Package ‘MuMIn’

stdize 51
stdize
Standardize data
Description
Usage
stdize standardizes variables by centring and scaling.
stdizeFit modifies a model call or existing model to use standardized variables.
## Default S3 method:
stdize(x, center = TRUE, scale = TRUE, ...)
## S3 method for class logical
stdize(x, binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = FALSE, ...)
## also for two-level factors
## S3 method for class data.frame
stdize(x, binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = TRUE, omit.cols = NULL, source = NULL,
prefix = TRUE, append = FALSE, ...)
## S3 method for class formula
stdize(x, data = NULL, response = FALSE,
binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = TRUE, omit.cols = NULL, prefix = TRUE,
append = FALSE, ...)
stdizeFit(object, data, which = c("formula", "subset", "offset", "weights"),
evaluate = TRUE, quote = NA)
Arguments
x
center, scale
binary
source
a numeric or logical vector, factor, numeric matrix, data.frame or a formula.
either a logical value, or a logical or numeric vector of length equal to the number
of columns of x (see ‘Details’). scale can be also a function to use for
scaling.
specifies how binary variables (logical or two-level factors) are scaled. Default
is to "center" by subtracting the mean assuming levels are equal to 0 and 1;
use "scale" to both centre and scale by SD, "binary" to centre to 0 / 1, "half"
to centre to -0.5 / 0.5, and "omit" to leave binary variables unmodified. This
argument has precedence over center and scale, unless it is set to NA (in which
case binary variables are treated like numeric variables).
a reference data.frame, being a result of previous stdize, from which scale
and center values are taken. Column names are matched. This can be used for
scaling new data using statistics of another data.