Dear all, I am a newcomer to R. I intend to using R to do stepwise regression and PLS with a data set (a 55x20 matrix, with one dependent and 19 independent variable). Based on the same data set, I have done the same work using SPSS and SAS. However, there is much difference between the results obtained by R and SPSS or SAS.
In the case of stepwise, SPSS gave out a model with 4 independent variable, but with step(), R gave out a model with 10 and much higher R2. Furthermore, regsubsets() also indicate the 10 variable is one of the best regression subset. How to explain this difference? And in the case of my data set, how many variables that enter the model would be reasonable? In the case of PLS, the results of mvr function of pls.pcr package is also different with that of SAS. Although the number of optimum latent variables is same, the difference between R2 is much large. Why? Any comment and suggestion is very appreciated. Thanks in advance! Best wishes, Jinsong Zhao ===== (Mr.) Jinsong Zhao Ph.D. Candidate School of the Environment Nanjing University 22 Hankou Road, Nanjing 210093 P.R. China E-mail: [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html