Performs a suite of normalizing transformations, and selects the best one on the basis of the Pearson P test statistic for normality. The transformation that has the lowest P (calculated on the transformed data) is selected. See details for more information.
bestNormalize(x, standardize = TRUE, allow_orderNorm = TRUE,
allow_lambert_s = FALSE, allow_lambert_h = FALSE,
out_of_sample = TRUE, cluster = NULL, k = 10, r = 5,
loo = FALSE, warn = TRUE, quiet = FALSE)# S3 method for bestNormalize
predict(object, newdata = NULL,
inverse = FALSE, ...)
# S3 method for bestNormalize
print(x, ...)
A vector to normalize
If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal. This will not change the normality statistic.
set to FALSE if orderNorm should not be applied
Set to TRUE if the lambertW of type "s" should be applied (see details)
Set to TRUE if the lambertW of type "h" should be applied (see details)
if FALSE, estimates quickly in-sample performance
name of cluster set using makeCluster
number of folds
number of repeats
should leave-one-out CV be used instead of repeated CV? (see details)
Should bestNormalize warn when a method doesn't work?
Should a progress-bar not be displayed for cross-validation progress?
an object of class 'bestNormalize'
a vector of data to be (reverse) transformed
if TRUE, performs reverse transformation
additional arguments
A list of class bestNormalize with elements
transformed original data
original data
Pearson's Pearson's P / degrees of freedom
out-of-sample or in-sample, number of folds + repeats
the chosen transformation (of appropriate class)
the other transformations (of appropriate class)
Out-of-sample predictions (if loo == TRUE) or normalization stats
bestNormalize estimates the optimal normalizing
transformation. This transformation can be performed on new data, and
inverted, via the predict function.
This function currently estimates the Yeo-Johnson transformation,
the Box Cox transformation (if the data is positive), the log_10(x+a)
transformation, the square-root (x+a) transformation, and the arcsinh
transformation. a is set to max(0, -min(x) + eps) by default. If
allow_orderNorm == TRUE and if out_of_sample == FALSE then the ordered
quantile normalization technique will likely be chosen since it essentially
forces the data to follow a normal distribution. More information on the
orderNorm technique can be found in the package vignette, or using
?orderNorm.
Repeated cross-validation is used by default to estimate the out-of-sample performance
of each transformation if out_of_sample = TRUE. While this can take some
time, users can speed it up by creating a cluster via the parallel
package's makeCluster function, and passing the name of this cluster
to bestNormalize via the cl argument. For best performance, we
recommend the number of clusters to be set to the number of repeats r. Care
should be taken to account for the number of observations per fold; to
small a number and the estimated normality statistic could be inaccurate,
or at least suffer from high variability.
As of version 1.3, users can use leave-one-out cross-validation as well for
each method by setting loo to TRUE. This will take a lot of
time for bigger vectors, but it will have the most accurate estimate of
normalization efficacy. Note that if this method is selected, arguments
k, r are ignored. This method will still work in parallel with the
cl argument.
NOTE: Only the Lambert technique of type = "s" (skew) ensures that the
transformation is consistently 1-1, so it is the only method currently used
in bestNormalize(). Use type = "h" or type = 'hh' at risk of not
having this estimate 1-1 transform. These alternative types are effective
when the data has exceptionally heavy tails, e.g. the Cauchy distribution.
Additionally, as of v. 1.2.0, Lambert of type "s" is not used by default in
bestNormalize() since it uses multiple threads on some Linux systems,
which is not allowed on CRAN checks. Set allow_lambert_s = TRUE in order to
test this transformation as well. Note that the Lambert of type "h" can also
be done by setting allow_lambert_h = TRUE, however this can take
significantly longer to run.
# NOT RUN {
x <- rgamma(100, 1, 1)
# }
# NOT RUN {
# With Repeated CV
BN_obj <- bestNormalize(x)
BN_obj
p <- predict(BN_obj)
x2 <- predict(BN_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
# }
# NOT RUN {
# }
# NOT RUN {
# With leave-one-out CV
BN_obj <- bestNormalize(x, loo = TRUE)
BN_obj
p <- predict(BN_obj)
x2 <- predict(BN_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
# }
# NOT RUN {
# Without CV
BN_obj <- bestNormalize(x, allow_orderNorm = FALSE, out_of_sample = FALSE)
BN_obj
p <- predict(BN_obj)
x2 <- predict(BN_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
# }
Run the code above in your browser using DataLab