Removing variables because of high P-values is not a valid procedure. Use of AIC or BIC is just a restatement of P-values. AIC can be quite useful if you have posited a very small number of fully pre-specified models (e.g., 2 or 3) and want to choose between them. Stepwise variable selection without shrinkage is invalid. Frank
pigpigmeow wrote: > > chris, > I'm not using lmer, i just use gam mixed with smoothing function and > linear function > and summary of the model, it shows > Family: gaussian > Link function: log > > Formula: > newNO2 ~ pressure + s(maxtemp, bs = "cr") + s(avetemp, bs = "cr") + > s(mintemp, bs = "cr") + RH + s(solar, bs = "cr") + s(windspeed, > bs = "cr") + s(transport, bs = "cr") > > Parametric coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 2.721513 0.049108 55.419 <2e-16 *** > pressure 0.028988 0.019434 1.492 0.140 > RH 0.005228 0.009763 0.535 0.594 > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Approximate significance of smooth terms: > edf Ref.df F p-value > s(maxtemp) 6.346 7.276 1.223 0.29991 > s(avetemp) 1.000 1.000 0.226 0.63562 > s(mintemp) 1.908 2.396 1.066 0.35871 > s(solar) 3.797 4.490 2.164 0.07359 . > s(windspeed) 5.305 6.341 2.346 0.03648 * > s(transport) 7.234 7.984 2.807 0.00884 ** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > R-sq.(adj) = 0.307 Deviance explained = 49.1% > GCV score = 61.136 Scale est. = 44.49 n = 105 > > In the parametric coefficients part, I see that Pr(>|t|) which mean > the probablity greater than T-value. Is that probablity mean p-value? > In the Approximate significance of smooth terms part, p-value column > shows the probability greater than F-value. > > I have the following question, > 1.if I reject the variable term which has greater the p-value no matter > the variable term is smoothing term or linear term, is it correct to > perform stepwise regression. > 2. In my model > noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian > (link=log),groupD,methods=REML) , is it generalized additive mixed model? > 3. what the different if I use other criteria such as AIC or BIC? > > Anyway, thank all of you! > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/About-stepwise-regression-problem-tp3870217p3882092.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@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.
