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(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, ... )
A (grouped) data frame, a vector or a statistical model (for
unstandardize()cannot be a model).
Arguments passed to or from other methods.
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).
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).
NULL(for no weighting), or:
For model: if
TRUE(default), a weighted-standardization is carried out.
data.frames: a numeric vector of weights, or a character of the name of a column in the
data.framethat contains the weights.
For numeric vectors: a numeric vector of weights.
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
Numeric values, which can be used as alternative to
referenceto define a reference centrality and deviation. If
centerare of length 1, they will be recycled to match the length of selected variables for standardization. Else,
scalemust be of same length as the number of selected variables. Values in
scalewill 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.
scalecorrespond 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
two_sd). Alternatively, if the input contains the attributes
scale(as does the output of
standardize()), it will take it from there if the rest of the arguments are absent.
Toggle warnings and messages on or off.
TRUE, forces recoding of factors and character vectors as well.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
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.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
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
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
How should missing values (
NA) be treated: if
"none"(default): each column's standardization is done separately, ignoring
NAs. Else, rows with
NAin the columns selected with
"selected") or in all columns (
"all") are dropped before standardization, and the resulting data frame does not include these cases.
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
xwill 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.
TRUEand 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.
TRUE, the search pattern from
selectwill 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 = TRUEis 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.
The standardized object (either a standardize data frame or a statistical model fitted on standardized data).
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.
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.
d <- iris[1:4, ] # vectors standardise(d$Petal.Length) #>  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