search for: kruskal_test

Hi, Is anyone aware of a rank-based, non-paired test such as the Krustal-Wallis, that can accommodate weights? Alternatively, would it make sense to simulate a dataset by duplicating observations in proportion to their weight, and then using the Krustal-Wallis test? thanks! Dylan

...68), rep("II", 89), rep("III", 12)))) ###box plot to visualize data boxplot(breeding~habitat,data=mydata,main="Boxplot of breeding strategies",ylab="breeding strategy",col="gold",lty=1) ### Kruskal-Wallis test kruskal_test(breeding ~ habitat, data = mydata, distribution = approximate(B = 9999)) ### Nemenyi-Damico-Wolfe-Dunn test (joint ranking) NDWD <- oneway_test(breeding ~ habitat, data = mydata, ytrafo = function(data) trafo(data, numeric_trafo = rank), xtrafo = function(data) trafo(data, facto...

Hello! I need to use Kruskal-Wallis test and post-hoc test (Dunn's test) for my data. But when I searched around, I only found this function: kruskal.test. But nothing for Dunn's test. So I started to write one myself. But I do not know how to count ties in the data frame. I can use for loops but it seems long and unnecessary since the rank function actually knows the ties. So

Hi folks, sorry if this has been answered before, I searched long and hard before deciding to make a thread. I'm trying to include multiple variables in a non-parametric analysis (hah!). So far what I've managed to figure out is that the NPMC package from CRAN MIGHT be able to do what I need, but I can't get it to. First I created a dataset as NPMC calls for. > Ind=Individual