nestedRanksTest package provides functions for performing a
Mann-Whitney-Wilcoxon-type nonparametric test for a difference between
treatment levels using nested ranks, together with functions for displaying
results of the test. The nested ranks test may be used when observations are
structured into several groups and each group has received both treatment
levels. The p-value is determined via bootstrapping.
nestedRanksTest is intended to be one possible mixed-model extension of
the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group
membership is a random effect. The standard Mann-Whitney-Wilcoxon test is
available in R as
The latest stable release of the package can be downloaded from CRAN:
Help is available via
?nestedRanksTest, and a vignette is available via:
The development version is hosted on github and can be installed via:
install.packages("devtools") devtools::install_github("douglasgscofield/nestedRanksTest", build_vignettes = TRUE) library(nestedRanksTest)
These statistical tools were developed in collaboration with Peter E. Smouse (Rutgers University) and Victoria L. Sork (UCLA) and were funded in part by U.S. National Science Foundation awards NSF-DEB-0514956 and NSF-DEB-0516529.
The principle function is
nestedRanksTest(), with two interfaces. The
formula interface is the simplest to use. It allows specification of
quantitative measures, treatments and group membership using R's familiar
formula syntax. Treat group membership as a random factor or
grouping variable by using
data(woodpecker_multiyear) result <- nestedRanksTest(Distance ~ Year | Granary, data = woodpecker_multiyear, subset = Species == "agrifolia") print(result)
Nested Ranks Test data: Distance by Year grouped by Granary Z = 0.27695, p-value = 1e-04 alternative hypothesis: Z lies above bootstrapped null values null values: 0% 1% 5% 10% 25% 50% 75% 90% 95% -0.29492 -0.15583 -0.11059 -0.08705 -0.04554 -0.00124 0.04430 0.08488 0.10936 99% 100% 0.15335 0.27695 bootstrap iterations: 10000 group weights: 10 31 140 151 152 938 942 0.05204461 0.04646840 0.02478315 0.14560099 0.30359356 0.29120198 0.13630731
The default interface uses arguments for specifying the variables.
# Make variables accessible using with() result <- with(subset(woodpecker_multiyear, Species == "agrifolia"), nestedRanksTest(y = Distance, x = Year, groups = Granary))
The statistic for the nested ranks test is a Z-score calculated by comparing ranks between treatment levels, with contributions of each group to the final Z-score weighted by group size. The p-value is determined by comparing the observed Z-score against a distribution of Z-scores calculated by bootstrapping ranks assuming no influence of treatment while respecting group sizes. When there is just one group, this test is essentially identical to a standard Mann-Whitney-Wilcoxon test.
For further details, please see the vignette for this package:
The generation of a null distribution can take some time. For example,
if any use of
nestedRanksTest() in the examples were run with the default
n.iter = 10000, completion would require a few seconds.
nestedRanksTest() returns a list of class
'htest_boot' based on class
'htest' containing the following components. Components marked with
are additions to
Component | Contents
statistic | the value of the observed Z-score.
p.value | the p-value for the test.
alternative | a character string describing the alternative hypothesis.
method | a character string indicating the nested ranks test performed.
data.name | a character string giving the name(s) of the data..
bad.obs | the number of observations in the data excluded because of
null.values | quantiles of the null distribution used for calculating the p-value.
n.iter* | the number of bootstrap iterations used for generating the null distribution.
weights* | the weights for groups, calculated by
null.distribution* | vector containing null distribution of Z-scores, with
statistic the last value.
The length of
n.iter. Note that
null.distribution will not be present if the
lightweight = TRUE option was
The package also includes a dataset,
woodpecker_multiyear, which contains the
data on woodpecker acorn movement underlying Figure 2 in Thompson et al.
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