Hayter-Stone All-Pairs Comparison Test
Performs the non-parametric Hayter-Stone all-pairs procedure to test against monotonically increasing alternatives.
hsAllPairsTest(x, ...)
## Default S3 method:
hsAllPairsTest(
x,
g,
alternative = c("greater", "less"),
method = c("look-up", "boot", "asympt"),
nperm = 10000,
...
)
## S3 method for class 'formula'
hsAllPairsTest(
formula,
data,
subset,
na.action,
alternative = c("greater", "less"),
method = c("look-up", "boot", "asympt"),
nperm = 10000,
...
)x |
a numeric vector of data values, or a list of numeric data vectors. |
... |
further arguments to be passed to or from methods. |
g |
a vector or factor object giving the group for the
corresponding elements of |
alternative |
the alternative hypothesis. Defaults to |
method |
a character string specifying the test statistic to use.
Defaults to |
nperm |
number of permutations for the asymptotic permutation test.
Defaults to |
formula |
a formula of the form |
data |
an optional matrix or data frame (or similar: see
|
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function which indicates what should happen when
the data contain |
Let X be an identically and idepentendly distributed variable that was n times observed at k increasing treatment levels. Hayter and Stone (1991) proposed a non-parametric procedure to test the null hypothesis, H: θ_i = θ_j ~~ (i < j ≤ k) against a simple order alternative, A: θ_i < θ_j.
The statistic for all-pairs comparisons is calculated as,
SEE PDF.
with the Mann-Whittney counts:
SEE PDF
Under the large sample approximation, the test statistic S_{ij} is distributed as h_{k,α,v}. Thus, the null hypothesis is rejected, if S_{ij} > h_{k,α,v}, with v = ∞ degree of freedom.
If method = "look-up" the function will not return
p-values. Instead the critical h-values
as given in the tables of Hayter (1990) for
α = 0.05 (one-sided)
are looked up according to the number of groups (k) and
the degree of freedoms (v = ∞).
If method = "boot" an asymetric permutation test
is conducted and p-values are returned.
If method = "asympt" is selected the asymptotic
p-value is estimated as implemented in the
function pHayStonLSA of the package NSM3.
Either a list of class "PMCMR" or a
list with class "osrt" that contains the following
components:
a character string indicating what type of test was performed.
a character string giving the name(s) of the data.
the estimated statistic(s)
critical values for α = 0.05.
a character string describing the alternative hypothesis.
the parameter(s) of the test distribution.
a string that denotes the test distribution.
There are print and summary methods available.
A list with class "PMCMR" containing the following components:
a character string indicating what type of test was performed.
a character string giving the name(s) of the data.
lower-triangle matrix of the estimated quantiles of the pairwise test statistics.
lower-triangle matrix of the p-values for the pairwise tests.
a character string describing the alternative hypothesis.
a character string describing the method for p-value adjustment.
a data frame of the input data.
a string that denotes the test distribution.
If method = "asympt" is selected, this function calls
an internal probability function pHS. The GPL-2 code for
this function was taken from pHayStonLSA of the
the package NSM3:
Grant Schneider, Eric Chicken and Rachel Becvarik (2020) NSM3: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition. R package version 1.15. https://CRAN.R-project.org/package=NSM3
Hayter, A. J.(1990) A One-Sided Studentised Range Test for Testing Against a Simple Ordered Alternative, Journal of the American Statistical Association 85, 778–785.
Hayter, A.J., Stone, G. (1991) Distribution free multiple comparisons for monotonically ordered treatment effects. Austral J Statist 33, 335–346.
## Example from Shirley (1977) ## Reaction times of mice to stimuli to their tails. x <- c(2.4, 3, 3, 2.2, 2.2, 2.2, 2.2, 2.8, 2, 3, 2.8, 2.2, 3.8, 9.4, 8.4, 3, 3.2, 4.4, 3.2, 7.4, 9.8, 3.2, 5.8, 7.8, 2.6, 2.2, 6.2, 9.4, 7.8, 3.4, 7, 9.8, 9.4, 8.8, 8.8, 3.4, 9, 8.4, 2.4, 7.8) g <- gl(4, 10) ## Shirley's test ## one-sided test using look-up table shirleyWilliamsTest(x ~ g, alternative = "greater") ## Chacko's global hypothesis test for 'greater' chackoTest(x , g) ## post-hoc test, default is standard normal distribution (NPT'-test) summary(chaAllPairsNashimotoTest(x, g, p.adjust.method = "none")) ## same but h-distribution (NPY'-test) chaAllPairsNashimotoTest(x, g, dist = "h") ## NPM-test NPMTest(x, g) ## Hayter-Stone test hayterStoneTest(x, g) ## all-pairs comparisons hsAllPairsTest(x, g)
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