assess_transformation {power.transform} | R Documentation |
Assess normality of transformed data
Description
Not all data allows for a reasonable transformation to normality using power transformation. For example, uniformly distributed data or multi-modal data cannot be transformed to normality. This function computes a p-value for an empirical goodness of fit test for central normality. A distribution is centrally normal if the central 80% of the data are approximately normally distributed. The null-hypothesis is that the transformed distribution is centrally normal.
Usage
assess_transformation(x, transformer, verbose = TRUE, ...)
Arguments
x |
A vector with numeric values that should be transformed to normality. |
transformer |
A transformer object created using
|
verbose |
Sets verbosity of the fubction. |
... |
Unused arguments. |
Value
p-value for empirical goodness of fit test.
Examples
x <- exp(stats::rnorm(1000))
transformer <- find_transformation_parameters(
x = x,
method = "box_cox")
assess_transformation(
x = x,
transformer = transformer)
[Package power.transform version 1.0.0 Index]