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

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:

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.


## 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

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 )

abline(0,0) qqnorm(e1, cex.main=.9) #QQ plot qqline(e1) par(mfrow = c(1, 1)) shapiro.test(e1)

Last edited:


Active Member
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.
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!