闪亮的应用错误：sample.int（length（x），size，replace，prob）中的错误：无效的'size'参数

Question

我正在创建一个Shiny应用程序，以使用随机森林模型预测患者的阳性流感病例。用户可以通过应用程序输入一定数量的输入变量，并且可以单击actionButton，然后查看出现正感的可能性。 GeneXpert（二进制编号）是用于训练RF模型的目标变量，所有其他变量本质上都是数字。当我运行该应用程序时，出现错误。

  

sample.int（length（x），size，replace，prob）中的错误：无效的'size'参数

这是我的代码：

library(shiny)
library(randomForest, quietly=TRUE)
library(ggplot2)

ui <- fluidPage(
 titlePanel("Random Forest Flu Prediction Model"),
   sidebarLayout(
    sidebarPanel(
     numericInput("month",
               label = "Enter the Month:",
               min = 1, max = 12, value = 5, step = 1),

    numericInput("DOI", label = "Enter the days of illness:",
               min = 1, max = 7, value = 5, step = 1),

   numericInput(inputId = "age", label = "Enter the age:",
               min = 1, max = 120, value = 5, step = 1),

   numericInput(inputId = "rhi", label = "cursympt_rhinorrhea:",
               min = 0, max = 1, value = 1, step = 1),

   numericInput(inputId = "wt", label = "Enter your weight:",
               min = 1, max = 400, value = 5, step = 0.5),

   numericInput(inputId = "ht", 
               label = "Enter your Height:",
               min = 1, max = 400, value = 1, step = 0.5),

   numericInput(inputId = "sore", label = "cursympt_sorethroat.facto:",
               min = 0, max = 1, value = 1, step = 1),

   numericInput(inputId = "shkilchi", label =       
               "cursympt_shakingchills.facto:", 
               min = 0, max = 1, value = 1, step = 1),

  numericInput(inputId = "shtbt", label = "cursympt_shortnessbreath.facto:", 
               min = 0, max = 1, value = 1, step = 1),

  numericInput(inputId = "appt", label = "cursympt_appetite.factor:",
               min = 0, max = 1, value = 1, step = 1),

  numericInput(inputId = "incsput", label = "cursympt_incrsputum.facto:",
               min = 0, max = 1, value = 1, step = 1),

  numericInput(inputId = "wheez", label = "cursympt_wheezing.facto:",
               min = 0, max = 1, value = 1, step = 1),

  numericInput(inputId = "stom", label = "cursympt_stomachpain.facto:",
               min = 0, max = 1, value = 1, step = 1),
  actionButton("Action", "Analyze")
),

mainPanel(
  textOutput('dynamicText')
    )
   )
 )

server <- function(input, output) {
 dataset =read.csv(file="Flu_Shiny_Data.csv",header=TRUE)
 nobs <- nrow(dataset)
 sample <- train <- sample(nrow(dataset), 0.8*nobs)
 validate <- NULL
 test <- setdiff(setdiff(seq_len(nrow(dataset)), train), validate)

 input_features <- c("age","heightin","weightlb",
            "cursympt_days","cursympt_incrsputum.factor", 
            "cursympt_sorethroat.factor","cursympt_rhinorrhea.factor", 
            "cursympt_shortnessbreath.factor","cursympt_wheezing.factor",
            "cursympt_shakingchills.factor","cursympt_appetite.factor",
            "cursympt_stomachpain.factor","Month")

 input_numbers <- c("age","heightin","weightlb","cursympt_days",                       
         "cursympt_incrsputum.factor","cursympt_sorethroat.factor",                       
         "cursympt_rhinorrhea.factor", "cursympt_shortnessbreath.factor",                       
         "cursympt_wheezing.factor","cursympt_shakingchills.factor",                       
         "cursympt_appetite.factor","cursympt_stomachpain.factor","Month")
 target_feature  <- "GeneXpert"
 result.rf <- randomForest::randomForest(as.factor(GeneXpert) ~ .,
                    data=dataset[sample,c(input_features, target_feature)],                                             
                                        ntree=500,
                                        mtry=3,
                                        importance=TRUE,
                                        na.action=randomForest::na.roughfix,
                                        replace=FALSE)

  values <- reactiveValues()
  observe({
  if(input$Action>0){
    newLine<-isolate(c(input$month,input$DOI,input$age,input$rhi,input$wt,
                       input$ht,input$sore,input$shkilchi,input$shtbt,
                       input$appt,input$incsput,input$wheez,input$stom))
    isolate(values$dataset <- unlist(newLine))
  }
  })

 output$dynamicText <- renderText({
    if (is.null(values$dataset))) 
    return()
    pr <- predict(result.rf, newdata=na.omit(values$dataset))
    pr <- as.numeric(pr)
    ({pr-1})
  })
 }
 # Run the application 
 shinyApp(ui = ui, server = server)