Performs a standardization of data (z-scoring) using
`standardize()`

and then re-fits the model to the standardized data.

Standardization is done by completely refitting the model on the standardized
data. Hence, this approach is equal to standardizing the variables *before*
fitting the model and will return a new model object. This method is
particularly recommended for complex models that include interactions or
transformations (e.g., polynomial or spline terms). The `robust`

(default to
`FALSE`

) argument enables a robust standardization of data, based on the
`median`

and the `MAD`

instead of the `mean`

and the `SD`

.

## Usage

```
# Default S3 method
standardize(
x,
robust = FALSE,
two_sd = FALSE,
weights = TRUE,
verbose = TRUE,
include_response = TRUE,
...
)
```

## Arguments

- x
A statistical model.

- robust
Logical, if

`TRUE`

, centering is done by subtracting the median from the variables and dividing it by the median absolute deviation (MAD). If`FALSE`

, variables are standardized by subtracting the mean and dividing it by the standard deviation (SD).- two_sd
If

`TRUE`

, the variables are scaled by two times the deviation (SD or MAD depending on`robust`

). This method can be useful to obtain model coefficients of continuous parameters comparable to coefficients related to binary predictors, when applied to**the predictors**(not the outcome) (Gelman, 2008).- weights
If

`TRUE`

(default), a weighted-standardization is carried out.- verbose
Toggle warnings and messages on or off.

- include_response
If

`TRUE`

(default), the response value will also be standardized. If`FALSE`

, only the predictors will be standardized.Note that for GLMs and models with non-linear link functions, the response value will not be standardized, to make re-fitting the model work.

If the model contains an

`stats::offset()`

, the offset variable(s) will be standardized only if the response is standardized. If`two_sd = TRUE`

, offsets are standardized by one-sd (similar to the response).(For

`mediate`

models, the`include_response`

refers to the outcome in the y model; m model's response will always be standardized when possible).

- ...
Arguments passed to or from other methods.

## Generalized Linear Models

Standardization for generalized linear models (GLM, GLMM, etc) is done only with respect to the predictors (while the outcome remains as-is, unstandardized) - maintaining the interpretability of the coefficients (e.g., in a binomial model: the exponent of the standardized parameter is the OR of a change of 1 SD in the predictor, etc.)

## Dealing with Factors

`standardize(model)`

or `standardize_parameters(model, method = "refit")`

do
*not* standardize categorical predictors (i.e. factors) / their
dummy-variables, which may be a different behaviour compared to other R
packages (such as **lm.beta**) or other software packages (like SPSS). To
mimic such behaviours, either use `standardize_parameters(model, method = "basic")`

to obtain post-hoc standardized parameters, or standardize the data
with `standardize(data, force = TRUE)`

*before* fitting the
model.

## Transformed Variables

When the model's formula contains transformations (e.g. `y ~ exp(X)`

) the
transformation effectively takes place after standardization (e.g.,
`exp(scale(X))`

). Since some transformations are undefined for none positive
values, such as `log()`

and `sqrt()`

, the relevel variables are shifted (post
standardization) by `Z - min(Z) + 1`

or `Z - min(Z)`

(respectively).

## See also

Other standardize:
`standardize()`

## Examples

```
model <- lm(Infant.Mortality ~ Education * Fertility, data = swiss)
coef(standardize(model))
#> (Intercept) Education Fertility Education:Fertility
#> 0.06386069 0.47482848 0.63270919 0.09829777
```