Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, fig.height = 5, fig.width = 7)
library(bestNormalize)
## -----------------------------------------------------------------------------
## Define user-function
cuberoot_x <- function(x, a = NULL, standardize = TRUE, ...) {
stopifnot(is.numeric(x))
min_a <- max(0, -(min(x, na.rm = TRUE)))
if(!length(a))
a <- min_a
if(a < min_a) {
warning("Setting a < max(0, -(min(x))) can lead to transformation issues",
"Standardize set to FALSE")
standardize <- FALSE
}
x.t <- (x + a)^(1/3)
mu <- mean(x.t, na.rm = TRUE)
sigma <- sd(x.t, na.rm = TRUE)
if (standardize) x.t <- (x.t - mu) / sigma
# Get in-sample normality statistic results
ptest <- nortest::pearson.test(x.t)
val <- list(
x.t = x.t,
x = x,
mean = mu,
sd = sigma,
a = a,
n = length(x.t) - sum(is.na(x)),
norm_stat = unname(ptest$statistic / ptest$df),
standardize = standardize
)
# Assign class
class(val) <- c('cuberoot_x', class(val))
val
}
## -----------------------------------------------------------------------------
predict.cuberoot_x <- function(object, newdata = NULL, inverse = FALSE, ...) {
# If no data supplied and not inverse
if (is.null(newdata) & !inverse)
newdata <- object$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$sd
}
# Return transformed data
unname(newdata)
}
## -----------------------------------------------------------------------------
print.cuberoot_x <- function(x, ...) {
cat(ifelse(x$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')
}
## -----------------------------------------------------------------------------
# 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))
## -----------------------------------------------------------------------------
all.equal(x^(1/3), b$chosen_transform$x.t)
all.equal(x^(1/3), predict(b))
## -----------------------------------------------------------------------------
bestNormalize(x, norm_stat_fn = function(x) nortest::lillie.test(x)$stat)
## -----------------------------------------------------------------------------
(dont_do_this <- bestNormalize(x, norm_stat_fn = function(x) nortest::lillie.test(x)$p))
## -----------------------------------------------------------------------------
best_transform <- names(which.max(dont_do_this$norm_stats))
(do_this <- dont_do_this$other_transforms[[best_transform]])
## -----------------------------------------------------------------------------
(do_this <- bestNormalize(x, norm_stat_fn = function(x) 1-nortest::lillie.test(x)$p))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.