sera {IRon}

Squared Error-Relevance Area (SERA)

Description

Computes an approximation of the area under the curve described by squared error of predictions for a sequence of subsets with increasing relevance

Usage

sera(
  trues,
  preds,
  phi.trues = NULL,
  ph = NULL,
  pl = FALSE,
  m.name = "Model",
  step = 0.001,
  return.err = FALSE,
  norm = FALSE
)

Arguments

trues

Target values from a test set of a given data set. Should be a vector and have the same size as the variable preds

preds

Predicted values given a certain test set of a given data set. Should be a vector and have the same size as the variable preds

phi.trues

Relevance of the values in the parameter trues. Use ??phi() for more information. Defaults to NULL

ph

The relevance function providing the data points where the pairs of values-relevance are known. Default is NULL

pl

Boolean to indicate if an illustration of the curve should be provided. Default is FALSE

m.name

Name of the model to be appended in the plot title

step

Relevance intervals between 0 (min) and 1 (max). Default 0.001

return.err

Boolean to indicate if the errors at each subset of increasing relevance should be returned. Default is FALSE

norm

Normalize the SERA values for internal optimisation only (TRUE/FALSE)

Value

Value for the area under the relevance-squared error curve (SERA)

Examples

library(IRon)
library(rpart)

if(requireNamespace("rpart")) {

   #' data(accel)

   form <- acceleration ~ .

   ind <- sample(1:nrow(accel),0.75*nrow(accel))

   train <- accel[ind,]
   test <- accel[-ind,]

   ph <- phi.control(accel$acceleration)

   m <- rpart::rpart(form, train)
   preds <- as.vector(predict(m,test))

   trues <- test$acceleration
   phi.trues <- phi(test$acceleration,ph)

   sera(trues,preds,phi.trues)
   sera(trues,preds,phi.trues,pl=TRUE, m.name="Regression Trees")
   sera(trues,preds,phi.trues,pl=TRUE, return.err=TRUE)

}