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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