fitdist在r中闪亮，误差需要有限'；xlim&x27；价值观

我正在创建我的第一个闪亮的应用程序，我正在努力从

fitdistrplus

软件包中绘制四个拟合优度测试。（对于参考资料，我试图从这里的参考资料复制第7页的绘图：

简单地说，我希望用户根据变量M选择数据子集，然后评估变量Q的不同概率分布密度。我使用代码创建了Shiny之外的拟合优度图，效果非常好。在R Shining中，我能够单独绘制拟合（fw、fg、fl）但在使用

denscomp

、

qqcomp

、

cdfcomp

和

ppcomp

时，我会收到以下错误消息：

错误：需要有限的“xlim”值

我试图在代码中添加

xlim

和

ylim

（例如：

xlim=c（0300），ylim=c（0.008）

），但仍然收到了错误消息

有人知道如何解决这个问题吗

我的代码如下：

library(fitdistrplus)
library(shiny)
library(dplyr)

ui<-  shinyUI(pageWithSidebar(

headerPanel("Distribution analysis"),

sidebarPanel(
  selectInput("input1", 
              label = "M",
              choices = data$m,
              selected = "M1"),

mainPanel(
  tabsetPanel(
    tabPanel("Fit", plotOutput("fit1")), 
    tabPanel("Distribution", plotOutput("hist1")), 
    tabPanel("Table", tableOutput("table"))
))
))

server<- shinyServer(function(input, output) {

dataInput <- reactive({
  data %>%
    filter(m==input$input1)
})

fw<- eventReactive(input$input1 {
  fitdist(dataInput()$Q, "weibull")
  })

fg<- eventReactive(input$input1 {
  fitdist(dataInput()$Q, "gamma")
  })

fln<- eventReactive(input$input1 {
  fitdist(dataInput()$Q, "lnorm")
  })

output$fit1 <- renderPlot({
  if (!is.null(dataInput())) {
   par(mfrow = c(2, 2))
   plot.legend <- c("Weibull", "lognormal", "gamma")
   denscomp(list(fw, fln, fg), legendtext = plot.legend)
   qqcomp(list(fw, fln, fg), legendtext = plot.legend)
   cdfcomp(list(fw, fln, fg), legendtext = plot.legend)
   ppcomp(list(fw, fln, fg), legendtext = plot.legend) 

    }
})
})  

shinyApp(ui=ui, server=server)  

库（FitDistripPlus）
图书馆（闪亮）
图书馆（dplyr）
ui我修复了您的一些问题，但由于我不熟悉package
FitDistripPlus
我无法完全调试其他警告。请注意，所有的反应式都是函数，因此应该这样使用，例如：
fln（）
而不是
fln

#rm(list = ls())
library(fitdistrplus)
library(shiny)
library(dplyr)

m<- c("M1","M3","M3", "M2", "M3","M2","M2","M1","M1","M1","M1","M3","M3","M2","M2","M1","M3","M3", "M3","M2","M2","M2","M1","M1","M1","M1","M1","M3","M3","M3" )
Q<- c(265, 65, 40, 245,230,175, 185, 190, 290, 85, 75, 155, 110, 60, 35, 245, 300,175, 180, 265, 55, 200, 95, 185, 165, 55, 90, 190, 235, 200) 
data<- data.frame(m,Q)

ui<-  shinyUI(pageWithSidebar(
  headerPanel("Distribution analysis"),
  sidebarPanel(  selectInput("input1", label = "M",choices = data$m,selected = "M1")),

  mainPanel(
    tabsetPanel(
      tabPanel("Fit", plotOutput("fit1")), 
      tabPanel("Distribution", plotOutput("hist1")), 
      tabPanel("Table", tableOutput("table"))
    ))
))

server<- shinyServer(function(input, output) {

  dataInput <- reactive({
    if(is.null(input$input1)){
      return()
    }
    data %>% filter(m==input$input1)
  })

  fw <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "weibull")
  })

  fg <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "gamma")
  })

  fln <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "lnorm")
  })

  output$fit1 <- renderPlot({
    if(is.null(dataInput()) | nrow(dataInput()) ==0){
      return()
    }
    par(mfrow = c(2, 2))
    plot.legend <- c("Weibull", "lognormal", "gamma")
    denscomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    qqcomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    cdfcomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    ppcomp(list(fw(), fln(), fg()), legendtext = plot.legend) 
  })
})  

shinyApp(ui=ui, server=server) 

#rm（list=ls（））
图书馆（FitPlus）
图书馆（闪亮）
图书馆（dplyr）
谢谢你的回答。它确实解决了问题。也谢谢你的建议。非常感谢。
#rm(list = ls())
library(fitdistrplus)
library(shiny)
library(dplyr)

m<- c("M1","M3","M3", "M2", "M3","M2","M2","M1","M1","M1","M1","M3","M3","M2","M2","M1","M3","M3", "M3","M2","M2","M2","M1","M1","M1","M1","M1","M3","M3","M3" )
Q<- c(265, 65, 40, 245,230,175, 185, 190, 290, 85, 75, 155, 110, 60, 35, 245, 300,175, 180, 265, 55, 200, 95, 185, 165, 55, 90, 190, 235, 200) 
data<- data.frame(m,Q)

ui<-  shinyUI(pageWithSidebar(
  headerPanel("Distribution analysis"),
  sidebarPanel(  selectInput("input1", label = "M",choices = data$m,selected = "M1")),

  mainPanel(
    tabsetPanel(
      tabPanel("Fit", plotOutput("fit1")), 
      tabPanel("Distribution", plotOutput("hist1")), 
      tabPanel("Table", tableOutput("table"))
    ))
))

server<- shinyServer(function(input, output) {

  dataInput <- reactive({
    if(is.null(input$input1)){
      return()
    }
    data %>% filter(m==input$input1)
  })

  fw <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "weibull")
  })

  fg <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "gamma")
  })

  fln <- eventReactive(input$input1, {
    fitdist(dataInput()$Q, "lnorm")
  })

  output$fit1 <- renderPlot({
    if(is.null(dataInput()) | nrow(dataInput()) ==0){
      return()
    }
    par(mfrow = c(2, 2))
    plot.legend <- c("Weibull", "lognormal", "gamma")
    denscomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    qqcomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    cdfcomp(list(fw(), fln(), fg()), legendtext = plot.legend)
    ppcomp(list(fw(), fln(), fg()), legendtext = plot.legend) 
  })
})  

shinyApp(ui=ui, server=server)