I have a data set where observations come from highly distinct groups. Each group may have a wildly different distribution, so I am trying to find the best distribution using fitdist from fitdistrplus, then use gamlssML from the gamlss package to find the best parameters.
My issue is with transforming the data after this step. For some of the distributions, like the Box-Cox t, I can find the equation for normalizing the data using the BCT coefficients, but for many of these distributions I cannot.
Does gamlss have a function that normalizes the data after fitting? Their documentation only provides the transformations for a small number of distributions https://www.gamlss.com/wp-content/uploads/2018/01/DistributionsForModellingLocationScaleandShape.pdf
Thanks a lot
The normalised data values (for any distribution) are exactly equal to the residuals from a gamlss fit,
m1 <- gamlss()
which can be accessed by
residuals(m1) or
m1$residuals