# data.scale: Scale a matrix In mht: Multiple Hypothesis Testing for Variable Selection in High-Dimensional Linear Models

## Description

Scale the data so each column has mean 0 and variance 1. This function is used as a pre-processing step to prep the data for analysis in all functions of the `mht` package.

## Usage

 `1` ```data.scale(data,warning) ```

## Arguments

 `data` Input matrix of dimension n * p; each row is an observation vector. The intercept should be included in the first column as (1,...,1). If not, it is added. `warning` Logical value. A warning message is printed if the intercept is added. Default is TRUE.

## Details

Scale the data so each column has mean 0 and variance 1. If we note `x` a column of the output scaled matrix -except the first one which is the intercept, we have `sum(x)=0` and `sum(x^2)/n=1`.

## Value

 `data` Scaled data. `intercept` Logical value. TRUE if the intercept was already included in the input data. `means.data` Vector of means of the input data matrix. `sigma.data` Vector of variances of the input data matrix.

## References

Multiple hypotheses testing for variable selection; F. Rohart 2011

## Examples

 ```1 2 3 4 5 6 7 8``` ```## Not run: x=matrix(rnorm(100*20),100,20) res=data.scale(x) x.scaled=res\$data means.x=res\$means.data sigma.x=res\$sigma.data ## End(Not run) ```

mht documentation built on May 30, 2017, 3:07 a.m.