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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 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): 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 GLM?

This is how the data plot would look like: enter image description here

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 ) 

STANDARIZED VS. PREDICTED BY THE MODEL PEARSON RESIDUE

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 SHAPIRO TEST AND GRAPHIC ANALYSIS

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  • $\begingroup$ What is the hypothesis that you want to test? $\endgroup$ Commented Mar 27, 2018 at 3:14
  • $\begingroup$ I would like to make a posteriori specific comparisons, to test if the following bars are different or not: red vs grey; red vs black; red vs light blue; black vs grey. Given normality and homoscedasticity I would run a summary function, then a tukey a posteriori comparison test. $\endgroup$
    – ybarnatan
    Commented Mar 27, 2018 at 17:45

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