# stdize

##### Standardization of Data Matrices

Performs standardization (centering and scaling) of a data matrix.

- Keywords
- multivariate, regression

##### Usage

```
stdize(x, center = TRUE, scale = TRUE)
# S3 method for stdized
predict(object, newdata, …)
# S3 method for stdized
makepredictcall(var, call)
```

##### Arguments

- x, newdata
numeric matrices. The data to standardize.

- center
logical value or numeric vector of length equal to the number of coloumns of

`x`

.- scale
logical value or numeric vector of length equal to the number of coloumns of

`x`

.- object
an object inheriting from class

`"stdized"`

, normally the result of a call to`stdize`

.- var
A variable.

- call
The term in the formula, as a call.

- …
other arguments. Currently ignored.

##### Details

`makepredictcall.stdized`

is an internal utility function; it is not
meant for interactive use. See `makepredictcall`

for details.

If `center`

is `TRUE`

, `x`

is centered by subtracting
the coloumn mean from each coloumn. If `center`

is a numeric
vector, it is used in place of the coloumn means.

If `scale`

is `TRUE`

, `x`

is scaled by dividing each
coloumn by its sample standard deviation. If `scale`

is a
numeric vector, it is used in place of the standard deviations.

##### Value

Both `stdize`

and `predict.stdized`

return a scaled and/or
centered matrix, with attributes `"stdized:center"`

and/or
`"stdized:scale"`

the vector used for centering and/or scaling.
The matrix is given class `c("stdized", "matrix")`

.

##### Note

`stdize`

is very similar to `scale`

. The
difference is that when `scale = TRUE`

, `stdize`

divides the
coloumns by their standard deviation, while `scale`

uses the
root-mean-square of the coloumns. If `center`

is `TRUE`

,
this is equivalent, but in general it is not.

##### See Also

##### Examples

```
# NOT RUN {
data(yarn)
## Direct standardization:
Ztrain <- stdize(yarn$NIR[yarn$train,])
Ztest <- predict(Ztrain, yarn$NIR[!yarn$train,])
## Used in formula:
mod <- plsr(density ~ stdize(NIR), ncomp = 6, data = yarn[yarn$train,])
pred <- predict(mod, newdata = yarn[!yarn$train,]) # Automatically standardized
# }
```

*Documentation reproduced from package pls, version 2.7-2, License: GPL-2*