quantileTestPValue {EnvStats} | R Documentation |

## Compute p-Value for the Quantile Test

### Description

Compute the p-value associated with a specified combination of
`m`

, `n`

, `r`

, and `k`

for the
quantile test (useful for determining `r`

and
`k`

for a given significance level `\alpha`

).

### Usage

```
quantileTestPValue(m, n, r, k, exact.p = TRUE)
```

### Arguments

`m` |
numeric vector of integers indicating the number of observations from the
“treatment” group.
Missing ( |

`n` |
numeric vector of integers indicating the number of observations from the
“reference” group.
Missing ( |

`r` |
numeric vector of integers indicating the ranks of the observations to use as the
lower cut off for the quantile test. All values of |

`k` |
numeric vector of integers indicating the number of observations from the
“treatment” group contained in the |

`exact.p` |
logical scalar indicating whether to compute the p-value based on the exact
distribution of the test statistic ( |

### Details

If the arguments `m`

, `n`

, `r`

, and `k`

are not all the same
length, they are replicated to be the same length as the length of the longest
argument.

For details on how the p-value is computed, see the help file for
`quantileTest`

.

The function `quantileTestPValue`

is useful for determining what values to
use for `r`

and `k`

, given the values of `m`

, `n`

, and a
specified significance level `\alpha`

. The function
`quantileTestPValue`

can be used to reproduce Tables A.6-A.9 in
USEPA (1994, pp.A.22-A.25).

### Value

numeric vector of p-values.

### Note

See the help file for `quantileTest`

.

### Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

### References

See the help file for `quantileTest`

.

### See Also

`quantileTest`

, `wilcox.test`

,
`htest.object`

, Hypothesis Tests.

### Examples

```
# Reproduce the first column of Table A.9 in USEPA (1994, p.A.25):
#-----------------------------------------------------------------
p.vals <- quantileTestPValue(m = 5, n = seq(15, 45, by = 5),
r = c(9, 3, 4, 4, 5, 5, 6), k = c(4, 2, 2, 2, 2, 2, 2))
round(p.vals, 3)
#[1] 0.098 0.091 0.119 0.089 0.109 0.087 0.103
#==========
# Clean up
#---------
rm(p.vals)
```

*EnvStats*version 2.8.1 Index]