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.
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!!!