compare: Compare two NP classification methods at different type I...

In nproc: Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves

Description

compare compares NP classification methods and provides the regions where one method is better than the other.

Usage

compare(roc1, roc2, plot = TRUE, col1 = "black", col2 = "red")

Arguments

roc1	the first nproc object.
roc2	the second nproc object.
plot	whether to generate the two NP-ROC plots and mark the area of significant difference. Default = 'TRUE'.
col1	the color of the region where roc1 is significantly better than roc2. Default = 'black'.
col2	the color of the region where roc2 is significantly better than roc1. Default = 'red'.

Value

A list with the following items.

alpha1	the alpha values where roc1 is significantly better than roc2.
alpha2	the alpha values where roc2 is significantly better than roc1.
alpha3	the alpha values where roc1 and roc2 are not significantly different.

References

Xin Tong, Yang Feng, and Jingyi Jessica Li (2018), Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristic (NP-ROC), Science Advances, 4, 2, eaao1659.

See Also

npc, nproc, predict.npc and plot.nproc

Examples

n = 1000
set.seed(1)
x1 = c(rnorm(n), rnorm(n) + 1)
x2 = c(rnorm(n), rnorm(n)*sqrt(6) + 1)
y = c(rep(0,n), rep(1,n))
fit1 = nproc(x1, y, method = 'lda')
fit2 = nproc(x2, y, method = 'lda')
v = compare(fit1, fit2)
legend('topleft',legend=c('x1','x2'),col=1:2,lty=c(1,1))

nproc documentation built on March 26, 2020, 7:30 p.m.