Standardizes a Design Matrix

This function accepts a design matrix and returns a standardized version of that matrix, the statistics of each column such as median and sd are also provided.

Usage

std(X, tol = 1e-06)

Arguments

X: Design matrix, of dimension nobs by nvars; each row is an observation vector; can also be an object that can be coerced to a matrix, e.g. a data.frame.
tol: The tolerance value; a column of X is considered as singular if the sd of its entries (observations) is less than tol. Singular columns will be dropped by the end.

Value

The standardized design matrix with the following attributes:

nonsingular: Indices of non-singular columns.
center: Median of each non-singular column which is used for standardization.
scale: Standard deviation of each non-singular column which is used for standardization.

Details

For each column of X, the standardization is done by first subtracting its median, then dividing by its sample standard deviation, while the original version in ncvreg uses mean and population standard deviation. Its speed is slower than ncvreg because of the complexity of median finding, but still substantially faster than scale() provided by R base.

Author

Patrick Breheny (original)
Steven Liu (modification)

Examples

set.seed(123)
mat <- matrix(rnorm(n = 80 * 90, mean = 100, sd = 50), 80, 90)
mat %>% as.numeric() %>% ggplot2::qplot(bins = 30, xlab = '')

mat %>% std() %>% as.numeric() %>% ggplot2::qplot(bins = 30, xlab = '')