Thanks for that. Still I am a bit confused. Please advice me. Now, I have got minimal adequate model keeping all the those significant predictors in the model which is shown below: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.846747 0.987461 5.921 3.2e-09 *** orgmatter -0.886985 0.235347 -3.769 0.000164 *** baresoil -0.935106 0.293838 -3.182 0.001461 ** orgmatter:moistcont 0.009452 0.002759 3.426 0.000612 *** baresoil:moistcont 0.025640 0.009698 2.644 0.008194 ** wood10:grass10 0.007433 0.003187 2.333 0.019667 * grass10:rdnet10 0.004822 0.001563 3.085 0.002036 ** wood10:rdnet10 -0.045485 0.016890 -2.693 0.007081 **
But when I do anova test of this minimal adequate model, only baresoil:moistcont, grass10:rdnet, wood10:rdnet10 were found significant. Df Deviance Resid. Df Resid. Dev Pr(>Chi) NULL 17 36.167 orgmatter 1 2.4260 16 33.741 0.119334 baresoil 1 1.0871 15 32.654 0.297104 orgmatter:moistcont 1 2.5611 14 30.093 0.109526 baresoil:moistcont 1 8.2976 13 21.795 0.003970 ** wood10:grass10 1 0.0184 12 21.777 0.892042 grass10:rdnet10 1 5.4520 11 16.325 0.019546 * wood10:rdnet10 1 8.1565 10 8.168 0.004291 ** So, when I report the outcome of this model, should I show summary significance values or anova significance value (chi-square). Regards Lutfor On Fri, Sep 13, 2013 at 7:42 PM, David Winsemius <dwinsem...@comcast.net>wrote: > > On Sep 13, 2013, at 9:38 AM, Lutfor Rahman wrote: > > Dear forum members, >> >> Please help me understanding significance value when GLM done in r. >> >> After doing minimal adequate model, I have found a number of independent >> values which are significant. But doing their anova significant values >> are >> different. Please find my result following. Which significant values >> should >> I use. >> >> >> glm(formula = richness ~ moistcont + orgmatter + baresoil + grass10 + >> wood10 + rdnet10 + moistcont:orgmatter + moistcont:baresoil + >> grass10:wood10 + grass10:rdnet10 + wood10:rdnet10, family = poisson, >> data = data) >> >> Deviance Residuals: >> Min 1Q Median 3Q Max >> -1.19112 -0.33682 0.09813 0.32808 0.70509 >> >> Coefficients: >> Estimate Std. Error z value Pr(>|z|) >> (Intercept) 11.384447 4.014170 2.836 0.00457 ** >> moistcont -0.095813 0.084995 -1.127 0.25962 >> orgmatter -1.810116 0.613688 -2.950 0.00318 ** >> baresoil -1.636707 0.559129 -2.927 0.00342 ** >> grass10 -0.018979 0.065647 -0.289 0.77250 >> wood10 0.150683 0.128386 1.174 0.24053 >> rdnet10 -0.011448 0.068090 -0.168 0.86648 >> moistcont:orgmatter 0.025698 0.011521 2.231 0.02571 * >> moistcont:baresoil 0.044110 0.015799 2.792 0.00524 ** >> grass10:wood10 0.010740 0.006498 1.653 0.09838 . >> grass10:rdnet10 0.011013 0.004412 2.496 0.01255 * >> wood10:rdnet10 -0.088297 0.027120 -3.256 0.00113 ** >> > > The only p-value I would have expected to be the same would have been the > last one in the avova output: > > Df Deviance Resid. Df Resid. Dev Pr(>Chi) >> ..... >> >> wood10:rdnet10 1 10.7812 6 3.928 0.001025 ** >> > > And that particular p-value is not far off from the 0.00113 value reported > in the model summary. The other p-values are not of the same sort. The > p-values above are basically reporting the "significance" of removing > single predictors or interactions from the full model. The anova reported > below is perfoming sequential addition of terms to a NULL model as well as > doing a different test: LR tests instead of Wald statistics. > > -- > David. > > > --- >> Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> >> (Dispersion parameter for poisson family taken to be 1) >> >> Null deviance: 36.1673 on 17 degrees of freedom >> Residual deviance: 3.9276 on 6 degrees of freedom >> AIC: 97.893 >> >> Number of Fisher Scoring iterations: 4 >> >> anova(data1, test="Chisq") >>> >> Analysis of Deviance Table >> >> Model: poisson, link: log >> >> Response: richness >> >> Terms added sequentially (first to last) >> >> >> Df Deviance Resid. Df Resid. Dev Pr(>Chi) >> NULL 17 36.167 >> moistcont 1 8.6322 16 27.535 0.003303 ** >> orgmatter 1 2.1244 15 25.411 0.144966 >> baresoil 1 0.0029 14 25.408 0.956986 >> grass10 1 1.5251 13 23.883 0.216842 >> wood10 1 3.6952 12 20.187 0.054570 . >> rdnet10 1 0.0001 11 20.187 0.990564 >> moistcont:orgmatter 1 2.0482 10 18.139 0.152381 >> moistcont:baresoil 1 2.8730 9 15.266 0.090076 . >> grass10:wood10 1 0.1431 8 15.123 0.705247 >> grass10:rdnet10 1 0.4141 7 14.709 0.519883 >> wood10:rdnet10 1 10.7812 6 3.928 0.001025 ** >> --- >> Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> >> [[alternative HTML version deleted]] >> >> ______________________________**________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> >> PLEASE do read the posting guide http://www.R-project.org/** >> posting-guide.html <http://www.R-project.org/posting-guide.html> >> and provide commented, minimal, self-contained, reproducible code. >> > > David Winsemius, MD > Alameda, CA, USA > > [[alternative HTML version deleted]]
______________________________________________ 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.