predict.ordASDA {accSDA}

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")