin_Freq_Plot[[4]],
"PDID"=All_Plots$McGBB_Bin_Freq_Plot[[5]],
"TDArg"=All_Plots$McGBB_Bin_Freq_Plot[[6]],
"TDUSA"=All_Plots$McGBB_Bin_Freq_Plot[[7]])
})
output$McGBBParaPlot <- renderPlot({
switch(input$Datasets,
"ADW1"=All_Plots$McGBB_Bin_Par_Plot[[1]],
"ADW2"=All_Plots$McGBB_Bin_Par_Plot[[2]],
"CD"=All_Plots$McGBB_Bin_Par_Plot[[3]],
"ED"=All_Plots$McGBB_Bin_Par_Plot[[4]],
"PDID"=All_Plots$McGBB_Bin_Par_Plot[[5]],
"TDArg"=All_Plots$McGBB_Bin_Par_Plot[[6]],
"TDUSA"=All_Plots$McGBB_Bin_Par_Plot[[7]])
})
output$ABD_Table_Plot<-renderUI({
Results<-switch(input$Datasets,
"ADW1"=All_Plots$ABD_Table[[1]],
"ADW2"=All_Plots$ABD_Table[[2]],
"CD"=All_Plots$ABD_Table[[3]],
"ED"=All_Plots$ABD_Table[[4]],
"PDID"=All_Plots$ABD_Table[[5]],
"TDArg"=All_Plots$ABD_Table[[6]],
"TDUSA"=All_Plots$ABD_Table[[7]])
All_Datas<-switch (input$Datasets,
"ADW1"=All_Plots$All_Data[[1]],
"ADW2"=All_Plots$All_Data[[2]],
"CD"=All_Plots$All_Data[[3]],
"ED"=All_Plots$All_Data[[4]],
"PDID"=All_Plots$All_Data[[5]],
"TDArg"=All_Plots$All_Data[[6]],
"TDUSA"=All_Plots$All_Data[[7]])
flextable::flextable(data=Results,
col_keys = c("Bin_RV","Actual_Freq","EstFreq_BinD",
"EstFreq_AddBinD","EstFreq_BetaCorrBinD",
"EstFreq_COMPBinD","EstFreq_CorrBinD",
"EstFreq_MultiBinD","EstFreq_LMBinD")) |>
flextable::theme_box() |> flextable::autofit() |>
flextable::fontsize(i=c(1:nrow(Results)),j=c(1:ncol(Results)),size = 12,part = "body") |>
flextable::fontsize(i=1,j=c(1:ncol(Results)),size = 13,part = "header") |>
flextable::bold(i=1,part = "header") |>
flextable::bold(i=c((length(All_Datas[,1])+1):nrow(Results)),j=1,part = "body") |>
flextable::align(i=c(1:nrow(Results)),j=c(1:ncol(Results)),align = "center") |>
flextable::set_header_labels(values = c(Bin_RV="Binomial Random Variable",
Actual_Freq="Observed Frequencies",
EstFreq_BinD="Binomial Distribution",
EstFreq_AddBinD="Additive Binomial Distribution",
EstFreq_BetaCorrBinD="Beta-Correlated Binomial Distribution",
EstFreq_COMPBinD="Composite Binomial Distribution",
EstFreq_CorrBinD="Correlated Binomial Distribution",
EstFreq_MultiBinD="Multiplicative Binomial Distribution",
EstFreq_LMBinD="Lovinson Multiplicative Binomial Distribution")) |>
flextable::align(i=1,part = "header",align = "center") |>
flextable::htmltools_value()
})
output$BMD_Table_Plot<-renderUI({
Results<-switch(input$Datasets,
"ADW1"=All_Plots$BMD_Table[[1]],
"ADW2"=All_Plots$BMD_Table[[2]],
"CD"=All_Plots$BMD_Table[[3]],
"ED"=All_Plots$BMD_Table[[4]],
"PDID"=All_Plots$BMD_Table[[5]],
"TDArg"=All_Plots$BMD_Table[[6]],
"TDUSA"=All_Plots$BMD_Table[[7]])
All_Datas<-switch (input$Datasets,
"ADW1"=All_Plots$All_Data[[1]],
"ADW2"=All_Plots$All_Data[[2]],
"CD"=All_Plots$All_Data[[3]],
"ED"=All_Plots$All_Data[[4]],
"PDID"=All_Plots$All_Data[[5]],
"TDArg"=All_Plots$All_Data[[6]],
"TDUSA"=All_Plots$All_Data[[7]])
flextable::flextable(data=Results,
col_keys = c("Bin_RV","Actual_Freq","EstFreq_BinD",
"EstFreq_TriBinD","EstFreq_BetaBinD","EstFreq_KumBinD",
"EstFreq_GammaBinD","EstFreq_GrassiaIIBinD",
"EstFreq_GHGBBD","EstFreq_McGBBD")) |>
flextable::theme_box() |> flextable::autofit()|>
flextable::fontsize(i=c(1:nrow(Results)),j=c(1:ncol(Results)),size = 12,part = "body") |>
flextable::fontsize(i=1,j=c(1:ncol(Results)),size = 13,part = "header") |>
flextable::bold(i=1,part = "header") |>
flextable::bold(i=c((length(All_Datas[,1])+1):nrow(Results)),j=1,part = "body") |>
flextable::align(i=c(1:nrow(Results)),j=c(1:ncol(Results)),align = "center") |>
flextable::set_header_labels(values = c(Bin_RV="Binomial Random Variable",
Actual_Freq="Observed Frequencies",
EstFreq_BinD="Binomial Distribution",
EstFreq_TriBinD="Triangular Binomial Distribution",
EstFreq_BetaBinD="Beta Binomial Distribution",
EstFreq_KumBinD="Kumaraswamy Binomial Distribution",
EstFreq_GammaBinD="Gamma Binomial Distribution",
EstFreq_GrassiaIIBinD="Grassia II Binomial Distribution",
EstFreq_GHGBBD="GHG Beta Binomial Distribution",
EstFreq_McGBBD="McG Beta Binomial Distribution")) |>
flextable::align(i=1,part = "header",align = "center") |>
flextable::htmltools_value()
})
output$Text_ABD<-renderText({
switch(input$Datasets,
"ADW1" = HTML("<li> For the Alcohol Data Week 1, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 6.2538 while the estimated variance is 1.696 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of variance difference
and count difference it seems that Beta Correlated Binomial distribution is the best choice."),
"ADW2" = HTML("<li> For the Alcohol Data Week 2, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.7873 while the estimated variance is 1.6945 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of count difference
and p-value Beta Correlated Binomial distribution is the best choice. <li> Composite binomial, Multiplicative
binomial and Lovinson Multiplicative binomial have smaller variance differences compared to Beta Correlated
Binomial distribution."),
"CD" = HTML("<li> For the Course Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.2562 while the estimated variance is 1.7094 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the results of count difference
clearly all distributions are suitable. <li> According to p-value the best choice is Beta-Correlated Binomial
distribution. <li> While Composite Binomial, Multiplicative Binomial and Lovinson Multiplicative Binomial
have the least variance differences."),
"ED" = HTML("<li> For the Exam Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 2.8245 while the estimated variance is 1.2156 and the frequencies are vastly different
as well. <li> Next Alternate Binomial distributions are fitted and based on the count difference
clearly distributions Multiplicative and Lovinson Multiplicative Binomial are suitable. <li> According
to p-value the above two distributions are the best choices as well and this is the case for variance difference
as well."),
"PDID" = HTML("<li> For the Plant Disease Incidence Data, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.8946 while the estimated variance is 1.2647 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
count difference all distributions are suitable. <li> According to p-value the distributions Multiplicative binomial
and Lovinson Multiplicative binomial are the best choices. <li> Based on variance difference the best choice is
Composite Binomial distribution."),
"TDArg" = HTML("<li> For the Terror Data of Argentina, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.9654 while the estimated variance is 0.6486 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
variance difference Additive Binomial and Correlated Binomial distributions are suitable. <li> According to p-value
the Composite Binomial distribution is the best choice."),
"TDUSA" = HTML("<li> For the Terror Data of USA, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 0.99 while the estimated variance is 0.5689 and the frequencies
are vastly different as well. <li> Next Alternate Binomial distributions are fitted and based on the results of
variance difference Composite Binomial distribution is suitable. <li> According to p-value the distributions
the Multiplicative Binomial and Lovinson Multiplicative Binomial distribution are the best choices."))
})
output$Text_BMD<-renderText({
switch(input$Datasets,
"ADW1" = HTML("<li> For the Alcohol Data Week 1, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 6.2538 while the estimated variance is 1.696 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of variance difference the
best choice is Beta-Binomial distribution. <li> According to count difference and p-value it seems that Gaussian
Hypergeometric Generalized Beta Binomial distribution is the best choice."),
"ADW2" = HTML("<li> For the Alcohol Data Week 2, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.7873 while the estimated variance is 1.6945 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of variance difference the
best choice is Gamma Binomial distribution. <li> According to count difference McDonald Generalized Beta Binomial
and based on p-value Gaussian Hypergeometric Generalized Beta Binomial are the best choices."),
"CD" = HTML("<li> For the Course Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 5.2562 while the estimated variance is 1.7094 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the results of count difference
clearly all distributions are suitable. <li> According to p-value the best choice is Kumaraswamy Binomial
distribution. <li> While Gaussian Hypergeometric Generalized Beta Binomial distribution has the least variance
difference."),
"ED" = HTML("<li> For the Exam Data, after fitting the binomial distribution it is clear that there is overdispersion.
<li> The observed variance is 2.8245 while the estimated variance is 1.2156 and the frequencies are vastly different
as well. <li> Next Binomial Mixture distributions are fitted and based on the count difference and
p-value clearly Gaussian Hypergeometric Generalized Beta Binomial distribution is suitable. <li> According
to variance difference the distributions Beta Binomial and Grassia II Binomial distributions are suitable."),
"PDID" = HTML("<li> For the Plant Disease Incidence Data, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.8946 while the estimated variance is 1.2647 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on the results of
count difference all distributions are suitable except for Triangular Binomial. <li> According to variance
difference the best choices are Beta Binomial, Kumaraswamy Binomial Grassia II Binomial and Gaussian
Hypergeometric Generalized Beta Binomial distributions."),
"TDArg" = HTML("<li> For the Terror Data of Argentina, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 1.9654 while the estimated variance is 0.6486 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on the results of
variance difference Gaussian Hypergeometric Generalized Beta Binomial is suitable. <li> Based on p-value
the Gamma Binomial distribution is the best choice and except for Triangular Binomial all other distributions
are a good choice based on count differences."),
"TDUSA" = HTML("<li> For the Terror Data of USA, after fitting the binomial distribution it is clear that there is
overdispersion. <li> The observed variance is 0.99 while the estimated variance is 0.5689 and the frequencies
are vastly different as well. <li> Next Binomial Mixture distributions are fitted and based on
variance difference Gaussian Hypergeometric Generalized Beta Binomial distribution is suitable. <li> According
to p-value the distribution Gamma Binomial is the best choice."))
})
observeEvent(input$download_ABD,{
shinyscreenshot::screenshot()
})
observeEvent(input$download_BMD, {
shinyscreenshot::screenshot()
})
}
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