How to use mlrModule

Introduction

soroban’s mlrModule perform Multiple Linear Regression analysis and shows linear model & variable importance.

In this article, we’ll use marketing dataset of datarium

This article is based on 0.0.1 Version of soroban

datarium::marketing

Declare module

soroban’s module assumes that used in the Shiny application.

and You can use snippet(type shinyapp) to build very basic shiny application.

library(shiny)

ui <- fluidPage(
 
)

server <- function(input, output, session) {
  
}

shinyApp(ui, server)

This application will show nothing.

So let’s add pcaModule in ui.

ui <- fluidPage(
  mod_mlrModule_ui(
    id = 'module'
  )
)

also, pcaModule in server.

server <- function(input, output, session) {
  mod_mlrModule_server(
    id = 'module', 
    inputData = reactive(datarium::marketing)
  )
}

So final (which is very basic) code will like this. (Assume data from AER loaded.)

library(shiny)

ui <- fluidPage(
  mod_mlrModule_ui(
    id = 'module'
  )
)

server <- function(input, output, session) {
  mod_mlrModule_server(
    id = 'module', 
    inputData = reactive(datarium::marketing) # remotes::install_github('kassambara/datarium')
  )
}

shinyApp(ui, server) # Run application

You should notice 2 things.

1. both id in ui and server should be same.
2. inputData in server should be format of reactive

Structure of pcaModule

pcaModule is consisted with Control Area and Result Area

and below using flow.

1. Declare module (we did already)
2. select explain, response variables
3. Reg (Regression !)

Usage of mlrModule

Using marketing, we’ll see which factor makes sales.

Select facebook, newspaper, and youtube as X and sales as Y

You can see variable importance in left, (which means Youtube has more effect on sales then Facebook)

and model in right panel ( \(sales \approx 0.1878 \times facebook + 0.045 \times youtube + 3.505\) )

Note that, mlrModule automatically select model by AIC (stepwise) so newspaper was removed in that process (it may have very small effect to sales)

For any issue or suggestion, please make issue in soroban’s github.