Solved – R – Help interpreting GLM and ANOVA output

anovageneralized linear modelrregression

  1. Am I correct in understanding that the effects of flood.level and plant.species are significant predictors of Inv.Simpson (my measure of diversity)?
  2. Is it also correct to say that the effect of different survey years on diversity is not important, as despite significance in the GLM, the F statistic for year is not significant? My data was obtained in the same place over 4 years, so would this lack of significance be reflective of the fact I'm surveying the same population?

Please note that year is categoric data and the others are numerical. I assume the years are being compared to 2015, as this has not been included in the output.

Sample of my code:

    model <- glm(Inv.Simpson ~ Year + Flood.level + Plant.species + Cloud.cover + Temperature + Humidity, data = glm_data)

    summary(model)

    anova(model, test="F")

The Results:

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1.195763   0.190682   6.271 3.17e-09 ***
Year.2016       0.465883   0.154722   3.011 0.003023 ** 
Year.2017       0.558740   0.153930   3.630 0.000381 ***
Year.2018       0.530750   0.164418   3.228 0.001510 ** 
Flood.level     0.207919   0.026946   7.716 1.19e-12 ***
Plant.species   0.095005   0.034442   2.758 0.006480 ** 
Cloud.cover    -0.001114   0.001197  -0.931 0.353234    
Temperature    -0.005615   0.028958  -0.194 0.846488    
Humidity       -0.009183   0.005453  -1.684 0.094102 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.3408132)

    Null deviance: 81.790  on 169  degrees of freedom
Residual deviance: 54.871  on 161  degrees of freedom
  (23 observations deleted due to missingness)
AIC: 310.2

Number of Fisher Scoring iterations: 2

> 
Analysis of Deviance Table

Model: gaussian, link: identity

Response: Inv.Simpson

Terms added sequentially (first to last)


               Df Deviance Resid. Df Resid. Dev       F    Pr(>F)    
NULL                             169     81.790                      
Year            3   0.7427       166     81.047  0.7264  0.537656    
Flood.level     1  22.3051       165     58.742 65.4468 1.367e-13 ***
Plant.species   1   2.3712       164     56.371  6.9574  0.009166 ** 
Cloud.cover     1   0.4186       163     55.952  1.2281  0.269421    
Temperature     1   0.1146       162     55.838  0.3362  0.562867    
Humidity        1   0.9666       161     54.871  2.8362  0.094102 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Best Answer

Regarding 1. Yes, that is correct.

Regarding 2. No. First "important" and "significant" are not the same. Second, in the ANOVA it is adding terms sequentially. Thus, year is not significant by itself. But, from the main output, year clearly is sig. after adding the other variables. From those results, it looks like the three years after 2015 were all higher than 2015, but not much different from each other. This may also be part of the reason why the year effect in the ANOVA was not sig - that is looking at year as a whole, not specific years.

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