sqrt(x + a) Normalization — sqrt

Perform a sqrt (x+a) normalization transformation

sqrt_x(x, a = NULL, standardize = TRUE, ...)

# S3 method for sqrt_x
predict(object, newdata = NULL, inverse = FALSE, ...)

# S3 method for sqrt_x
print(x, ...)

Arguments

x: A vector to normalize with with x
a: The constant to add to x (defaults to max(0, -min(x)))
standardize: If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal
...: additional arguments
object: an object of class 'sqrt_x'
newdata: a vector of data to be (potentially reverse) transformed
inverse: if TRUE, performs reverse transformation

Value

A list of class sqrt_x with elements

x.t: transformed original data
x: original data
mean: mean after transformation but prior to standardization
sd: sd after transformation but prior to standardization
n: number of nonmissing observations
norm_stat: Pearson's P / degrees of freedom
standardize: was the transformation standardized

The predict function returns the numeric value of the transformation performed on new data, and allows for the inverse transformation as well.

Details

sqrt_x performs a simple square-root transformation in the context of bestNormalize, such that it creates a transformation that can be estimated and applied to new data via the predict function. The parameter a is essentially estimated by the training set by default (estimated as the minimum possible), while the base must be specified beforehand.

Examples

x <- rgamma(100, 1, 1)

sqrt_x_obj <- sqrt_x(x)
sqrt_x_obj
#> Standardized sqrt(x + a) Transformation with 100 nonmissing obs.:
#>  Relevant statistics:
#>  - a = 0 
#>  - mean (before standardization) = 0.9442748 
#>  - sd (before standardization) = 0.451784 
p <- predict(sqrt_x_obj)
x2 <- predict(sqrt_x_obj, newdata = p, inverse = TRUE)

all.equal(x2, x)
#> [1] TRUE