LIS.pvalue {LIStest} | R Documentation |
This function calculate the pvalue for the two samples L.I.S independence test for continuous random variables.
LIS.pvalue(x, y, alternative = c("two.sided", "less", "greater"))
x |
x a numeric vector of data values (size less than 70) |
y |
y a numeric vector of data values (same size as x ) |
alternative |
alternative description of the alternative hypothesis |
a number between 0 and 1 corresponding to the calculated p-value
Jesus Garcia and Veronica Andrea Gonzalez Lopez
A nonparametric idependence test for small sample size
X<-rexp(50) Y<-runif(50,0,X) res<-LIS.pvalue(X,-Y,alternative="two.sided") res ## The function is currently defined as function (x, y, alternative = c("two.sided", "less", "greater")) { data(MODE.LN,CUM.LN) n <- length(x) m <- length(y) if (n != m) stop("x and y should have the same size") if (n < 1) stop("not enough data") if (n >= 69) stop("sample size must be lesser than 70") if (length(unique(c(x,y))) < n) stop("cannot compute p-values with ties") alternative <- match.arg(alternative) if(alternative=="greater"){ ln<-cliss(x,y) pvalue<-1-CUM.LN[n,ln] } if(alternative=="less"){ ln<-cliss(x,-y) pvalue<-1-CUM.LN[n,ln] } if(alternative=="two.sided"){ ln<-cliss(x,y) if(ln<MODE.LN[n]){pvalue<-CUM.LN[n,ln]/CUM.LN[n,MODE.LN[n]]} if(ln>MODE.LN[n]){pvalue<-(1-CUM.LN[n,ln])/(1-CUM.LN[n,MODE.LN[n]])} if(ln==MODE.LN[n]){pvalue<-1} } res<-pvalue }