predict.ordASDA: Predict method for ordinal sparse discriminant analysis

In gumeo/accSDA: Accelerated Sparse Discriminant Analysis

Predict method for ordinal sparse discriminant analysis

Description

Predicted values based on fit from the function ordASDA. This function is used to classify new observations based on their explanatory variables/features. There is no need to normalize the data, the data is normalized based on the normalization data from the ordASDA object.

Usage

## S3 method for class 'ordASDA'
predict(object, newdata = NULL, ...)

Arguments

object	Object of class ordASDA. This object is returned from the function ordASDA.
newdata	A matrix of new observations to classify.
...	Arguments passed to predict.lda.

Value

A vector of predictions.

See Also

ordASDA

Examples

    set.seed(123)

    # You can play around with these values to generate some 2D data to test one
    numClasses <- 5
    sigma <- matrix(c(1,-0.2,-0.2,1),2,2)
    mu <- c(0,0)
    numObsPerClass <- 5

    # Generate the data, can access with train$X and train$Y
    train <- accSDA::genDat(numClasses,numObsPerClass,mu,sigma)
    test <- accSDA::genDat(numClasses,numObsPerClass*2,mu,sigma)

    # Visualize it, only using the first variable gives very good separation
    plot(train$X[,1],train$X[,2],col = factor(train$Y),asp=1,main="Training Data")

    # Train the ordinal based model
    res <- accSDA::ordASDA(train$X,train$Y,s=2,h=1, gam=1e-6, lam=1e-3)
    vals <- predict(object = res,newdata = test$X) # Takes a while to run ~ 10 seconds
    sum(vals==test$Y)/length(vals) # Get accuracy on test set
    #plot(test$X[,1],test$X[,2],col = factor(test$Y),asp=1,
    #      main="Test Data with correct labels")
    #plot(test$X[,1],test$X[,2],col = factor(vals),asp=1,
    #     main="Test Data with predictions from ordinal classifier")

gumeo/accSDA documentation built on Nov. 16, 2023, 11:47 p.m.