# predict.ASDA: Predict method for sparse discriminant analysis In accSDA: Accelerated Sparse Discriminant Analysis

 predict.ASDA R Documentation

## 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

`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
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)
``````

accSDA documentation built on May 29, 2024, 4:12 a.m.