13  add_dummy_ from node1,function

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Note

The examples in Chapter 13 require

library(maxEff)

🚧 Chapter 13 is being re-written English-wise. All code are correct here, though. Expected delivery by 2025-12-31.

The internal class 'add_dummy_' defined in package maxEff v0.2.4, GPL-2 inherits from the class 'node1' (Chapter 32) and is assigned with additional attributes

• attr(., 'p1'), a numeric scalar between 0 and 1, the TRUE probability of the additional logical predictor in the training set
• attr(., 'effsize'), a numeric scalar, the regression coefficients, i.e., effect size effsize, of the additional logical predictor
• attr(., 'model'), the regression model with additional logical predictor

The S3 method print.default() displays each 'add_dummy_' object.

b0[[1L]]

b0[[2L]]

c0[[1L]]

c0[[2L]]

The S3 method predict.node1() evaluates a dichotomizing rule in a hyper data frame. Note that user must call the S3 method predict.node1() explicitly, otherwise the S3 generic predict() (R version 4.5.3 (2026-03-11)) would dispatch to predict.add_dummy_().

b0[[1L]] |> 
  predict.node1(newdata = s0) |>
  table() |> 
  addmargins()  
b0[[1L]] |> 
  predict.node1(newdata = s1) |>
  table() |> 
  addmargins()

b0[[2L]] |> 
  predict.node1(newdata = s0) |>
  table() |> 
  addmargins() 
b0[[2L]] |> 
  predict.node1(newdata = s1) |>
  table() |> 
  addmargins()  

c0[[1L]] |>
  predict.node1(newdata = s0) |>
  table() |> 
  addmargins()
c0[[1L]] |>
  predict.node1(newdata = s1) |>
  table() |> 
  addmargins()

c0[[2L]] |>
  predict.node1(newdata = s0) |>
  table() |> 
  addmargins()
c0[[2L]] |>
  predict.node1(newdata = s1) |>
  table() |> 
  addmargins()

The S3 method predict.add_dummy_() is the workhorse of the S3 method predict.add_dummy().

b0[[1L]] |> 
  predict(newdata = s1) |>
  identical(y = b1[[1L]]) |>
  stopifnot()

b0[[2L]] |> 
  predict(newdata = s1) |>
  identical(y = b1[[2L]]) |>
  stopifnot()

c0[[1L]] |> 
  predict(newdata = s1) |>
  identical(y = c1[[1L]]) |>
  stopifnot()

c0[[2L]] |> 
  predict(newdata = s1) |>
  identical(y = c1[[2L]]) |>
  stopifnot()