library(lme4) #library(car) #overwrites the Levene's test from lawtest library(lawstat) data<-read.csv("ISC_total.csv") head(data) data$TP2<-log(data$P2/(1-data$P2)) data$TP2<-replace(data$TP2,data$TP2==Inf,log(0.99/(1-0.99))) dataL<-data[data$Pop=="L",] dataZ<-data[data$Pop=="Z",] dataM<-data[data$Pop=="M",] dataS<-data[data$Pop=="S",] #Are mean ISC values different t.test(TP2~AoS,data=data) wilcox.test(TP2~AoS,data=data) #Difference in mean or vairance between alloaptry t.test(c(dataL$P2,dataZ$P2)~c(dataL$Pop,dataZ$Pop)) var(dataL$P2) var(dataZ$P2) levene.test(c(dataL$P2,dataZ$P2),c(dataL$Pop,dataZ$Pop)) levene.test(c(dataL$P2,dataZ$P2),c(dataL$Pop,dataZ$Pop),kruskal.test=TRUE) t.test(c(dataM$P2,dataS$P2)~c(dataM$Pop,dataS$Pop)) var(dataM$P2) var(dataS$P2) levene.test(c(dataM$P2,dataS$P2),c(dataM$Pop,dataS$Pop)) levene.test(c(dataM$P2,dataS$P2),c(dataM$Pop,dataS$Pop),kruskal.test=TRUE) #Variance between allopatric and sympatric levene.test(data$P2,data$AoS) levene.test(data$P2,data$AoS,kruskal.test=TRUE) var(data$P2[data$AoS=="A"]) var(data$P2[data$AoS=="S"]) #Symaptry actually has more variance compared to Alloaptric populations #pairwise t.test(dataL$P2,dataM$P2) t.test(dataL$P2,dataS$P2) t.test(dataZ$P2,dataM$P2) t.test(dataZ$P2,dataS$P2) levene.test(c(dataL$P2,dataM$P2),c(dataL$Pop,dataM$Pop),kruskal.test=T) levene.test(c(dataL$P2,dataS$P2),c(dataL$Pop,dataS$Pop),kruskal.test=T) levene.test(c(dataZ$P2,dataM$P2),c(dataZ$Pop,dataM$Pop),kruskal.test=T) levene.test(c(dataZ$P2,dataS$P2),c(dataZ$Pop,dataS$Pop),kruskal.test=T)