Performs a normalization of data, i.e., it scales variables in the range
0 - 1. This is a special case of rescale()
. unnormalize()
is the
counterpart, but only works for variables that have been normalized with
normalize()
.
normalize(x, ...)# S3 method for numeric
normalize(x, include_bounds = TRUE, verbose = TRUE, ...)
# S3 method for data.frame
normalize(
x,
select = NULL,
exclude = NULL,
include_bounds = TRUE,
append = FALSE,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
unnormalize(x, ...)
# S3 method for numeric
unnormalize(x, verbose = TRUE, ...)
# S3 method for data.frame
unnormalize(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
# S3 method for grouped_df
unnormalize(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
A normalized object.
A numeric vector, (grouped) data frame, or matrix. See 'Details'.
Arguments passed to or from other methods.
Numeric or logical. Using this can be useful in case of
beta-regression, where the response variable is not allowed to include
zeros and ones. If TRUE
, the input is normalized to a range that includes
zero and one. If FALSE
, the return value is compressed, using
Smithson and Verkuilen's (2006) formula (x * (n - 1) + 0.5) / n
, to avoid
zeros and ones in the normalized variables. Else, if numeric (e.g., 0.001
),
include_bounds
defines the "distance" to the lower and upper bound, i.e.
the normalized vectors are rescaled to a range from 0 + include_bounds
to
1 - include_bounds
.
Toggle warnings and messages on or off.
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. extract_column_names(iris, select = c("Species", "Test"))
will just return "Species"
.
See select
, however, column names matched by the pattern
from exclude
will be excluded instead of selected. If NULL
(the default),
excludes no columns.
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.
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.
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.
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.
If x
is a matrix, normalization is performed across all values (not
column- or row-wise). For column-wise normalization, convert the matrix to a
data.frame.
If x
is a grouped data frame (grouped_df
), normalization is performed
separately for each group.
Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.
See makepredictcall.dw_transformer()
for use in model formulas.
Other transform utilities:
ranktransform()
,
rescale()
,
reverse()
,
standardize()
normalize(c(0, 1, 5, -5, -2))
normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE)
# use a value defining the bounds
normalize(c(0, 1, 5, -5, -2), include_bounds = .001)
head(normalize(trees))
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