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Arguments passed on to find_transformation_parameters
method One of the following methods for power transformation:
-
box_cox : Transformation using the Box-Cox transformation (Box and Cox,
1964). The Box-Cox transformation requires that all data are strictly
positive. Features that contain zero or negative values cannot be
transformed using this transformation. In their work, Box and Cox define a
shifted variant. We use this variant to shift values to a strictly positive
range, when negative values are present. The Box-Cox transformation relies
on a single parameter lambda, which is estimated through maximisation of
the log-likelihood function corresponding to a normal distribution.
-
yeo_johnson :Transformation using the Yeo-Johnson
transformation (Yeo and Johnson, 2000). Unlike the Box-Cox transformation,
the Yeo-Johnson transformation allows for negative and positive values.
Like the Box-Cox transformation, this transformation relies on a single
parameter lambda, which is estimated through maximisation of the
log-likelihood function corresponding to a normal distribution.
-
none : A fall-back method that will not transform values.
robust Flag for using a robust version of Box-Cox or Yeo-Johnson
transformation, as defined by Raymaekers and Rousseeuw (2021). This version
is less sensitive in the presence outliers.
invariant Flag for using a version of Box-Cox or Yeo-Johnson
transformation that simultaneously optimises location and scale in addition
to the lambda parameter.
lambda Single lambda value, or range of lambda values that should be
considered. Default: c(4.0, 6.0). Can be NULL to force optimisation
without a constraint in lambda values.
empirical_gof_normality_p_value Significance value for the empirical
goodness-of-fit test for central normality. The p-value is computed through
the assess_transformation function. By setting this parameter to a
numeric value other than NULL , the transformation will be rejected when
the p-value of the test is below the significance value.
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