#
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
require(shiny)
require(grctoolkit)
require(ggplot2)
require(colorspace)
require(labeling)
require(cowplot)
require(ggExtra)
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
# Declare user session variables
factor_estimate_1 <- factor_estimate_gld_3points$new(
estimated_range_min_value = -100,
estimated_mode_value = 0,
estimated_range_max_value = 100
)
update_factor_estimate <- reactive({
factor_estimate_1$estimated_range_min_value <- input$estimated_range_min_value
factor_estimate_1$estimated_mode_value <- input$estimated_mode_value
factor_estimate_1$estimated_range_max_value <- input$estimated_range_max_value
factor_estimate_1$estimated_range_min_proba <- input$estimated_range_min_proba
factor_estimate_1$estimated_range_max_proba <- input$estimated_range_max_proba
factor_estimate_1$limit_min_value <- input$limit_min_value
factor_estimate_1$limit_min_behavior <- input$limit_min_behavior
factor_estimate_1$limit_max_value <- input$limit_max_value
factor_estimate_1$limit_max_behavior <- input$limit_max_behavior
factor_estimate_1$fit_distribution()
factor_estimate_1$simulate()
})
output$fit_distribution_summary <- renderText({
update_factor_estimate()
})
output$plot_density <- renderPlot({
update_factor_estimate()
factor_estimate_1$plot_density(x_start = input$x_start, x_end = input$x_end)
})
output$plot_quantile <- renderPlot({
update_factor_estimate()
factor_estimate_1$plot_quantile()
})
output$plot_probability <- renderPlot({
update_factor_estimate()
factor_estimate_1$plot_probability(x_start = input$x_start, x_end = input$x_end)
})
output$plot_simulation_sample <- renderPlot({
update_factor_estimate()
factor_estimate_1$plot_simulation_sample()
})
output$text_summary <- renderPrint({
update_factor_estimate()
print(factor_estimate_1)
})
output$table_simulation_sample_head <- renderTable({
update_factor_estimate()
factor_estimate_1$get_simulation_sample_head(n = input$simulation_sample_sample_size)
})
output$table_simulation_sample_tail <- renderTable({
update_factor_estimate()
factor_estimate_1$get_simulation_sample_tail(n = input$simulation_sample_sample_size)
})
output$table_simulation_sample_random <- renderTable({
update_factor_estimate()
factor_estimate_1$get_simulation_sample_random(n = input$simulation_sample_sample_size)
})
})
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