predict.abcrlda: Class Prediction for abcrlda objects

In abcrlda: Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis

Description

Classifies observations based on a given abcrlda object.

Usage

## S3 method for class 'abcrlda'
predict(object, newx, ...)

Arguments

object	An object of class "abcrlda".
newx	Matrix of new values for x at which predictions are to be made.
...	Argument used by generic function predict(object, x, ...).

Value

Returns factor vector with predictions (i.e., assigned labels) for each observation. Factor levels are inherited from the object variable.

Reference

A. Zollanvari, M. Abdirash, A. Dadlani and B. Abibullaev, "Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification," in IEEE Signal Processing Letters, vol. 26, no. 9, pp. 1300-1304, Sept. 2019. doi: 10.1109/LSP.2019.2918485 URL: https://ieeexplore.ieee.org/document/8720003

See Also

Other functions in the package: abcrlda(), cross_validation(), da_risk_estimator(), grid_search(), risk_calculate()

Examples

data(iris)
train_data <- iris[which(iris[, ncol(iris)] == "virginica" |
                           iris[, ncol(iris)] == "versicolor"), 1:4]
train_label <- factor(iris[which(iris[, ncol(iris)] == "virginica" |
                                   iris[, ncol(iris)] == "versicolor"), 5])
model <- abcrlda(train_data, train_label, gamma = 0.5, cost = 0.75)
a <- predict(model, train_data)
# same params but more explicit
model <- abcrlda(train_data, train_label, gamma = 0.5, cost = c(0.75, 0.25))
b <- predict(model, train_data)
# same class costs ratio
model <- abcrlda(train_data, train_label, gamma = 0.5, cost = c(3, 1))
c <- predict(model, train_data)
# all this model will give the same predictions
all(a == b & a == c & b == c)
#' [1] TRUE

abcrlda documentation built on July 2, 2020, 4:13 a.m.