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
>
>
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