Standardizes a design matrix

Accepts a design matrix and returns a standardized version of that matrix (i.e., each column will have mean 0 and mean sum of squares equal to 1).

std(X, Xnew)

Arguments

X: A matrix (or object that can be coerced to a matrix, such as a data frame or numeric vector).
Xnew: Optional. If supplied, X must be the output of std() and Xnew is to be standardized in the same way. See examples for why this might be useful.

Value

The standardized design matrix, with the following attribues:

center, scale: mean and standard deviation used to scale the columns
nonsingular: A vector indicating which columns of the original design matrix were able to be standardized (constant columns cannot be standardized to have a standard deviation of 1)

Details

This function centers and scales each column of X so that $$\sum_{i=1}^n x_{ij}=0$$ and $$n^{-1} \sum_{i=1}^n x_{ij}^2 = 1$$ for all j. This is usually not necessary to call directly, as ncvreg internally standardizes the design matrix, but inspection of the standardized design matrix can sometimes be useful. This differs from the base R function scale() in two ways:

scale() uses the sample standard deviation sqrt(sum(x^2)/(n-1)), while std() uses the root-mean-square standard deviation sqrt(mean(sum(x^2)) without the $n/(n-1)$ correction
std is faster.

Examples

data(Prostate)
S <- std(Prostate$X)
apply(S, 2, sum)
#>        lcavol       lweight           age          lbph           svi 
#>  4.211909e-15  6.637225e-14  4.140785e-14  1.110223e-16 -2.886580e-15 
#>           lcp       gleason         pgg45 
#>  1.576864e-14 -4.218847e-15  4.982126e-15 
apply(S, 2, function(x) mean(x^2))
#>  lcavol lweight     age    lbph     svi     lcp gleason   pgg45 
#>       1       1       1       1       1       1       1       1 

# Standardizing new observations
X1 <- Prostate$X[1:90,]
X2 <- Prostate$X[91:97,]
S <- std(X1)
head(std(S, X2))
#>      lcavol    lweight        age       lbph        svi        lcp    gleason
#> 91 1.845841  1.1196037  0.5439694 -1.0472200 -0.4472136 -0.8399653 -1.0076612
#> 92 1.197790  0.1447897 -0.4241118  0.8380061  2.2360680 -0.8399653  0.3459136
#> 93 1.467844  0.6016472  0.5439694 -1.0472200  2.2360680  1.2129092  0.3459136
#> 94 2.367589  0.6487805 -2.7751661 -1.0472200  2.2360680  1.8552150  0.3459136
#> 95 1.537935 -0.5017477 -1.6687876 -1.0472200  2.2360680  2.0786918  0.3459136
#> 96 1.515337  0.3661621  0.5439694  0.9828411  2.2360680  1.3921070  0.3459136
#>         pgg45
#> 91 -0.8476607
#> 92 -0.3129215
#> 93  1.2912962
#> 94  0.5783106
#> 95 -0.4911679
#> 96  2.0042819
# Useful if you fit to a standardized X, but then get new obs:
y <- Prostate$y[1:90]
fit <- ncvreg(S, y)
predict(fit, std(S, X2), lambda=0.1)
#>       91       92       93       94       95       96       97 
#> 3.514077 2.938705 3.408371 3.813543 2.900628 3.445538 3.617198 
# Same as
predict(ncvreg(X1, y), X2, lambda=0.1)
#>       91       92       93       94       95       96       97 
#> 3.514077 2.938705 3.408371 3.813543 2.900628 3.445538 3.617198