InitialTransform: Initial transformation of data

Description

Initial transformation of data before the construction of a biplot. (or any other technique)

Usage

InitialTransform(X, sup.rows = NULL, sup.cols = NULL, 
InitTransform = "None", transform = "Standardize columns", 
grouping = NULL)

Value

A list with the following components

X: Transformed data matrix
sup.rows: Transformed supplementary rows
sup.rows: Transformed supplementary columns

Arguments

X: Original Raw Data Matrix
sup.rows: Supplementary or illustrative rows.
sup.cols: Supplementary or illustrative columns.
InitTransform: Pevious transformation (to use. See details)none or log.
transform: Transformation to use. See details.
grouping: factor to stadadize with the within groups variability

Author

Jose Luis Vicente Villardon

Details

Possible Transformations are:

1.- "Raw Data": When no transformation is required.

2.- "Substract the global mean": Eliminate an eefect common to all the observations

3.- "Double centering" : Interaction residuals. When all the elements of the table are comparable. Useful for AMMI models.

4.- "Column centering": Remove the column means.

5.- "Standardize columns": Remove the column means and divide by its standard deviation.

6.- "Row centering": Remove the row means.

7.- "Standardize rows": Divide each row by its standard deviation.

8.- "Divide by the column means and center": The resulting dispersion is the coefficient of variation.

9.- "Normalized residuals from independence" for a contingency table.

The transformation can be provided to the function by using the string beetwen the quotes or just the associated number.

The supplementary rows and columns are not used to calculate the parameters (means, standard deviations, etc). Some of the transformations are not compatible with supplementary data.

References

M. J. Baxter (1995) Standardization and Transformation in Principal Component Analysis, with Applications to Archaeometry. Journal of the Royal Statistical Society. Series C (Applied Statistics). Vol. 44, No. 4 (1995) , pp. 513-527

Kroonenberg, P. M. (1983). Three-mode principal component analysis: Theory and applications (Vol. 2). DSWO press. (Chapter 6)

Examples

Run this code

data(iris)
x=as.matrix(iris[,1:4])
x=InitialTransform(x, transform=4)
x

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