source("dWALD.R")
source("WALD.R")
source("dExWALD.R")
source("ExWALD.R")

library(shiny)
library(MASS) 
library(gamlss)

shinyServer(function(input, output) {
  
  datos_reactivo <- reactive({
    req(input$datos)
    
    datos_char <- unlist(strsplit(input$datos, ","))
    datos_num <- as.numeric(trimws(datos_char))
    datos_num <- datos_num[!is.na(datos_num)]
    
    if(length(datos_num) == 0) {
      return(NULL)
    } else {
      return(datos_num)
    }
  })
  
  output$histograma <- renderPlot({
    datos <- datos_reactivo()
    req(datos)
    
    dist_sel <- input$dist
    
    #par(mfrow=c(2, 1))
    
    hist(datos, probability = TRUE, col = "white",
         main = paste("Histogram and estimated density assuming", dist_sel),
         xlab = "RT")
    
    x_seq <- seq(min(datos), max(datos), length.out = 200)
    
    # Estimation y curva según distribución elegida
    if(dist_sel == "Normal") {
      fit <- gamlss(datos~1, family=NO)
      mu    <- coef(fit, what="mu")
      sigma <- exp(coef(fit, what="sigma"))
      y <- dNO(x_seq, mu = mu, sigma = sigma)
      lines(x_seq, y, col = "steelblue2", lwd=3)
      legend("topright", bty="n",
             legend="Estimated density",
             lwd=3, col="steelblue2")
    } else if(dist_sel == "exGAUS") {
      # Ajuste con fitdistr
      fit <- tryCatch({
        mod <- gamlss(datos~1, family=exGAUS,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- coef(fit, what="mu")
        sigma <- exp(coef(fit, what="sigma"))
        nu    <- exp(coef(fit, what="nu"))
        y <- dexGAUS(x_seq, mu=mu, sigma=sigma, nu=nu)
        lines(x_seq, y, col = "steelblue2", lwd=3)
        legend("topright", bty="n",
               legend="Estimated density",
               lwd=3, col="steelblue2")
      }
    } else if(dist_sel == "WALD") {
      # Ajuste con fitdistr
      fit <- tryCatch({
        mod <- gamlss(datos~1, family=WALD,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- exp(coef(fit, what="mu"))
        sigma <- exp(coef(fit, what="sigma"))
        y <- dWALD(x_seq, mu=mu, sigma=sigma)
        lines(x_seq, y, col = "steelblue2", lwd=3)
        legend("topright", bty="n",
               legend="Estimated density",
               lwd=3, col="steelblue2")
      }
    } else if(dist_sel == "ExWALD") {
      fit <- tryCatch({
        fit <- gamlss(datos~1, family=ExWALD,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- exp(coef(fit, what="mu"))
        sigma <- exp(coef(fit, what="sigma"))
        nu    <- exp(coef(fit, what="nu"))
        y <- dExWALD(x_seq, mu=mu, sigma=sigma, nu=nu)
        lines(x_seq, y, col = "steelblue2", lwd=3)
        legend("topright", bty="n",
               legend="Estimated density",
               lwd=3, col="steelblue2")
      }
    }
    # To plot the residual analysis
    #plot(fit)
  })
  
  output$parametros <- renderTable({
    datos <- datos_reactivo()
    req(datos)
    
    dist_sel <- input$dist
    
    if(dist_sel == "Normal") {
      mu <- mean(datos)
      sigma <- sd(datos)
      data.frame(
        Parameter = c("μ", "σ", 
                      "Sample mean", "Sample standard deviation"),
        Estimation = c(mu, sigma, mean(datos), sd(datos))
      )
      
    } else if(dist_sel == "exGAUS") {
      fit <- tryCatch({
        mod <- gamlss(datos~1, family=exGAUS,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- coef(fit, what="mu")
        sigma <- exp(coef(fit, what="sigma"))
        nu    <- exp(coef(fit, what="nu"))
        data.frame(
          Parameter = c("μ", "σ", "ν", 
                        "Sample mean", "Sample standard deviation"),
          Estimation = c(mu, sigma, nu, mean(datos), sd(datos))
        )
      } else {
        data.frame(
          Parameter = "Error",
          Estimation = "We cannot fit WALD"
        )
      }
      
    } else if(dist_sel == "WALD") {
      fit <- tryCatch({
        mod <- gamlss(datos~1, family=WALD,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- exp(coef(fit, what="mu"))
        sigma <- exp(coef(fit, what="sigma"))
        data.frame(
          Parameter = c("μ", "σ", 
                        "Sample mean", "Sample standard deviation"),
          Estimation = c(mu, sigma, mean(datos), sd(datos))
        )
      } else {
        data.frame(
          Parameter = "Error",
          Estimation = "We cannot fit WALD"
        )
      }
      
    } else if(dist_sel == "ExWALD") {
      fit <- tryCatch({
        fit <- gamlss(datos~1, family=ExWALD,
                      control=gamlss.control(n.cyc=5000, trace=TRUE))
      }, error = function(e) NULL)
      
      if(!is.null(fit)) {
        mu    <- exp(coef(fit, what="mu"))
        sigma <- exp(coef(fit, what="sigma"))
        nu    <- exp(coef(fit, what="nu"))
        data.frame(
          Parameter = c("μ", "σ", "ν", 
                        "Sample mean", "Sample standard deviation"),
          Estimation = c(mu, sigma, nu, mean(datos), sd(datos))
        )
      } else {
        data.frame(
          Parameter = "Error",
          Estimation = "We cannot fit ExWALD"
        )
      }
    }
  })
  
})