effectsize (version 0.2.0)

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,
  ...
)
# S3 method for lm
standardize(
  x,
  robust = FALSE,
  two_sd = FALSE,
  include_response = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x

A dataframe, a vector or a statistical model.

...

Arguments passed to or from other methods.

robust

Logical, if TRUE, centering is done by substracting the median from the variables and dividing it by the median absolute deviation (MAD). If FALSE, variables are standardized by substracting 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).

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 dataframe or a statistical model fitted on standardized data).

See Also

normalize standardize_parameters

Examples

# NOT RUN {
# Dataframes
summary(standardize(iris))

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