predict.npc: Predicting the outcome of a set of new observations using the...

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

Description

Predicting the outcome of a set of new observations using the fitted npc object.

Usage

## S3 method for class 'npc'
predict(object, newx = NULL, ...)

Arguments

object	fitted npc object using npc.
newx	a set of new observations.
...	additional arguments.

Value

A list containing the predicted label and score.

pred.label	Predicted label vector.
pred.score	Predicted score vector.

See Also

npc and nproc

Examples

n = 1000
x = matrix(rnorm(n*2),n,2)
c = 1+3*x[,1]
y = rbinom(n,1,1/(1+exp(-c)))
xtest = matrix(rnorm(n*2),n,2)
ctest = 1+3*xtest[,1]
ytest = rbinom(n,1,1/(1+exp(-ctest)))


## Not run: 
##Use logistic classifier and the default type I error control with alpha=0.05
fit = npc(x, y, method = 'logistic')
pred = predict(fit,xtest)
fit.score = predict(fit,x)
accuracy = mean(pred$pred.label==ytest)
cat('Overall Accuracy: ',  accuracy,'\n')
ind0 = which(ytest==0)
ind1 = which(ytest==1)
typeI = mean(pred$pred.label[ind0]!=ytest[ind0]) #type I error on test set
cat('Type I error: ', typeI, '\n')
typeII = mean(pred$pred.label[ind1]!=ytest[ind1]) #type II error on test set
cat('Type II error: ', typeII, '\n')

## End(Not run)

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