[R] Models

[Greg Snow]

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]]
> 
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