ctree-module: Shiny module to build and analyse conditional inference trees

In moreparty: A Toolbox for Conditional Inference Trees and Random Forests

Shiny module to build and analyse conditional inference trees

Description

The module builds a conditional inference trees according to several parameter inputs. Then it plots the tree and computes performance measures, variable importance, checks the stability and return the code to reproduce the analyses.

Usage

ctreeUI(id)

ctreeServer(id, data, name)

Arguments

id	Module id. See shiny::callModule().
data	shiny::reactive() function returning a data.frame to use for the analyses.
name	shiny::reactive() function returning a character string representing data name.

Author(s)

Nicolas Robette

References

Hothorn T, Hornik K, Van De Wiel MA, Zeileis A. "A lego system for conditional inference". The American Statistician. 60:257–263, 2006.

Hothorn T, Hornik K, Zeileis A. "Unbiased Recursive Partitioning: A Conditional Inference Framework". Journal of Computational and Graphical Statistics, 15(3):651-674, 2006.

See Also

ictree

Examples

library(shiny)
library(moreparty)

data(titanic)

ui <- fluidPage(
  titlePanel("Conditional inference trees"),
  ctreeUI(id = "ctree_app")
)

server <- function(input, output, session) {
  rv <- reactiveValues(
    data = titanic,
    name = deparse(substitute(titanic))
  )
  ctreeServer(id = "ctree_app", reactive(rv$data), reactive(rv$name))
}

if (interactive())
  shinyApp(ui, server)

moreparty documentation built on Nov. 22, 2023, 5:08 p.m.