| 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)