Not Applicable Predict Method

[gcefalu]

Please see code below. I get the error: Warning: Error in UseMethod: no applicable method for 'predict' applied to an object of class "c('reactiveExpr', 'reactive', 'function')"

library(shiny)
library(ggplot2)
library(caret)



# Define server logic required to draw a histogram
function(input, output, session) {
  
  output$data <- renderTable({
    
    mtcars[, c(input$variable), drop = FALSE]
  }, rownames = TRUE)
  
  
  output$data2 <- renderTable({
   
    mtcars[, c(input$variable2), drop = FALSE]
  }, rownames = TRUE)
  
  output$summary <- renderText({
    paste("SUMMARY STATISTICS")
  })
  output$sum <- renderTable({
    summary(mtcars)
  })
  
  
   model <- reactive({
     lm(mtcars[, c(input$variable)] ~ mtcars[ , c(input$variable2)], data = mtcars)
   })
 
  
   output$reg <- renderText({
     paste("REGRESSION COEFFICIENTS AND RESIDUALS")
     
   })
   output$coef <- renderTable({
    as.data.frame(model()$coefficients)
    
  })
   
  output$res <- renderTable({
    as.data.frame(model()$residuals)
  })

      

  
  output$num <- renderText({
    
    req(input$obs)
    
  })
  
  input_value <- reactive({
    input$obs
    
  })
  

  predic <- reactive({
    predict(model, newdata = data.frame(input_value))
     
 })
  

  
  output$p <- renderText({
    
     predic()
  })
  

  

#  output$plot <- renderPlot({
#    ggplot(mtcars, aes(mtcars[,c(input$variable)], mtcars[, c(input$variable2)])) 
#  })
  
 # output$data3 <- renderTable({
 #  brushedPoints(mtcars, input$plot_brush, xvar = input$variable, yvar =  input$variable2)
  
 # })
}

[gcefalu]

I found the solution to the orevious error. instead of model in the predict function I have to use model() because it ia a reactive function. Please see below. But this time I get a warning :" Warning: 'newdata' had 1 row but variables found have 32 rows". So this script asks input from the user (just one number) to make a prediction with that number, however the function returns many values, not just 1. How can I fix this?

library(shiny)
library(ggplot2)
library(caret)
library(shinyPredict)



# Define server logic required to draw a histogram
function(input, output, session) {
  
  output$data <- renderTable({
    
    mtcars[, c(input$variable), drop = FALSE]
  }, rownames = TRUE)
  
  
  output$data2 <- renderTable({
   
    mtcars[, c(input$variable2), drop = FALSE]
  }, rownames = TRUE)
  
  output$summary <- renderText({
    paste("SUMMARY STATISTICS")
  })
  output$sum <- renderTable({
    summary(mtcars)
  })
  
  
   model <- reactive({
     lm(mtcars[, c(input$variable)] ~ mtcars[ , c(input$variable2)], data = mtcars)
   })
 
  
   output$reg <- renderText({
     paste("REGRESSION COEFFICIENTS AND RESIDUALS")
     
   })
   output$coef <- renderTable({
    as.data.frame(model()$coefficients)
    
  })
   
  output$res <- renderTable({
    as.data.frame(model()$residuals)
  })

      

  
  output$num <- renderText({
    
    req(input$obs)
    
  })
  
  input_value <- reactive({
     req(input$obs)
  })
  
  
  predic <- reactive({
    predict(model(), newdata = data.frame(input_value()))
     
 })
  

  
  output$p <- renderText({
    
     predic()
  })
  

  

#  output$plot <- renderPlot({
#    ggplot(mtcars, aes(mtcars[,c(input$variable)], mtcars[, c(input$variable2)])) 
#  })
  
 # output$data3 <- renderTable({
 #  brushedPoints(mtcars, input$plot_brush, xvar = input$variable, yvar =  input$variable2)
  
 # })
  
}

[gcefalu]

I realized that if I want to predict for one value, I just need to output the coefficients to build the equation