R/demo_sim_Ricker.R

In BruceKendall/PVA: Population viability analysis in R

#' Stochastic Ricker model demo
#' 
#' A Shiny demo of the stochastic Ricker model. The user can set the low-density growth rate,
#' the equilibrium abundance, 
#' the variance of the log growth rate, the initial population size, and the length of simulation.
#' The number of replicate simulations can also be changed.
#'
#' Four graphs are produced. The left plots are time series of abundance, plotted on a linear 
#' scale (top) and a logarithmic scale (bottom). The right plots are histograms of the final 
#' abundance and final log abundance across the replicate simulations.
#' 
#' The simulations update immediately when you change any parameter value. To
#' see a new set of stochastic simulations without changing any parameters, hit 
#' the "Run again" button.
#' 
#' 
#' @return Nothing is returned; the commmand is run for its side effect of 
#' launching a shiny app. You will have to close the shiny window or hit the
#' Stop button in the console to get the console prompt back.
#' 
demo_sim_Ricker <- function() {
  require(shiny)
  require(PVA)
  shinyApp(
    ui = fluidPage(
      # Application title
      titlePanel("Stochastic Ricker model simulation"),
      
      # Sidebar with a slider input for the number of bins
      sidebarLayout(
        sidebarPanel(
          numericInput("r",
                       "Low-density growth rate (r)",
                       value=1,
                       min=0),
          numericInput("K",
                       "Equilibrium abundance (K)",
                       value=100,
                       min=0),
          numericInput("sigma2",
                       "Variance of log growth rate (sigma2)",
                       value=0.01,
                       min=0,
                       max=1),
          numericInput("N0",
                       "Initial population size (N0)",
                       value=100,
                       min=1),
          numericInput("time_horizon",
                       "Number of years to simulate",
                       value=10,
                       min=2),
          numericInput("num_sims",
                       "Number of simulations",
                       value=1,
                       min=1),
          br(),
          actionButton("runAgain",
                       "Run Again")
        ),
        
        # Show a plot of the generated distribution
        mainPanel(
          fluidRow(
            column(8,
                   plotOutput("tsPlot")
                   
            ),
            column(4,
                   plotOutput("finalHist"))
          ),
          fluidRow(
            column(8,
                   plotOutput("tsPlotlog")
                   
            ),
            column(4,
                   plotOutput("finalHistlog"))
          )
        )
      )
    ),
    server = function(input,output) {
      Years <- reactive({ 0:input$time_horizon })
      Nt1 <- reactive({
        input$runAgain
        simulateRicker(input$r, input$K, input$sigma2, input$N0, 
                    input$time_horizon, input$num_sims, TRUE)
        
      })
      
      
      
      output$tsPlot <- renderPlot({
        matplot(Years(), Nt1(), type='l', xlab="Time (t)", ylab=expression(N[t]),
                main="Time series") 
      })
      output$tsPlotlog <- renderPlot({
        matplot(Years(), Nt1(), type="l", log="y", xlab="Time (t)", 
                ylab=expression(N[t]~~(log~~scale)))
      })
      output$finalHist <- renderPlot({
        hist(as.matrix(Nt1())[(input$time_horizon+1),],
             main="Final Distribution", xlab=expression(N[t]))
      })
      output$finalHistlog <- renderPlot({
        hist(as.matrix(log(Nt1()))[(input$time_horizon+1),],
             main="", xlab=expression(log(N[t])))        
      })
      
      
    }
  )
}