Knut Krueger <r...@krueger-family.de> wrote > >I think this is more an general question to GLMs. > >The result was better in all prior GLMs when I admitted the non >significant factors, but this is the first time that the result is worse >than before. What could be the reason for that? > >glm(data1~data2+data3+data4+data5+data6,family="gaussian") >The result: > >Coefficients: > Estimate Std. Error t value Pr(>|t|) >(Intercept) 3.3670852 0.8978306 3.750 0.000445 *** >data2 0.0002623 0.0001168 2.245 0.029024 * >data3 -0.9742336 0.5032712 -1.936 0.058337 . >data4 0.0628245 0.1503066 0.418 0.677686 >data5 -0.0438871 0.0740210 -0.593 0.555818 >data6$ -0.0012216 0.0187702 -0.065 0.948357 > > > >if I test only or lm() of course >glm(data1~data2,family="gaussian") > >Coefficients: > Estimate Std. Error t value Pr(>|t|) >(Intercept) 2.473e+00 2.787e-01 8.876 2.86e-12 *** >data2 7.289e-05 7.485e-05 0.974 0.334 >
What do you mean by "better"? Do you mean data2 was significant in one model and not the other? How is this "better"? The two models ask different questions, so, they get different answers. The first, more complex model, asks (re data2) what its relationship to data1 is, controlling for the other variables. The second model asks for uncontrolled. Hope this helps Peter Peter L. Flom, PhD Statistical Consultant Website: www DOT peterflomconsulting DOT com Writing; http://www.associatedcontent.com/user/582880/peter_flom.html Twitter: @peterflom ______________________________________________ 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.
