nproc: Calculate the Neyman-Pearson Receiver Operating...

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

Description

nproc calculates the Neyman-Pearson Receiver Operating Characteristics band for a given sequence of type I error values.

Usage

nproc(x = NULL, y, method = c("logistic", "penlog", "svm", "randomforest",
  "lda", "nb", "nnb", "ada", "tree"), delta = 0.05, split = 1,
  split.ratio = 0.5, n.cores = 1, randSeed = 0, ...)

Arguments

x	n * p observation matrix. n observations, p covariates.
y	n 0/1 observatons.
method	base classification method(s). logistic: Logistic regression. glm function with family = 'binomial' penlog: Penalized logistic regression with LASSO penalty. glmnet in glmnet package svm: Support Vector Machines. svm in e1071 package randomforest: Random Forest. randomForest in randomForest package Linear Discriminant Analysis. lda: lda in MASS package nb: Naive Bayes. naiveBayes in e1071 package nnb: Nonparametric Naive Bayes. naive_bayes in naivebayes package ada: Ada-Boost. ada in ada package
delta	the violation rate of the type I error. Default = 0.05.
split	the number of splits for the class 0 sample. Default = 1. For ensemble version, choose split > 1.
split.ratio	the ratio of splits used for the class 0 sample to train the classifier. Default = 0.5.
n.cores	number of cores used for parallel computing. Default = 1.
randSeed	the random seed used in the algorithm.
...	additional arguments.

Value

An object with S3 class nproc.

typeI.u	sequence of upper bound of type I error.
typeII.l	sequence of lower bound of type II error.
typeII.u	sequence of upper bound of type II error.
auc.l	the auc value of the lower NP-ROC curve.
auc.u	the auc value of the upper NP-ROC curve.
method	the base classification method implemented.
delta	the violation rate.

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

Examples

n = 200
x = matrix(rnorm(n*2),n,2)
c = 1 - 3*x[,1]
y = rbinom(n,1,1/(1+exp(-c)))
#fit = nproc(x, y, method = 'svm')
fit2 = nproc(x, y, method = 'penlog')
##Plot the nproc curve
plot(fit2)

## Not run: 
fit3 = nproc(x, y, method = 'penlog',  n.cores = 2)
#In practice, replace 2 by the number of cores available 'detectCores()'
fit4 = nproc(x, y, method = 'penlog', n.cores = detectCores())

#Confidence nproc curves
fit6 = nproc(x, y, method = 'lda')
plot(fit6)
nproc ensembled version
fit7 = nproc(x, y, method = 'lda', split = 11)
plot(fit7)

## End(Not run)

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