Hi Jade,

You should look into the hier.part package.  I know it works for glm but
you might try it for glm.nb.

Best,
Tim


On Fri, Nov 30, 2012 at 6:40 AM, Martin Weiser <weiser2@natur.cuni.cz>wrote:

> Jade Maggs píše v Čt 01. 11. 2012 v 16:02 +0200:
> > Dear list,
> >
> > I have run a generalized linear model with negative binomial
> > distribution (log-link) on fish abundance data.
> >
> > log(abundTot) ~ Bo + B1(topog) + B2(activity) + e
> >
> > *My output is as follows**:*
> >
> > summary(glmNB1)
> > Call: glm.nb(formula = abundTot ~ activity + topog, init.theta =
> > 5.431057349,
> >      link = log)
> > Deviance Residuals:
> >      Min       1Q   Median       3Q      Max
> > -3.1348  -0.7846  -0.2781   0.3097   5.3866
> > Coefficients:
> >                  Estimate Std. Error z value Pr(>|z|)
> > (Intercept)      2.65215    0.07810  33.958  < 2e-16 ***
> > activity[T.hd]  -0.04447    0.08299  -0.536   0.5921
> > activity[T.hfd] -0.44926    0.08884  -5.057 4.27e-07 ***
> > activity[T.nil]  0.07510    0.05808   1.293   0.1960
> > topog            0.03084    0.01546   1.995   0.0461 *
> > ---
> > Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> >
> > (Dispersion parameter for Negative Binomial(5.4311) family taken to be 1)
> >
> >      Null deviance: 473.32  on 428  degrees of freedom Residual
> > deviance: 428.00  on 424  degrees of freedom
> > AIC: 2916.3
> >
> > Number of Fisher Scoring iterations: 1
> >
> >
> >                Theta:  5.431
> >            Std. Err.:  0.485
> >
> > 2 x log-likelihood:  -2904.260
> >
> > *and*
> > anova(glmNB1)
> > Analysis of Deviance Table
> >
> > Model: Negative Binomial(5.4311), link: log
> >
> > Response: abundTot
> >
> > Terms added sequentially (first to last)
> >
> >
> >           Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
> > NULL                       428     473.32
> > activity  3   41.272       425     432.05 5.726e-09 ***
> > topog     1    4.053       424     428.00    0.0441 *
> > ---
> > Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> >
> > Please can someone explain how to calculate the percentage deviance
> > explained by each factor separately (i.e. topog and activity).
> >
> > Kind regards,
> >
>
> Dear Jade,
> as far as I know (and feel free to correct me, anybody), traditional
> goodness of fit measure - R squared (or ratio of sum of squares) is not
> strictly defined for models other than linear. You should look for some
> alternative, eg. Nagelkerke's R-squared, but be warned that these
> alternatives sometimes lack some usual properties of R2 like aditivity -
> sum of Your "explained variances" for individual factors would not be
> the same as the "explained variance" of the full model.
> I hope this helps.
> Martin Weiser
>
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