std: Standardizes a Design Matrix

In longhaiSK/HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors

Standardizes a Design Matrix

Description

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.

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.

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.

Author(s)

Patrick Breheny (original)
Steven Liu (modification)

See Also

http://pbreheny.github.io/ncvreg/reference/std.html

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 = '')
 

longhaiSK/HTLR documentation built on Oct. 24, 2022, 5:33 p.m.