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PMCMRplus (version 1.9.3)

NPMTest: All-Pairs Comparisons for Simply Ordered Mean Ranksums

Description

Performs Nashimoto and Wright's all-pairs comparison procedure for simply ordered mean ranksums.

Usage

NPMTest(x, ...)

# S3 method for default NPMTest( x, g, alternative = c("greater", "less"), method = c("look-up", "boot", "asympt"), nperm = 10000, ... )

# S3 method for formula NPMTest( formula, data, subset, na.action, alternative = c("greater", "less"), method = c("look-up", "boot", "asympt"), nperm = 10000, ... )

Arguments

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 "x". Ignored with a warning if "x" is a list.

alternative

the alternative hypothesis. Defaults to greater.

method

a character string specifying the test statistic to use. Defaults to "look-up" that uses published Table values of Williams (1972).

nperm

number of permutations for the asymptotic permutation test. Defaults to 1000. Ignored, if method = "look-up".

formula

a formula of the form response ~ group where response gives the data values and group a vector or factor of the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

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 NAs. Defaults to getOption("na.action").

Value

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

statistic

the estimated statistic(s)

crit.value

critical values for \(\alpha = 0.05\).

alternative

a character string describing the alternative hypothesis.

parameter

the parameter(s) of the test distribution.

dist

a string that denotes the test distribution.

There are print and summary methods available.

Either a list of class "PMCMR" or a list with class "osrt" that contains the following components:

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

statistic

the estimated statistic(s)

crit.value

critical values for \(\alpha = 0.05\).

alternative

a character string describing the alternative hypothesis.

parameter

the parameter(s) of the test distribution.

dist

a string that denotes the test distribution.

There are print and summary methods available.

A list with class "PMCMR" containing the following components:

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

statistic

lower-triangle matrix of the estimated quantiles of the pairwise test statistics.

p.value

lower-triangle matrix of the p-values for the pairwise tests.

alternative

a character string describing the alternative hypothesis.

p.adjust.method

a character string describing the method for p-value adjustment.

model

a data frame of the input data.

dist

a string that denotes the test distribution.

Details

The procedure uses the property of a simple order, \(\theta_m' - \theta_m \le \theta_j - \theta_i \le \theta_l' - \theta_l \qquad (l \le i \le m~\mathrm{and}~ m' \le j \le l')\). The null hypothesis H\(_{ij}: \theta_i = \theta_j\) is tested against the alternative A\(_{ij}: \theta_i < \theta_j\) for any \(1 \le i < j \le k\).

The all-pairs comparisons test statistics for a balanced design are $$ \hat{h}_{ij} = \max_{i \le m < m' \le j} \frac{\left(\bar{R}_{m'} - \bar{R}_m \right)}{\sigma_a / \sqrt{n}}, $$

with \(n = n_i; ~ N = \sum_i^k n_i ~~ (1 \le i \le k)\), \(\bar{R}_i\) the mean rank for the \(i\)th group, and \(\sigma_a = \sqrt{N \left(N + 1 \right) / 12}\). The null hypothesis is rejected, if \(h_{ij} > h_{k,\alpha,\infty}\).

For the unbalanced case with moderate imbalance the test statistic is $$ \hat{h}_{ij} = \max_{i \le m < m' \le j} \frac{\left(\bar{R}_{m'} - \bar{R}_m \right)} {\sigma_a \left(1/n_m + 1/n_{m'}\right)^{1/2}}, $$

The null hypothesis is rejected, if \(\hat{h}_{ij} > h_{k,\alpha,\infty} / \sqrt{2}\).

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 \(\alpha = 0.05\) (one-sided) are looked up according to the number of groups (\(k\)) and the degree of freedoms (\(v = \infty\)).

If method = "boot" an asymetric permutation test is conducted and \(p\)-values is returned.

If method = "asympt" is selected the asymptotic \(p\)-value is estimated as implemented in the function pHayStonLSA of the package NSM3.

References

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.

Nashimoto, K., Wright, F.T. (2007) Nonparametric Multiple-Comparison Methods for Simply Ordered Medians. Comput Stat Data Anal 51, 5068<U+2013>5076.

See Also

MTest

Examples

Run this code
# NOT RUN {
## 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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