comp.soft-sys.sas - The SAS statistics package.
>>> David L Cassell < XXXX@XXXXX.COM > 03/28/07 12:56 AM >>> wrote <<< In my experience, log-transforming to correct for skewness was usually followed by disgruntled grumbling, as the log transform corrected too much or not enough, or ruined some other aspect of the underlying assumptions. I haven't had a real data set which called out for a log transform since the 1980's. >>>> I agree with David that blindly transforming things because 'that's what people do' is silly and likely to be counter-productive. I think a better approach is to figure out whether the transformation makes any substantive sense. If the scale is arbitrary and not well-known, and you are lucky enough that some transformation creates fewer problems than it solves, then it may be sensible to use the BoxCox transformation, even if the transformation is bizarre. OTOH, if the scale is known, or inherently sensible, then transformation is less likely to be the ideal solution, and, if a transformation is necessary, it should be one that preserves the sense of the variable. So......a colleague wanted to log transform a ratio. WAIT, I said. Does it make any substantive sense to change a ratio into a difference? (It did not). OTOH, if called for, an inverse transformation of a ratio can make sense (changing miles per gallon to gallons per mile) As always, I think the substantive question should drive the statistics, not vice versa HTH Peter
Hi all, I was wondering if anyone knows of a way to back transform a factor loading matrix (with say, varimax rotation) to a correlation or covariance matrix. Alternatively, can one input the pattern matrix into any of the SAS Procs to output a corr or cov matrix? I recently read two papers on a survey instrument that I know were incorrectly analyzed. Unfortunately, the authors only published the factor loading matrices. I would like to input these matrices into Proc Factor and Proc Calis to reanalyze their conclusions. Does anyone know of a way to do this? thanks, Cristian
Hi! I have some binomial disease data and have analysed it with probit and logit link functions. I have been able to back transform the logits but not the probits. Has anyone got code to back transform the probits? That would be great if you do. Thanks.
option1 implicate inference on transform world where your dependent variable is not normally distributed but poisson distributed. In case of option2 your dependent variable is continous but its scale is adjusted & it is normally distributed. Option1 follows the assumption of the genralized linear model whereas option2 follows the assumption of general linear model. On 2/27/09, nuria < XXXX@XXXXX.COM > wrote: > What is the difference between: > > option 1 > proc genmod; > class treat; > model count = treat / dist=poisson link=log; > run; > > option2 > data dataset; > set dataset; > logcount= log (count); > > proc mixed; > class treat; > model logcount = treat; > run; > > Thanks! >