# dip.test

From diptest v0.75-3
by Martin Maechler

##### Hartigans' Dip Test for Unimodality

Compute Hartigans' dip statistic $D_n$, and its P-value for the test for unimodality, by interpolating tabulated quantiles of $\sqrt{n} D_n$.

- Keywords
- distribution, htest

##### Usage

`dip.test(x, simulate.p.value = FALSE, B = 2000)`

##### Arguments

- x
- numeric vector; sample to be tested for unimodality.
- simulate.p.value
- a logical indicating whether to compute p-values by Monte Carlo simulation.
- B
- an integer specifying the number of replicates used in the Monte Carlo test.

##### Details

If `simulate.p.value`

is `FALSE`

, the p-value is computed
via linear interpolation (of $\sqrt{n} D_n$) in the
`qDiptab`

table.
Otherwise the p-value is computed from a Monte Carlo simulation of a
uniform distribution (`runif(n)`

) with `B`

replicates.

##### Value

- A list with class
`"htest"`

containing the following components: statistic the dip statistic $D_n$, i.e., `dip(x)`

.p.value the p-value for the test, see details. method character string describing the test, and whether Monte Carlo simulation was used. data.name a character string giving the name(s) of the data.

##### Note

see also the package vignette, which describes the procedure in more details.

##### References

see those in `dip`

.

##### See Also

For goodness-of-fit testing, notably of continuous distributions,
`ks.test`

.

##### Examples

```
## a first non-trivial case
(d.t <- dip.test(c(0,0, 1,1))) # "perfect bi-modal for n=4" --> P-value = 0
stopifnot(d.t$p.value == 0)
data(statfaculty)
plot(density(statfaculty)); rug(statfaculty)
(d.t <- dip.test(statfaculty))
x <- c(rnorm(50), rnorm(50) + 3)
plot(density(x)); rug(x)
## border-line bi-modal ... BUT (most of the times) not significantly:
dip.test(x)
dip.test(x, simulate=TRUE, B=5000)
## really large n -- get a message
dip.test(runif(4e5))
```

*Documentation reproduced from package diptest, version 0.75-3, License: GPL (>= 2)*

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