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
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Alameda, CA, USA
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
