sparseLDA (version 0.1-7)

normalize: Normalize training data

Description

Normalize a vector or matrix to zero mean and unit length columns

Usage

normalize(X)

Arguments

X

a matrix with the training data with observations down the rows and variables in the columns.

Value

Xc

The normalized data.

mx

Mean of columns of X.

vx

Length of columns of X.

Id

Logical vector indicating which variables are included in X. If some of the columns have zero length they are omitted.

Details

The function can e.g. be used for the training data in sda or smda.

References

Clemmensen, L., Hastie, T. and Ersboell, K. (2008) "Sparse discriminant analysis", Technical report, IMM, Technical University of Denmark

See Also

normalizetest, sda, smda

Examples

## Data
X<-matrix(sample(seq(3),12,replace=TRUE),nrow=3)

## Normalize data
Nm<-normalize(X)
print(Nm$Xc)

## See if any variables have been removed
which(!Nm$Id)