e1071 (version 1.7-5)

scale_data_frame: Scaling and Centering of Data Frames

Description

scale_data_frame centers and/or scales the columns of a data frame.

Usage

scale_data_frame(x, center = TRUE, scale = TRUE)

Arguments

x

a data frame or a numeric matrix (or vector). In the latter case, the default method is used.

center

either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.

scale

either a logical value or a numeric-alike vector of length equal to the number of columns of x.

Value

For scale.default, the centered, scaled data frame. Non-numeric columns are ignored. The numeric centering and scalings used (if any) are returned as attributes "scaled:center" and "scaled:scale"

Details

The value of center determines how column centering is performed. If center is a numeric-alike vector with length equal to the number of columns of x, then each column of x has the corresponding value from center subtracted from it. If center is TRUE then centering is done by subtracting the column means (omitting NAs) of x from their corresponding columns, and if center is FALSE, no centering is done.

The value of scale determines how column scaling is performed (after centering). If scale is a numeric-alike vector with length equal to the number of columns of x, then each column of x is divided by the corresponding value from scale. If scale is TRUE then scaling is done by dividing the (centered) columns of x by their standard deviations if center is TRUE, and the root mean square otherwise. If scale is FALSE, no scaling is done.

The root-mean-square for a (possibly centered) column is defined as \(\sqrt{\sum(x^2)/(n-1)}\), where \(x\) is a vector of the non-missing values and \(n\) is the number of non-missing values. In the case center = TRUE, this is the same as the standard deviation, but in general it is not. (To scale by the standard deviations without centering, use scale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE)).)

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

sweep which allows centering (and scaling) with arbitrary statistics.

Examples

# NOT RUN {
require(stats)
data(iris)
summary(scale_data_frame(iris))
# }