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I have a question about Poisson GLM and formula representation:

Considering a data set:

p <- read.csv("https://raw.githubusercontent.com/Leprechault/PEN-533/master/bradysia-greenhouse.csv")
  1. Without considering the interaction:

    m1 <- glm(bradysia ~ area + mes, family="quasipoisson", data=p)
        
    summary(m1)

 
    #(Intercept)  4.36395    0.12925  33.765  < 2e-16 ***
    #areaCV      -0.19696    0.12425  -1.585    0.113    
    #areaMJC     -0.71543    0.08553  -8.364 3.11e-16 ***
    #mes         -0.08872    0.01970  -4.503 7.82e-06 ***

The final formula is: bradysia = exp(4.36395*CS-0.19696*CV-0.71543-0.08872*mes)

  1. Considering the interaction:
    m2 <- glm(bradysia ~ area*mes, family="quasipoisson", data=p)
    summary(m2)
      
    #(Intercept)  4.05682    0.15468  26.227  < 2e-16 ***
    #areaCV       0.15671    0.35219   0.445   0.6565    
    #areaMJC      0.54132    0.31215   1.734   0.0833 .  
    #mes         -0.03943    0.02346  -1.680   0.0933 .  
    #areaCV:mes  -0.05724    0.05579  -1.026   0.3052    
    #areaMJC:mes -0.22609    0.05576  -4.055 5.57e-05 **

The final formula is: bradysia = exp(?????) and any help, please?

Isabel
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    `y ~ x1*x2` in R means the same thing as `y ~ x1 + x2 + x1:x2` or mathematically that Y = a + b1*x1 + b2*x2 + b3*x1*x2. – DanY Sep 03 '20 at 21:48
  • Thanks @DanY but and the problem about not significant coefficients? Because is very difficult to find categorical and quantitative factor mixed in a GLM with all the coefficients with significance. – Isabel Sep 03 '20 at 22:14

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