standardize: Standardization (Z-scoring)

Description

Performs a standardization of data (z-scoring), i.e., centering and scaling, so that the data is expressed in terms of standard deviation (i.e., mean = 0, SD = 1) or Median Absolute Deviance (median = 0, MAD = 1). When applied to a statistical model, this function extracts the dataset, standardizes it, and refits the model with this standardized version of the dataset. The normalize() function can also be used to scale all numeric variables within the 0 - 1 range.

Usage

standardize(x, ...)
# S3 method for data.frame
standardize(
  x,
  robust = FALSE,
  two_sd = FALSE,
  select = NULL,
  exclude = NULL,
  verbose = TRUE,
  force = FALSE,
  append = FALSE,
  suffix = "_z",
  ...
)
# S3 method for default
standardize(
  x,
  robust = FALSE,
  two_sd = FALSE,
  include_response = TRUE,
  verbose = TRUE,
  ...
)

Arguments

A data frame, a vector or a statistical model.

...

Arguments passed to or from other methods.

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 (Gelman, 2008).

select

Character vector of column names. If NULL (the default), all variables will be selected.

exclude

Character vector of column names to be excluded from selection.

verbose

Toggle warnings on or off.

force

Logical, if TRUE, forces standardization of factors as well. Factors are converted to numerical values, with the lowest level being the value 1 (unless the factor has numeric levels, which are converted to the corresponding numeric value).

append

Logical, if TRUE and x is a data frame, standardized variables will be added as additional columns; if FALSE, existing variables are overwritten.

suffix

Character value, will be appended to variable (column) names of x, if x is a data frame and append = TRUE.

include_response

For a model, if TRUE (default), the response value will also be standardized. If FALSE, only the predictors will be standardized. Note that for certain models (logistic regression, count models, ...), the response value will never be standardized, to make re-fitting the model work.

Value

The standardized object (either a standardize data frame or a statistical model fitted on standardized data).

Details

If x is a model object, 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. However, 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, i.e., based on the median and MAD instead of the mean and SD. See standardize_parameters() for other methods of standardizing model coefficients.

Examples

Run this code

# NOT RUN {
# Data frames
summary(standardize(iris))

# Models
model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
coef(standardize(model))
# }