# Customization within bestNormalize

#### Ryan A Peterson

#### 2025-11-29

# Custom functions with bestNormalize

This vignette will go over the steps required to implement a custom user-defined function within the `bestNormalize` framework.

There are 3 steps.

1. Create transformation function

2. Create predict method for transformation function (that can be applied to new data)

3. Pass through new function and predict method to bestNormalize

## Example: cube-root

## S3 methods

Here, we start by defining a new function that we’ll call `cuberoot_x`, which will take an argument `a` (as does the `sqrt_x` function) which will try to add a constant if it sees any negative numbers in `x`. It will also take the argument `standardize` which will center and scale the transformed data so that it’s centered at 0 with SD = 1.

    ## Define user-function
    cuberoot_x <- function(x, a = NULL, standardize = TRUE, ...) {
      stopifnot(is.numeric(x))

      min_a <- max(0, -(min(x, na.rm = TRUE)))
      if(!length(a))
        a <- min_a
      if(a < min_a) {
        warning("Setting a <  max(0, -(min(x))) can lead to transformation issues",
                "Standardize set to FALSE")
        standardize <- FALSE
      }

      x.t <- (x + a)^(1/3)
      mu <- mean(x.t, na.rm = TRUE)
      sigma <- sd(x.t, na.rm = TRUE)
      if (standardize) x.t <- (x.t - mu) / sigma

      # Get in-sample normality statistic results
      ptest <- nortest::pearson.test(x.t)

      val <- list(
        x.t = x.t,
        x = x,
        mean = mu,
        sd = sigma,
        a = a,
        n = length(x.t) - sum(is.na(x)),
        norm_stat = unname(ptest$statistic / ptest$df),
        standardize = standardize
      )

      # Assign class
      class(val) <- c('cuberoot_x', class(val))
      val
    }

Note that we assigned a class to the object this returns of the same name; this is necessary for successful implementation within `bestNormalize`. We’ll also need an associated `predict` method that is used to apply the transformation to newly observed data. `

    predict.cuberoot_x <- function(object, newdata = NULL, inverse = FALSE, ...) {

      # If no data supplied and not inverse
      if (is.null(newdata) & !inverse)
        newdata <- object$x

      # If no data supplied and inverse
      if (is.null(newdata) & inverse)
        newdata <- object$x.t

      # Actually performing transformations

      # Perform inverse transformation as estimated
      if (inverse) {

        # Reverse-standardize
        if (object$standardize)
          newdata <- newdata * object$sd + object$mean

        # Reverse-cube-root (cube)
        newdata <-  newdata^3 - object$a

        # Otherwise, perform transformation as estimated
      } else if (!inverse) {
        # Take cube root
        newdata <- (newdata + object$a)^(1/3)

        # Standardize to mean 0, sd 1
        if (object$standardize)
          newdata <- (newdata - object$mean) / object$sd
      }

      # Return transformed data
      unname(newdata)
    }

## Optional: print method

This will be printed when bestNormalize selects your custom method or when you print an object returned by your new custom function.

    print.cuberoot_x <- function(x, ...) {
      cat(ifelse(x$standardize, "Standardized", "Non-Standardized"),
          'cuberoot(x + a) Transformation with', x$n, 'nonmissing obs.:\n',
          'Relevant statistics:\n',
          '- a =', x$a, '\n',
          '- mean (before standardization) =', x$mean, '\n',
          '- sd (before standardization) =', x$sd, '\n')
    }

Note: if you can find a similar transformation in the source code, it’s easy to model your code after it. For instance, for `cuberoot_x` and `predict.cuberoot_x`, I used `sqrt_x.R` as a template file.

## Implementing with bestNormalize

    # Store custom functions into list
    custom_transform <- list(
      cuberoot_x = cuberoot_x,
      predict.cuberoot_x = predict.cuberoot_x,
      print.cuberoot_x = print.cuberoot_x
    )

    set.seed(123129)
    x <- rgamma(100, 1, 1)
    (b <- bestNormalize(x = x, new_transforms = custom_transform, standardize = FALSE))

    ## Best Normalizing transformation with 100 Observations
    ##  Estimated Normality Statistics (Pearson P / df, lower => more normal):
    ##  - arcsinh(x): 1.2347
    ##  - Box-Cox: 1.0267
    ##  - cuberoot_x: 0.9787
    ##  - Double Reversed Log_b(x+a): 2.4507
    ##  - Exp(x): 4.7947
    ##  - Log_b(x+a): 1.3547
    ##  - No transform: 2.0027
    ##  - orderNorm (ORQ): 1.1627
    ##  - sqrt(x + a): 1.0907
    ##  - Yeo-Johnson: 1.0987
    ## Estimation method: Out-of-sample via CV with 10 folds and 5 repeats
    ##
    ## Based off these, bestNormalize chose:
    ## Non-Standardized cuberoot(x + a) Transformation with 100 nonmissing obs.:
    ##  Relevant statistics:
    ##  - a = 0
    ##  - mean (before standardization) = 0.9588261
    ##  - sd (before standardization) = 0.3298665

Evidently, the cube-rooting was the best normalizing transformation!

## Sanity check

Is this code actually performing the cube-rooting?

    all.equal(x^(1/3), b$chosen_transform$x.t)

    ## [1] TRUE

    all.equal(x^(1/3), predict(b))

    ## [1] TRUE

It does indeed.

# Using custom normalization statistics

The bestNormalize package can estimate any univariate statistic using its CV framework. A user-defined function can be passed in through the `norm_stat_fn` argument, and this function will then be applied in lieu of the Pearson test statistic divided by its degree of freedom.

The user-defined function must take an argument `x`, which indicates the data on which a user wants to evaluate the statistic.

Here is an example using Lilliefors (Kolmogorov-Smirnov) normality test statistic:

    bestNormalize(x, norm_stat_fn = function(x) nortest::lillie.test(x)$stat)

    ## Best Normalizing transformation with 100 Observations
    ##  Estimated Normality Statistics (using custom normalization statistic)
    ##  - arcsinh(x): 0.1958
    ##  - Box-Cox: 0.1785
    ##  - Center+scale: 0.2219
    ##  - Double Reversed Log_b(x+a): 0.2624
    ##  - Exp(x): 0.3299
    ##  - Log_b(x+a): 0.1959
    ##  - orderNorm (ORQ): 0.186
    ##  - sqrt(x + a): 0.1829
    ##  - Yeo-Johnson: 0.1872
    ## Estimation method: Out-of-sample via CV with 10 folds and 5 repeats
    ##
    ## Based off these, bestNormalize chose:
    ## Standardized Box Cox Transformation with 100 nonmissing obs.:
    ##  Estimated statistics:
    ##  - lambda = 0.3281193
    ##  - mean (before standardization) = -0.1263882
    ##  - sd (before standardization) = 0.9913552

Here is an example using Lilliefors (Kolmogorov-Smirnov) normality test’s p-value:

    (dont_do_this <- bestNormalize(x, norm_stat_fn = function(x) nortest::lillie.test(x)$p))

    ## Best Normalizing transformation with 100 Observations
    ##  Estimated Normality Statistics (using custom normalization statistic)
    ##  - arcsinh(x): 0.4327
    ##  - Box-Cox: 0.4831
    ##  - Center+scale: 0.2958
    ##  - Double Reversed Log_b(x+a): 0.2028
    ##  - Exp(x): 0.0675
    ##  - Log_b(x+a): 0.3589
    ##  - orderNorm (ORQ): 0.4492
    ##  - sqrt(x + a): 0.4899
    ##  - Yeo-Johnson: 0.4531
    ## Estimation method: Out-of-sample via CV with 10 folds and 5 repeats
    ##
    ## Based off these, bestNormalize chose:
    ## Standardized exp(x) Transformation with 100 nonmissing obs.:
    ##  Relevant statistics:
    ##  - mean (before standardization) = 6.885396
    ##  - sd (before standardization) = 13.66084

Note: `bestNormalize` will attempt to minimize this statistic by default, which is definitely not what you want to do when calculating the p-value. This is seen in the example above, as the WORST normalization transformation is chosen.

In this case, a user is advised to either manually select the best one:

    best_transform <- names(which.max(dont_do_this$norm_stats))
    (do_this <- dont_do_this$other_transforms[[best_transform]])

    ## Standardized sqrt(x + a) Transformation with 100 nonmissing obs.:
    ##  Relevant statistics:
    ##  - a = 0
    ##  - mean (before standardization) = 0.9811849
    ##  - sd (before standardization) = 0.4779252

Or, the user can reverse their defined statistic (in this case by subtracting it from 1):

    (do_this <- bestNormalize(x, norm_stat_fn = function(x) 1-nortest::lillie.test(x)$p))

    ## Best Normalizing transformation with 100 Observations
    ##  Estimated Normality Statistics (using custom normalization statistic)
    ##  - arcsinh(x): 0.5166
    ##  - Box-Cox: 0.4191
    ##  - Center+scale: 0.6521
    ##  - Double Reversed Log_b(x+a): 0.8005
    ##  - Exp(x): 0.9601
    ##  - Log_b(x+a): 0.5338
    ##  - orderNorm (ORQ): 0.4646
    ##  - sqrt(x + a): 0.4475
    ##  - Yeo-Johnson: 0.4773
    ## Estimation method: Out-of-sample via CV with 10 folds and 5 repeats
    ##
    ## Based off these, bestNormalize chose:
    ## Standardized Box Cox Transformation with 100 nonmissing obs.:
    ##  Estimated statistics:
    ##  - lambda = 0.3281193
    ##  - mean (before standardization) = -0.1263882
    ##  - sd (before standardization) = 0.9913552