Which non-parametric test should I use while running GLM?

#1
I'm trying to analyze some experimental data about animal behaviour using R and would need some help or advice regarding which non-parametric test should I use.

The variables I have are:
- Response variable: "Vueltasmin", a numeric one
- Explicatory variable: "Condicion", a factor with 6 levels
- Random effect variable: "Bicho", as the same animal performing some behavioural task was measured more than once.
As I have a random effect variable, I chose a GLM model. Then, when checking the normality and homoscedasticity assumptions, Shapiro-Wilks test showed there was no normality and QQplots revealed there weren´t patterns nor outliers in my data. So the question would be: which non-parametric test would be optimal in this case, knowing that I would like to perform certain a posteriori comparisons (and not all-against-all comparisons)?? This is how the data plot would look like:

data.png
Here is some information that might be useful. I´d like to thank everyone in advance!

DATABASE: is composed of 174 observations (29 individuals that were tested in 6 different situations or tasks, represented by one colour in the bar graph and hence the random effect variable); "Bicho" stands for the individual; "Condicion" states the explicatory variable and "Vueltasmin" is the response variable. "Datos" is the name of my database.


CODE:
Condicion<-as.factor(Condicion)
Vueltasmin<-as.numeric(Vueltasmin)

## My model should be: Vueltasmin = Condicion + 1|Bicho
m1 <- lmer(Vueltasmin ~ Condicion + (1 | Bicho), Datos)

#Checking assumptions BEFORE looking at the stats:
e1<-resid(m1) # Pearson residues
pre1<-predict(m1) #predicted

windows()
par(mfrow = c(1, 2))
plot(pre1, e1, xlab="Predichos", ylab="Residuos de pearson",main="Gráfico de
dispersión de RE vs PRED",cex.main=.8 )
pearson.png


abline(0,0) qqnorm(e1, cex.main=.9) #QQ plot qqline(e1) par(mfrow = c(1, 1)) shapiro.test(e1)
#SHAPIRO WILKS: NO NORMALITY!!!

normality.png
 
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#2
The data are never normal, so don't worry about the Shapiro-Wilk statistic. To a first approximation, your results speak for themselves: DB is greater than everything else; DF is lower than everything else; and we can argue about the others. I would use parametric statistics to compare treatments and do a sensitivity analysis without the outlier in the upper-right corner of the QQ plot to see if results are sensitive to that data point.
 
#3
Hi, thanks for the response!
The shapiro, histogram and QQplot were made with the residuals, not with the dependent variable, so I think it still would matter. Nevertheless the sensitivity analysis is a good idea. Any thoughts on how can I ask R to tell me which datapoints are those? Thanks!