predict.ASDA: Predict method for sparse discriminant analysis

In gumeo/accSDA: Accelerated Sparse Discriminant Analysis

Predict method for sparse discriminant analysis

Description

Predicted values based on fit from the function ASDA. This function is used to classify new observations based on their explanatory variables/features.

Usage

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

Arguments

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

Value

A list with components:

class

The classification (a factor)

posterior

posterior probabilities for the classes

x

the scores

Note

The input matrix newdata should be normalized w.r.t. the normalization of the training data

See Also

SDAAP, SDAP and SDAD

Examples

    # Prepare training and test set
    train <- c(1:40,51:90,101:140)
    Xtrain <- iris[train,1:4]
    nX <- normalize(Xtrain)
    Xtrain <- nX$Xc
    Ytrain <- iris[train,5]
    Xtest <- iris[-train,1:4]
    Xtest <- normalizetest(Xtest,nX)
    Ytest <- iris[-train,5]

    # Define parameters for SDAD
    Om <- diag(4)+0.1*matrix(1,4,4) #elNet coef mat
    gam <- 0.01
    lam <- 0.01
    method <- "SDAD"
    q <- 2
    control <- list(PGsteps = 100,
                    PGtol = c(1e-5,1e-5),
                    mu = 1,
                    maxits = 100,
                    tol = 1e-3,
                    quiet = FALSE)

    # Run the algorithm
    res <- ASDA(Xt = Xtrain,
                Yt = Ytrain,
                Om = Om,
                gam = gam ,
                lam = lam,
                q = q,
                method = method,
                control = control)

    # Do the predictions on the test set
    preds <- predict(object = res, newdata = Xtest)

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