Hi: Recently, I want to perform a transformation on my data to make it more normal, meanwhile the order statistics is unchanged. So I decided to use a box-cox transformation. below is the qq-plot of the original data http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem%2B02.jpeg Note that the min of my data is -1099, so I add a fix value 1200 to the original sample.
I choose the "box.cox.powers" function in package 'car'. Here is the result: > box.cox.powers(na.exclude(c888.dl.ma080+1200)) Box-Cox Transformation to Normality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) 0.9526 0.0237 40.2638 -2.0036 L.R. test, power = 0: 2014.192 df = 1 p = 0 L.R. test, power = 1: 3.9807 df = 1 p = 0.046 Then I compared the result with original data, and it really confused me: http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem.jpeg The left is my original data sample, you can see that it is symetric and the mean is close to 0. It just that the spread is large (there are outliers). The right is the transformed data, and the distribution is obviously no normal. Can anyone explain that to me? Thank you in advanced. ----- ------------------------------------------------------------------ Saji Ren from Shanghai China GoldenHeart Investment Group ------------------------------------------------------------------ -- View this message in context: http://n4.nabble.com/Problem-about-Box-Cox-transformation-topic-in-html-form-tp1011015p1011015.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.