I've read that Box-Cox can help determine the appropriate exponent to use when transforming data, and I need to convert some data into a "normal shape."
From what I can tell
In linear models, the response variables are represented by car::boxCoxVariable(y), and
For a formula or fitted model object, use MASS::boxcox(object). In light of the fact that my data are variables in a dataframe, the only function I could find to utilise is:
dataframe$variable, family="bcPower", car::powerTransform
Is that accurate? Or have I overlooked something?
The second query concerns what to do following the acquisition of
Parameters for the estimated transformation dataframe$variable 0.6394806
Should I just multiply this by the variable? I did this:
Dataframe$variable2 = (dataframe$variable)*aaa; aaa = 0.6394806
the Shapiro-Wilks test for normalcy is next performed, but once more, my data don't appear to be