predict.nlda: Classify Multivariate Observations by 'nlda'

In tonedivad/FIEmspro: Flow Injection Electrospray Mass Spectrometry Processing: \\ data processing, classification modelling and variable selection in metabolite fingerprinting

Description

Classify multivariate observations in conjunction with nlda, and also project data onto the linear discriminants.

Usage

## S3 method for class 'nlda'
predict(object, newdata, dim2use = NULL, ...)

Arguments

object	Object of class nlda.
newdata	A matrix or data frame of cases to be classified.
dim2use	The dimension of rotated data set to be used in prediction.
...	Arguments passed to or from other methods.

Details

This function is a method for the generic function predict() for class nlda. If newdata is omitted, the results of training data in nlda object will be returned.

Value

A list with components:

class	The predicted class (a factor).
x	The projections of test data on discriminant variables.
prob	The posterior probabilities for the predicted classes.
xmeans	The group means obtained from training.
dim2use	The dimension of rotated data set to be used in prediction.

Author(s)

David Enot [e-mail] and Wanchang Lin [e-mail].

See Also

nlda, plot.nlda

Examples

data(abr1)
cl   <- factor(abr1$fact$class)
dat  <- abr1$pos

## divide data as training and test data
idx <- sample(1:nrow(dat), round((2/3)*nrow(dat)), replace=FALSE) 

## constrcuct train and test data 
train.dat  <- dat[idx,]
train.t    <- cl[idx]
test.dat   <- dat[-idx,]        
test.t     <- cl[-idx] 

## apply NLDA
model    <- nlda(train.dat,train.t)
pred.te  <- predict(model, test.dat)

## confusion matrix
table(test.t,pred.te$class)

tonedivad/FIEmspro documentation built on May 31, 2019, 6:20 p.m.