**0**wrote:

Hello all!!! I'm trying to analyze some experimental data about animal behaviour and would need some help or advice regarding which non-parametric test should I use.

The variables I have are: - Response variable: "Vueltasmin", a continuous one (both positive and negative values) - 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 mixed 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): red vs grey; red vs black; red vs light blue; black vs grey.

**My database has lots of zeros responses in some conditions, I´ve read that for t-students tests lacking of normality due to lots of zeros it´s OK to turn a blind eye on lack of normality (Srivastava, 1958; Sullivan & D'agostino, 1992) ... is there something similar with mixed models?**

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 )
```

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

You'll likely want to post this on cross-validated instead of here. While many of us use mixed-effect models on occasion, I don't know that there are many people here comfortable giving advice on this particular issue.

90kHi @Devon Ryan, thanks! I´ve already done that and I had no luck, nobody answered my question so I´ve been looking for other statistics forums.

0N.B., I've changed your mentions of GLM to mixed model or mixed-effect model. You're not using a GLM.

90kI thought they meant the same, so thanks for the correction!

0