airt (version 0.2.2)

effectiveness_crm: Computes the actual and predicted effectiveness of the collection of algorithms.

Description

This function computes the actual and predicted effectiveness of the collection of algorithms for different tolerance values.

Usage

effectiveness_crm(model)
# S3 method for effectivenesscrm
autoplot(object, plottype = 1, ...)

Value

A list with the following components:

effectivenessAUC

The area under the actual and predicted effectiveness curves.

actcurves

The x,y coodinates for the actual effectiveness curves for each algorithm.

#'

prdcurves

The x,y coodinates for the predicted effectiveness curves for each algorithm.

Arguments

model

The output of the function cirtmodel.

object

For autoplot: The output of the function effectiveness_crm

plottype

For autoplot: If plottype = 1, then actual effectiveness is plotted, if plottype = 2, then predicted effectiveness is plotted. If plottype = 3, area under the actual effectiveness curve (AUAEC) is plotted against area under the predicted effectiveness curve (AUPEC).

...

Other arguments currently ignored.

Examples

set.seed(1)# \donttest{
x1 <- runif(200)
x2 <- 2*x1 + rnorm(200, mean=0, sd=0.1)
x3 <- 1 - x1 + rnorm(200, mean=0, sd=0.1)
X <- cbind.data.frame(x1, x2, x3)
mod <- cirtmodel(X)
out <- effectiveness_crm(mod)
out
# For the actual effectiveness plot
autoplot(out, plottype = 1)
# For the predicted effectivness plot
autoplot(out, plottype = 2)
# For actual and predicted effectiveness plot
autoplot(out, plottype = 3)

# }