[R] GLM result output..

David Winsemius dwinsemius at comcast.net
Sun Sep 15 17:30:26 CEST 2013


On Sep 15, 2013, at 2:15 AM, Lutfor Rahman wrote:

> 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).

I don't think you should report anything. You need a consultation.  

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-- 
David.
> 
> Regards
> Lutfor   
> 
> 
> 
> 
> 
> On Fri, Sep 13, 2013 at 7:42 PM, David Winsemius <dwinsemius at 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]]
> 
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> and provide commented, minimal, self-contained, reproducible code.
> 
> David Winsemius, MD
> Alameda, CA, USA
> 
> 

David Winsemius
Alameda, CA, USA



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