# qDiptab

0th

Percentile

##### Table of Quantiles from a Large Simulation for Hartigan's Dip Test

Whereas Hartigan(1985) published a table of empirical percentage points of the dip statistic (see dip) based on N=9999 samples of size $n$ from $U[0,1]$, our table of empirical quantiles is currently based on N=1'000'001 samples for each $n$.

Keywords
datasets
##### Note

Taking N=1'000'001 ensures that all the quantile(X, p) used here are exactly order statistics sort(X)[k].

A numeric matrix where each row corresponds to sample size $n$, and each column to a probability (percentage) in $[0,1]$. The dimnames are named n and Pr and coercable to these values, see the examples. attr(qDiptab, "N_1") is $N - 1$, such that with k <- as.numeric(dimnames(qDiptab)$Pr) * attr(qDiptab, "N_1"), e.g., qDiptab[n == 15,] contains exactly the order statistics$D_{[k]}$(from the$N+1$simulated values of dip(U), where U <- runif(15). ##### See Also dip, also for the references; dip.test() which performs the hypothesis test, using qDtiptab (and its null hypothesis of a uniform distribution). ##### Aliases • qDiptab ##### Examples data(qDiptab) str(qDiptab) ## the sample sizes n' : dnqd <- dimnames(qDiptab) (nn <- as.integer(dnqd$n))
## the probabilities:
P.p <- as.numeric(print(dnqd \$ Pr))

## This is as "Table 1" in Hartigan & Hartigan (1985) -- but more accurate
ps <- c(1,5,10,50,90,95,99, 99.5, 99.9)/100
tab1 <- qDiptab[nn <= 200,  as.character(ps)]
round(tab1, 4)
`
Documentation reproduced from package diptest, version 0.75-7, License: GPL (>= 2)

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