I want to see if there's a significant difference between 3 groups (klasse) and their agressive behaviour (by counting the number of agressive interactions). The situation is not relevant, but it must be taken into account. I compared the agressive interactions between the groups with a general linear model and I used the family poisson for the count data.
This is the data input in Rstudio:
data.frame(agressiekrab=c(8,5,1,10,6,12,4,17,1,1,5,9,11,2,3,0,21,17,4,1,10,4,14,15,22,8,19,0,6,16,4,10), klasse=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3), situatie=c(1,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,1,2,2,2,1,1,1,1,1,2,2,2,2,2))
structure(list(agressiekrab = c(8, 5, 1, 10, 6, 12, 4, 17, 1,
1, 5, 9, 11, 2, 3, 0, 21, 17, 4, 1, 10, 4, 14, 15, 22, 8, 19,
0, 6, 16, 4, 10), klasse = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
), situatie = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3,
3, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2)), class = "data.frame", row.names = c(NA,
-32L))
It's non-parametric data, so not normally distributed This is the code that I used in Rstudio:
agmodel1<-glm(agressiekrab~klasse*situatie,poisson)
summary(agmodel1)
The result is this:
Call:
glm(formula = agressiekrab ~ klasse * situatie, family = poisson)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.688 -1.623 -0.462 1.306 3.718
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.5198 0.3812 3.987 6.68e-05 ***
klasse 0.7223 0.1827 3.953 7.71e-05 ***
situatie 0.0905 0.2112 0.428 0.66836
klasse:situatie -0.3251 0.1169 -2.781 0.00542 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 171.19 on 31 degrees of freedom
Residual deviance: 120.51 on 28 degrees of freedom
AIC: 240.09
Number of Fisher Scoring iterations: 5
Now i would like to use a post-hoc test to see where the difference is between de groups (klasse). I tried the pairwise wilcox test, but it doesn't give me two-by-two comparisons. I don't know how to interpret the outcome. I also tried the TukeyHSD test, but that didn't work either (and I don't think it's right because of the non-parametric data)
pairwise.wilcox.test(agressiekrab,klasse*situatie,P.adj="Bonj")
Pairwise comparisons using Wilcoxon rank sum test
data: agressiekrab and klasse * situatie £
1 2 3 4
2 1 - - -
3 1 1 - -
4 1 1 1 -
6 1 1 1 1
P value adjustment method: holm
The tukey method gives an error:
TukeyHSD(aov(agressiekrab~klasse*situatie))
Error in TukeyHSD.aov(aov(agressiekrab ~ klasse * situatie)) :
no factors in the fitted model
