extracting significance test for individual lm() parameters after using by

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extracting significance test for individual lm() parameters after using by

 I’m trying to test what growth functions best fit individual subjects. I’m wanting compare linear, quadratic, cubic etc.  Here is the example from the cubic curve. b3a<-by(c,id,function(x) lm(w~agec+ageq+agecub,data=x)) I can get quiet a bit of information out of sapply(b3a,summary) but it reports something like this for each person               37 call          Expression terms         Expression residuals     Numeric,62 coefficients  Numeric,16 aliased           Logical,4  4 sigma         67.05895 df            Integer,3 r.squared     0.9822921 adj.r.squared 0.9813762 fstatistic    Numeric,3 cov.unscaled  Numeric,16 I could obviously compute by hand the r squared change and then compute a p value based for what the partial r is for the variable but I’d like to have a simpler solution to it. Can I get the information or is there an package that I should download that will do what I’m trying to do much simpler?         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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Re: extracting significance test for individual lm() parameters after using by

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Re: extracting significance test for individual lm() parameters after using by

 Sorry I was not clearer, but I was asking an R programming question not a theory question. I will try to clarify. If I did this analysis with a dataset involving  just one subject the summary command on the lm object would give me a significance test on each parameter fit. The question in this cSe, is the cubic parameter significant? When I try to do this for each subject separately in a larger dataset using the by command, I get the parameter estimate but can't find a significance test. I apologize again for my poor explanation originally, > On Jan 24, 2015, at 10:04 PM, Jeff Newmiller <[hidden email]> wrote: > > R-square is often a poor indicator of whether a model is appropriate or not. While it is possible that there exists a package that implements your algorithm (which you might find using the sos package), I would recommend that you get some advice from an expert on how to approach this subject, and this list is not a good place for studying statistics theory (read the Posting Guide). You might try stats.stackexchange. com. > --------------------------------------------------------------------------- > Jeff Newmiller                        The     .....       .....  Go Live... > DCN:<[hidden email]>        Basics: ##.#.       ##.#.  Live Go... >                                      Live:   OO#.. Dead: OO#..  Playing > Research Engineer (Solar/Batteries            O.O#.       #.O#.  with > /Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1 > --------------------------------------------------------------------------- > Sent from my phone. Please excuse my brevity. >> . > ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.