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.

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!!!

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.**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!!!

Last edited: