What is a Box Cox transformation in R?
A box-cox transformation is a commonly used method for transforming a non-normally distributed dataset into a more normally distributed one.
Why do we use Box Cox transformation?
The Box-Cox transformation transforms our data so that it closely resembles a normal distribution. In many statistical techniques, we assume that the errors are normally distributed. This assumption allows us to construct confidence intervals and conduct hypothesis tests.
How to apply the boxcox transformation to data?
Applying the BoxCox transformation to data, without the need of any underlying model, can be done currently using the package geoR. Specifically, you can use the function boxcoxfit () for finding the best parameter and then predict the transformed variables using the function BCtransform ().
How to use the Box-Cox power transformation in R?
Applying the BoxCox transformation to data, without the need of any underlying model, can be done currently using the package geoR. Specifically, you can use the function boxcoxfit () for finding the best parameter and then predict the transformed variables using the function BCtransform (). Thanks for contributing an answer to Stack Overflow!
When to call the boxcox function in R?
In the case when optimize=TRUE, the function boxcox calls the R function nlminb to minimize the negative value of the objective (i.e., maximize the objective) over the range of possible values of \\ (\\lambda\\) specified in the argument lambda. The starting value for the optimization is always \\ (\\lambda=1\\) (i.e., no transformation).
What is the purpose of the boxcox function?
boxcox is a generic function used to compute the value (s) of an objective for one or more Box-Cox power transformations, or to compute an optimal power transformation based on a specified objective. The function invokes particular methods which depend on the class of the first argument.