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Hello,   Could someone help me please and to tell how to get the probability from empirical DENSITY (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “dnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical DENSITY function (which does not correspond to any typical

Dear all, I have some trouble understanding the chisq.test function. Take the following example: set.seed(1) A <- cut(runif(100),c(0.0, 0.35, 0.50, 0.65, 1.00), labels=FALSE) B <- cut(runif(100),c(0.0, 0.25, 0.40, 0.75, 1.00), labels=FALSE) C <- cut(runif(100),c(0.0, 0.25, 0.50, 0.80, 1.00), labels=FALSE) x <- table(A,B) y <- table(A,C) When I calculate the test statistic by hand

All, I realize from the archive that the sort argument in merge has been subject to discussion before, though I couldn't find an explanation for this behavior. I tried to simplify this to (kind of) minimal code from a real example to the following (and I have no doubts that there are smart people around achieving the same with smarter code :-)). I'm running R 2.15.1 64bit under MS Windows

Dear R users, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance with kruskal.test() function. I am comparing three different groups. The preliminary analysis using the kruskal-wallis-test show significance, but I still don''t know the relationship and the significance level