# normalization: Normalization Methods In KODAMA: Knowledge Discovery by Accuracy Maximization

 normalization R Documentation

## Normalization Methods

### Description

Collection of Different Normalization Methods.

### Usage

```normalization(Xtrain,Xtest=NULL, method = "pqn",ref=NULL)
```

### Arguments

 `Xtrain` a matrix of data (training data set). `Xtest` a matrix of data (test data set).(by default = NULL). `method` the normalization method to be used. Choices are "`none`", "`pqn`", "`sum`", "`median`", "`sqrt`" (by default = "`pqn`"). A partial string sufficient to uniquely identify the choice is permitted. `ref` Reference sample for Probabilistic Quotient Normalization. (by default = NULL).

### Details

A number of different normalization methods are provided:

• "`none`": no normalization method is applied.

• "`pqn`": the Probabilistic Quotient Normalization is computed as described in Dieterle, et al. (2006).

• "`sum`": samples are normalized to the sum of the absolute value of all variables for a given sample.

• "`median`": samples are normalized to the median value of all variables for a given sample.

• "`sqrt`": samples are normalized to the root of the sum of the squared value of all variables for a given sample.

### Value

The function returns a list with 2 items or 4 items (if a test data set is present):

 `newXtrain` a normalized matrix (training data set). `coeXtrain` a vector of normalization coefficient of the training data set. `newXtest` a normalized matrix (test data set). `coeXtest` a vector of normalization coefficient of the test data set.

### Author(s)

Stefano Cacciatore and Leonardo Tenori

### References

Dieterle F,Ross A, Schlotterbeck G, Senn H.
Probabilistic Quotient Normalization as Robust Method to Account for Diluition of Complex Biological Mixtures. Application in 1H NMR Metabolomics.
Anal Chem 2006;78:4281-90.

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.

`scaling`

### Examples

```data(MetRef)
u=MetRef\$data;
u=u[,-which(colSums(u)==0)]
u=normalization(u)\$newXtrain
u=scaling(u)\$newXtrain
class=as.numeric(as.factor(MetRef\$gender))
cc=pca(u)
plot(cc\$x,pch=21,bg=class,xlab=cc\$txt,ylab=cc\$txt)
```

KODAMA documentation built on April 1, 2022, 5:06 p.m.