bestLogConstant {bestNormalize} | R Documentation |
Similar to bestNormalize, this selects the best candidate constant for a log transformation 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. This function is currently in development and may not behave as expected.
See details for more information.
bestLogConstant(x, a, standardize = TRUE, ...)
## S3 method for class 'bestLogConstant'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'bestLogConstant'
print(x, ...)
x |
A vector to normalize |
a |
(optional) a list of candidate constants to choose from |
standardize |
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. |
... |
additional arguments. |
object |
an object of class 'bestLogConstant' |
newdata |
a vector of data to be (reverse) transformed |
inverse |
if TRUE, performs reverse transformation |
bestLogConstant
estimates the optimal normalizing constant
for a log transformation. This transformation can be performed on new data, and
inverted, via the predict
function.
A list of class bestLogConstant
with elements
x.t |
transformed original data |
x |
original data |
norm_stats |
Pearson's Pearson's P / degrees of freedom |
method |
out-of-sample or in-sample, number of folds + repeats |
chosen_constant |
the chosen constant transformation (of class 'log_x') |
other_transforms |
the other transformations (of class 'log_x') |
The predict
function returns the numeric value of the transformation
performed on new data, and allows for the inverse transformation as well.