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Models
2 messages · Val, Greg Snow
6 days later
If t1-t5 are all correlated with the outcome and with each other, than which are significant will depend on variations in the data (it is possible to have a set of values t1-t5 that predict the outcome well, but which all have nonsignificant p-values when taking the others into account). Allowing gam to fit a non-linear function could easily lead to a different set being "significant". My guess is that the question that you are interested in and the question that is answered by looking at the p-values are not as similar as you had hoped. If you tell us what question you are trying to answer is, then we may be more helpful.
Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111 > -----Original Message----- > From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- > project.org] On Behalf Of Val > Sent: Monday, November 09, 2009 4:08 PM > To: r-help at r-project.org > Subject: [R] Models > > Hi all, > I hope that there might be some statistician out there to help me for > a > possible explanation for the following simple question. > > Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model > > library(gam) > Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive > model > In the first model t1, t2 and t3 found to be significant,. > However, in the second model (using gam package) t1, t4 and t5 are > significant. > > I was hopping to expect nearly similar results from both models but I > found > the opposite results. > > Is there any possible explanation for that? > > Thanks > Val > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at 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.