Ferroao - 10 months ago 149

R Question

I would like to kindly request some way to predict with lm (linear model) in R, that accepts reactive variables.

If you have a linear model "lm" with y and x, "predict" can be done for new data giving new values for x. I would like to do that for reactive y and x in a shiny app

In the following working example, I created somehow (arbitrarily, just to make it work) reactive y and x values for the lm, and also a input to give a changing new value (to be used as new x).

The objective is to correctly get the predicted y for the new (input) x considering y(), x() .

`library(shiny)`

ui <- fluidPage (

sidebarLayout(

sidebarPanel

(

numericInput('variable1', 'new x', 0.1, min = 0, max = 100, step = 0.1)

),

mainPanel (plotOutput('plot1') )

)

)

server <- function(input, output){

## Initial data and linear regression that should be reactive,

# the dependency on input$variable1<1 is just an example to work with a lm based on reactive data.

y<- reactive (

if (input$variable1<1)

{

y <- c(3.1, 3.25, 3.5, 4, 3.5, 5, 5.5)

}

else

y <- c(.1, .25, .5, 1, 1.5, 2, 2.5)

)

x<- reactive (

if (input$variable1>=1)

{

x <- c(.1, .332, .631, .972, 1.201, 1.584, 1.912)

}

else

x <- c(.1, .3, .631, .972, 2.201, 2.584, 2.912)

)

results <- reactive({

r <- data.frame(y(),x())

})

lmod <- reactive ({

mod1 <- lm(y()~ x(), data = results()

)

})

output$plot1 <- renderPlot({

plot(x(),y())

abline(lmod())

#overwrite problem, we have a new x, which is also x() as the previous

x <-reactive ({ x <- input$variable1 })

newdata <- reactive ({ data.frame(x() ) } )

#problem here, predict does not work with reactive x()

newdata.pred <- reactive ({ predict(lmod(),newdata(),level=1)

})

#problem is visible here, newdata.pred is not the predicted value for the input (new x)

segments(input$variable1, 0, input$variable1, newdata.pred(), col = "red")

} )

} # end server

shinyApp(ui, server)

Answer

It's better when using `lm()`

and `predict`

to have a properly named data.frame and use a proper formula. If you change these parts

```
results <- reactive({
r <- data.frame(y=y(),x=x())
})
lmod <- reactive ({
mod1 <- lm(y~x, data = results() )
})
```

and

```
newdata <- reactive ({ data.frame(x=x() ) } )
```

I think you will get the behavior you want. Now both the model fitting data.frame and the predicting data.frame have a column named `x`

and the formula used in the `lm()`

clearly lists `x`

as the variable used to predict `y`

Source (Stackoverflow)