| predict.abcrlda {abcrlda} | R Documentation |
Class Prediction for abcrlda objects
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