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.

For model standardization, see `standardize.default()`

.

## Usage

```
standardize(x, ...)
standardise(x, ...)
# S3 method for numeric
standardize(
x,
robust = FALSE,
two_sd = FALSE,
weights = NULL,
reference = NULL,
center = NULL,
scale = NULL,
verbose = TRUE,
...
)
# S3 method for factor
standardize(
x,
robust = FALSE,
two_sd = FALSE,
weights = NULL,
force = FALSE,
verbose = TRUE,
...
)
# S3 method for data.frame
standardize(
x,
select = NULL,
exclude = NULL,
robust = FALSE,
two_sd = FALSE,
weights = NULL,
reference = NULL,
center = NULL,
scale = NULL,
remove_na = c("none", "selected", "all"),
force = FALSE,
append = FALSE,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
unstandardize(x, ...)
unstandardise(x, ...)
# S3 method for numeric
unstandardize(
x,
center = NULL,
scale = NULL,
reference = NULL,
robust = FALSE,
two_sd = FALSE,
...
)
# S3 method for data.frame
unstandardize(
x,
center = NULL,
scale = NULL,
reference = NULL,
robust = FALSE,
two_sd = FALSE,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
```

## Arguments

- x
A (grouped) data frame, a vector or a statistical model (for

`unstandardize()`

cannot be a 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, when applied to**the predictors**(not the outcome) (Gelman, 2008).- weights
Can be

`NULL`

(for no weighting), or:For model: if

`TRUE`

(default), a weighted-standardization is carried out.For

`data.frame`

s: a numeric vector of weights, or a character of the name of a column in the`data.frame`

that contains the weights.For numeric vectors: a numeric vector of weights.

- reference
A data frame or variable from which the centrality and deviation will be computed instead of from the input variable. Useful for standardizing a subset or new data according to another data frame.

- center, scale
For

`standardize()`

:

Numeric values, which can be used as alternative to`reference`

to define a reference centrality and deviation. If`scale`

and`center`

are of length 1, they will be recycled to match the length of selected variables for standardization. Else,`center`

and`scale`

must be of same length as the number of selected variables. Values in`center`

and`scale`

will be matched to selected variables in the provided order, unless a named vector is given. In this case, names are matched against the names of the selected variables.For

`unstandardize()`

:`center`

and`scale`

correspond to the center (the mean / median) and the scale (SD / MAD) of the original non-standardized data (for data frames, should be named, or have column order correspond to the numeric column). However, one can also directly provide the original data through`reference`

, from which the center and the scale will be computed (according to`robust`

and`two_sd`

). Alternatively, if the input contains the attributes`center`

and`scale`

(as does the output of`standardize()`

), it will take it from there if the rest of the arguments are absent.

- verbose
Toggle warnings and messages on or off.

- force
Logical, if

`TRUE`

, forces recoding of factors and character vectors as well.- select
Variables that will be included when performing the required tasks. Can be either

a variable specified as a literal variable name (e.g.,

`column_name`

),a string with the variable name (e.g.,

`"column_name"`

), or a character vector of variable names (e.g.,`c("col1", "col2", "col3")`

),a formula with variable names (e.g.,

`~column_1 + column_2`

),a vector of positive integers, giving the positions counting from the left (e.g.

`1`

or`c(1, 3, 5)`

),a vector of negative integers, giving the positions counting from the right (e.g.,

`-1`

or`-1:-3`

),one of the following select-helpers:

`starts_with()`

,`ends_with()`

,`contains()`

, a range using`:`

or`regex("")`

.`starts_with()`

,`ends_with()`

, and`contains()`

accept several patterns, e.g`starts_with("Sep", "Petal")`

.or a function testing for logical conditions, e.g.

`is.numeric()`

(or`is.numeric`

), or any user-defined function that selects the variables for which the function returns`TRUE`

(like:`foo <- function(x) mean(x) > 3`

),ranges specified via literal variable names, select-helpers (except

`regex()`

) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a`-`

, e.g.`-ends_with("")`

,`-is.numeric`

or`-(Sepal.Width:Petal.Length)`

.**Note:**Negation means that matches are*excluded*, and thus, the`exclude`

argument can be used alternatively. For instance,`select=-ends_with("Length")`

(with`-`

) is equivalent to`exclude=ends_with("Length")`

(no`-`

). In case negation should not work as expected, use the`exclude`

argument instead.

If

`NULL`

, selects all columns. Patterns that found no matches are silently ignored, e.g.`find_columns(iris, select = c("Species", "Test"))`

will just return`"Species"`

.- exclude
See

`select`

, however, column names matched by the pattern from`exclude`

will be excluded instead of selected. If`NULL`

(the default), excludes no columns.- remove_na
How should missing values (

`NA`

) be treated: if`"none"`

(default): each column's standardization is done separately, ignoring`NA`

s. Else, rows with`NA`

in the columns selected with`select`

/`exclude`

(`"selected"`

) or in all columns (`"all"`

) are dropped before standardization, and the resulting data frame does not include these cases.- append
Logical or string. If

`TRUE`

, standardized variables get new column names (with the suffix`"_z"`

) and are appended (column bind) to`x`

, thus returning both the original and the standardized variables. If`FALSE`

, original variables in`x`

will be overwritten by their standardized versions. If a character value, standardized variables are appended with new column names (using the defined suffix) to the original data frame.- ignore_case
Logical, if

`TRUE`

and when one of the select-helpers or a regular expression is used in`select`

, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if

`TRUE`

, the search pattern from`select`

will be treated as regular expression. When`regex = TRUE`

, select*must*be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1.`regex = TRUE`

is comparable to using one of the two select-helpers,`select = contains("")`

or`select = regex("")`

, however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

## Value

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

## Note

When `x`

is a vector or a data frame with `remove_na = "none")`

,
missing values are preserved, so the return value has the same length /
number of rows as the original input.

## Selection of variables - the `select`

argument

For most functions that have a `select`

argument (including this function),
the complete input data frame is returned, even when `select`

only selects
a range of variables. That is, the function is only applied to those variables
that have a match in `select`

, while all other variables remain unchanged.
In other words: for this function, `select`

will not omit any non-included
variables, so that the returned data frame will include all variables
from the input data frame.

## See also

See `center()`

for grand-mean centering of variables, and
`makepredictcall.dw_transformer()`

for use in model formulas.

Other transform utilities:
`normalize()`

,
`ranktransform()`

,
`rescale()`

,
`reverse()`

Other standardize:
`standardize.default()`

## Examples

```
d <- iris[1:4, ]
# vectors
standardise(d$Petal.Length)
#> [1] 0.000000 0.000000 -1.224745 1.224745
#> (center: 1.4, scale = 0.082)
#>
# Data frames
# overwrite
standardise(d, select = c("Sepal.Length", "Sepal.Width"))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 1.2402159 1.3887301 1.4 0.2 setosa
#> 2 0.3382407 -0.9258201 1.4 0.2 setosa
#> 3 -0.5637345 0.0000000 1.3 0.2 setosa
#> 4 -1.0147221 -0.4629100 1.5 0.2 setosa
# append
standardise(d, select = c("Sepal.Length", "Sepal.Width"), append = TRUE)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_z
#> 1 5.1 3.5 1.4 0.2 setosa 1.2402159
#> 2 4.9 3.0 1.4 0.2 setosa 0.3382407
#> 3 4.7 3.2 1.3 0.2 setosa -0.5637345
#> 4 4.6 3.1 1.5 0.2 setosa -1.0147221
#> Sepal.Width_z
#> 1 1.3887301
#> 2 -0.9258201
#> 3 0.0000000
#> 4 -0.4629100
# append, suffix
standardise(d, select = c("Sepal.Length", "Sepal.Width"), append = "_std")
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_std
#> 1 5.1 3.5 1.4 0.2 setosa 1.2402159
#> 2 4.9 3.0 1.4 0.2 setosa 0.3382407
#> 3 4.7 3.2 1.3 0.2 setosa -0.5637345
#> 4 4.6 3.1 1.5 0.2 setosa -1.0147221
#> Sepal.Width_std
#> 1 1.3887301
#> 2 -0.9258201
#> 3 0.0000000
#> 4 -0.4629100
# standardizing with reference center and scale
d <- data.frame(
a = c(-2, -1, 0, 1, 2),
b = c(3, 4, 5, 6, 7)
)
# default standardization, based on mean and sd of each variable
standardize(d) # means are 0 and 5, sd ~ 1.581139
#> a b
#> 1 -1.2649111 -1.2649111
#> 2 -0.6324555 -0.6324555
#> 3 0.0000000 0.0000000
#> 4 0.6324555 0.6324555
#> 5 1.2649111 1.2649111
# standardization, based on mean and sd set to the same values
standardize(d, center = c(0, 5), scale = c(1.581, 1.581))
#> a b
#> 1 -1.2650221 -1.2650221
#> 2 -0.6325111 -0.6325111
#> 3 0.0000000 0.0000000
#> 4 0.6325111 0.6325111
#> 5 1.2650221 1.2650221
# standardization, mean and sd for each variable newly defined
standardize(d, center = c(3, 4), scale = c(2, 4))
#> a b
#> 1 -2.5 -0.25
#> 2 -2.0 0.00
#> 3 -1.5 0.25
#> 4 -1.0 0.50
#> 5 -0.5 0.75
# standardization, taking same mean and sd for each variable
standardize(d, center = 1, scale = 3)
#> a b
#> 1 -1.0000000 0.6666667
#> 2 -0.6666667 1.0000000
#> 3 -0.3333333 1.3333333
#> 4 0.0000000 1.6666667
#> 5 0.3333333 2.0000000
```