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I have a binomial GLM:

all.fit <-  glm(Presence/Total~Season*ToD*Site, family = binomial, weights = Total, data = all.dt)

Call:  glm(formula = Presence/Total ~ Season * ToD * Site, family = binomial, 
    data = all.dt, weights = Total)

Coefficients:
                         (Intercept)                          SeasonSpring  
                            -4.66074                               0.60478  
                        SeasonSummer                          SeasonWinter  
                            -0.82682                               1.26868  
                              ToDDay                               ToDDusk  
                             0.09865                               2.48420  
                            ToDNight                             SiteKawau  
                             2.65294                               3.09495  
                          SiteNoises                        SiteTawharanui  
                             3.48048                               2.94694  
                        SiteTiritiri                   SeasonSpring:ToDDay  
                             3.25976                               0.90540  
                 SeasonSummer:ToDDay                   SeasonWinter:ToDDay  
                             2.16685                              -0.12465  
                SeasonSpring:ToDDusk                  SeasonSummer:ToDDusk  
                            -2.78750                              -1.52114  
                SeasonWinter:ToDDusk                 SeasonSpring:ToDNight  
                            -0.90286                              -1.66162  
               SeasonSummer:ToDNight                 SeasonWinter:ToDNight  
                            -1.85826                              -0.21758  
              SeasonSpring:SiteKawau                SeasonSummer:SiteKawau  
                            -0.67217                               1.24760  
              SeasonWinter:SiteKawau               SeasonSpring:SiteNoises  
                            -1.68237                              -0.79023  
             SeasonSummer:SiteNoises               SeasonWinter:SiteNoises  
                             0.42570                              -0.73055  
         SeasonSpring:SiteTawharanui           SeasonSummer:SiteTawharanui  
                            -0.25773                               1.32456  
         SeasonWinter:SiteTawharanui             SeasonSpring:SiteTiritiri  
                            -0.51055                              -0.51381  
           SeasonSummer:SiteTiritiri             SeasonWinter:SiteTiritiri  
                             0.95804                              -1.29898  
                    ToDDay:SiteKawau                     ToDDusk:SiteKawau  
                             0.70933                              -2.35320  
                  ToDNight:SiteKawau                     ToDDay:SiteNoises  
                            -5.33351                               0.27852  
                  ToDDusk:SiteNoises                   ToDNight:SiteNoises  
                            -2.53814                              -4.04432  
               ToDDay:SiteTawharanui                ToDDusk:SiteTawharanui  
                            -0.16455                              -2.76975  
             ToDNight:SiteTawharanui                   ToDDay:SiteTiritiri  
                            -2.62701                               0.81969  
                ToDDusk:SiteTiritiri                 ToDNight:SiteTiritiri  
                            -2.24361                              -4.14062  
       SeasonSpring:ToDDay:SiteKawau         SeasonSummer:ToDDay:SiteKawau  
                            -0.19831                              -1.61316  
       SeasonWinter:ToDDay:SiteKawau        SeasonSpring:ToDDusk:SiteKawau  
                             0.57193                               2.48229  
      SeasonSummer:ToDDusk:SiteKawau        SeasonWinter:ToDDusk:SiteKawau  
                             1.37786                               0.81702  
     SeasonSpring:ToDNight:SiteKawau       SeasonSummer:ToDNight:SiteKawau  
                             1.41405                               2.06227  
     SeasonWinter:ToDNight:SiteKawau        SeasonSpring:ToDDay:SiteNoises  
                             0.08689                              -0.28007  
      SeasonSummer:ToDDay:SiteNoises        SeasonWinter:ToDDay:SiteNoises  
                            -1.48112                              -0.06044  
     SeasonSpring:ToDDusk:SiteNoises       SeasonSummer:ToDDusk:SiteNoises  
                             1.93847                               0.63232  
     SeasonWinter:ToDDusk:SiteNoises      SeasonSpring:ToDNight:SiteNoises  
                             0.83278                               0.69621  
    SeasonSummer:ToDNight:SiteNoises      SeasonWinter:ToDNight:SiteNoises  
                             0.06911                               0.48690  
  SeasonSpring:ToDDay:SiteTawharanui    SeasonSummer:ToDDay:SiteTawharanui  
                            -0.23150                              -1.96624  
  SeasonWinter:ToDDay:SiteTawharanui   SeasonSpring:ToDDusk:SiteTawharanui  
                             0.43487                               2.04614  
 SeasonSummer:ToDDusk:SiteTawharanui   SeasonWinter:ToDDusk:SiteTawharanui  
                             0.59597                               0.73280  
SeasonSpring:ToDNight:SiteTawharanui  SeasonSummer:ToDNight:SiteTawharanui  
                             1.02467                               1.19957  
SeasonWinter:ToDNight:SiteTawharanui      SeasonSpring:ToDDay:SiteTiritiri  
                            -0.16571                              -0.48960  
    SeasonSummer:ToDDay:SiteTiritiri      SeasonWinter:ToDDay:SiteTiritiri  
                            -1.63190                               0.35354  
   SeasonSpring:ToDDusk:SiteTiritiri     SeasonSummer:ToDDusk:SiteTiritiri  
                             1.77115                               0.79347  
   SeasonWinter:ToDDusk:SiteTiritiri    SeasonSpring:ToDNight:SiteTiritiri  
                             0.48411                               1.51686  
  SeasonSummer:ToDNight:SiteTiritiri    SeasonWinter:ToDNight:SiteTiritiri  
                             1.27621                               0.77385  

Degrees of Freedom: 22998 Total (i.e. Null);  22919 Residual
Null Deviance:      105700 
Residual Deviance: 63170    AIC: 87340

And then I do an anova:

> anova(all.fit,test = "Chisq")
Analysis of Deviance Table

Model: binomial, link: logit

Response: Presence/Total

Terms added sequentially (first to last)


                Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
NULL                            22998     105714              
Season           3   1005.8     22995     104709 < 2.2e-16 ***
ToD              3  16404.9     22992      88304 < 2.2e-16 ***
Site             4   7664.5     22988      80639 < 2.2e-16 ***
Season:ToD       9   2793.5     22979      77846 < 2.2e-16 ***
Season:Site     12   3890.0     22967      73956 < 2.2e-16 ***
ToD:Site        12   9804.5     22955      64151 < 2.2e-16 ***
Season:ToD:Site 36    981.0     22919      63170 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

In this example, all of the interactioms are significant. But, I don't want to report a huge list of significant combinations of factors in my report. Since I guess if the interaction is important, the single effects don't matter? How could I state the significance of my findings more simply?

Could I use the output of anova(all.fit)? If I did so, what would I then be reporting?

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  • $\begingroup$ Your code doesn't make sense. lmer() doesn't take a family= argument. Did you mean to use glmer()? However, you don't indicate any random effects in the formula (eg, something like +(1:id)). Do you have such? Otherwise, it would be better to use glm(). In fact, for the purposes of this question, glm() would be much preferred, since it's simpler & nothing in your Q relates to the greater complexities associated w/ the other functions. $\endgroup$ Jun 17, 2021 at 14:42
  • $\begingroup$ @gung-ReinstateMonica Sorry, I have corrected my code above! I see that I can use anova(all.fit,test="LRT") but I am not sure what this is telling me. Or how I should report it. I am guessing I should include the df and the residual df for each factor, the significance, but should I also report the deviance? $\endgroup$ Jun 17, 2021 at 20:50
  • $\begingroup$ Thanks. It might help if you post example output. Are you just wondering about which p-values to list in the results section, or how to best convey the meaning of it all to an audience? (Something else? What?) Who is the audience? What is the context? Is this for a poster at a scientific conference, a presentation (to whom?), a submission to a scientific journal, something else? More information will help. $\endgroup$ Jun 18, 2021 at 11:05
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    $\begingroup$ @gung-ReinstateMonica Thanks! I have updated the question with example outputs. I want to report the significance of the interaction, and yes, to convey the meaning at a conference and within a report, a presentation to other scientists doing this same research. Aswell as stating the significance of the interaction, I want to be able to look at comparisons between the different levels of each variable, and so for example say, 'summer at one site had a higher proportion of 'presence' than winter'. Does that make sense? $\endgroup$ Jun 20, 2021 at 6:19

2 Answers 2

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You have all categorical variables, and they are all nested within the top level, three-way interaction. Make sure you understand how categorical variables are coded by default and what the standard output means (see: here and here). In R, the output from summary(model) will differ from anova(model). The latter will output a sequential (Type I, see: here) analysis of deviance table (since you have a GLM). In general, p-values will differ between these two (see: here), although in your case the summary output doesn't actually include the test of any of the variables when taken as a whole (again, read up on how variables are coded and presented). In general, drop1(model, test="LRT") will better accord with people's intuitions, but since you only have categorical variables all nested within a three-way interaction, drop1 will only show the p-value for the interaction, which will be the same as listed in the output from anova. As it happens, that's really the only p-value you need. Any other p-values wouldn't really mean what people think they mean.

After that, you have 3x3x4=36 combinations of levels. I would simply make a table with the predicted means, or represent the means with a plot (like a dotplot or a barplot). You'll want to order the cells / dots / bars in such a way to allow you to conveniently point out the comparisons of interest.

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    $\begingroup$ Thank you! Turns out the p-value and deviance output from anova(all.fit,test = "Chisq") is the same as LRT output of drop1(model, test="LRT"). Should I report this as (LRT 36, 250944 = 976.67, p = < 0.005) ? $\endgroup$ Jun 22, 2021 at 3:21
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    $\begingroup$ @LouiseWilson, in R "LRT" & "Chisq" are synonyms in drop1() & anova(). I would report it as, ($\chi^2=981.0$, $df=36$, $p<0.0001$. $\endgroup$ Jun 22, 2021 at 11:25
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If you want to hold on to your current presentation, it is necessary to include at least the third interaction term. If you cannot reduce that, it will not make sense to reduce any of the lower level interaction terms. To test the significance of time, you can remove time completely from from the model, and then test from one model to the other. Similar for season and site.

If you variables time, site, season are all discreate, you can combine them in a new variables and use anova, to show that not all groups can be considered as one. You can then test for reduction to a model without time, without site, and without season, to consider them separately. I am aware that this might not be exactly the answer you are looking for, but I think this is the simplest solution for interpretation.

I hope it answers your question.

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