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

Description

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

Usage

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

Arguments

object

fitted npc object using npc.

newx

a set of new observations.

pred.score

a vector of scores for the new observations. Used when method = 'custom'.

...

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

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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)))

##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')