normalize {datawizard} | R Documentation |
Normalize numeric variable to 0-1 range
Description
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()
.
Usage
normalize(x, ...)
## S3 method for class 'numeric'
normalize(x, include_bounds = TRUE, verbose = TRUE, ...)
## S3 method for class '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 class 'numeric'
unnormalize(x, verbose = TRUE, ...)
## S3 method for class 'data.frame'
unnormalize(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
## S3 method for class 'grouped_df'
unnormalize(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
Arguments
x |
A numeric vector, (grouped) data frame, or matrix. See 'Details'.
|
... |
Arguments passed to or from other methods.
|
include_bounds |
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 .
|
verbose |
Toggle warnings and messages on or off.
|
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" ), a character
vector of variable names (e.g., c("col1", "col2", "col3") ), or a
character vector of variable names including ranges specified via :
(e.g., c("col1:col3", "col5") ),
for some functions, like data_select() or data_rename() , select can
be a named character vector. In this case, the names are used to rename
the columns in the output data frame. See 'Details' in the related
functions to see where this option applies.
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") . regex() can be used to define regular
expression patterns.
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" .
|
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.
|
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.
|
Details
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.
Value
A normalized object.
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.
References
Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood
Regression with Beta-Distributed Dependent Variables. Psychological Methods,
11(1), 54–71.
See Also
See makepredictcall.dw_transformer()
for use in model formulas.
Other transform utilities:
ranktransform()
,
rescale()
,
reverse()
,
standardize()
Examples
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))
[Package
datawizard version 1.1.0
Index]