nestedRanksTest: Mann-Whitney-Wilcoxon ranks test when data are in groups.
In douglasgscofield/nestedRanksTest: Mann-Whitney-Wilcoxon Test for Nested Ranks

Description Usage Arguments Value Note References See Also Examples

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. This test is intended to be a mixed-model extension of the wilcox.test, for which treatment is a fixed effect and group membership is a random effect.

## S3 method for class 'formula'
nestedRanksTest(formula, data, groups = NULL, subset, ...)

## Default S3 method:
nestedRanksTest(x, y, groups, n.iter = 10000,
  lightweight = FALSE, ...)

`formula`	A formula of the form `lhs ~ rhs` or `lhs ~ rhs \| groups`, where `lhs` is a numeric variable giving the data values, `rhs` is a variable obeying conditions for `x`, and `groups` is a variable obeying conditions for `groups`. If `"\| groups"` is not included in the formula, group membership must be specified with the `groups` argument.
`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)`.
`groups`	A (non-empty) vector specifying group membership for each `y`, coerced to a factor. There must be at least one `y` in each group for each treatment level.
`subset`	An optional vector specifying a subset of observations to be used.
`...`	Further arguments to be passed to or from methods.
`x`	A (non-empty) vector of treatments for each `y`, coerced to factor. Must contain exactly two levels.
`y`	A (non-empty) numeric vector of data values.
`n.iter`	Number of bootstrap iterations to perform. The value of the final iteration is provided by the observed Z-score. Using `n.iter = 1` simply returns the observed Z-score.
`lightweight`	If `TRUE`, the vector of individual values of the null distribution is excluded from the return value of class `'htest_boot'`. By default the null distribution is included. If `n.iter` is large, specifying `TRUE` for this option can save space, but note that calling `plot` on the return value will produce an error if so.

A list with class 'htest_boot' based on class 'htest' containing the following components. Components marked with "*" are additions to 'htest'.

`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 `NA` values.
`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 `nestedRanksTest_weights`.
`null.distribution*`	null distribution of Z-scores, with `statistic` the last value.

The length of null.distribution equals n.iter. Note that null.distribution will not be present if the lightweight = TRUE option was given to nestedRanksTest.

Cases for which any of x, y or groups are NA are removed.

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.

Thompson, P. G., Smouse, P. E., Scofield, D. G. and Sork, V. L. (2014) What seeds tell us about birds: a multi-year analysis of acorn woodpecker foraging movements. Movement Ecology 2:12. http://www.movementecologyjournal.com/content/2/1/12

https://github.com/douglasgscofield/nestedRanksTest

wilcox.test, print.htest_boot, plot.htest_boot

require(graphics)

data(woodpecker_multiyear)

## S3 method for class 'formula'

## n.iter set to 1000 to shorten completion time

## group in formula
nestedRanksTest(Distance ~ Year | Granary, n.iter = 1000,
                data = woodpecker_multiyear,
                subset = Species == "agrifolia")
## group in 'groups='
nestedRanksTest(Distance ~ Year, groups = Granary, n.iter = 1000,
                data = woodpecker_multiyear,
                subset = Species == "lobata")

## Default S3 method

dat.a <- subset(woodpecker_multiyear, Species == "agrifolia")
## arguments in default order
nestedRanksTest(dat.a$Year, dat.a$Distance, dat.a$Granary, n.iter = 1000)
## named arguments used in 'formula' order
res <- with(subset(woodpecker_multiyear, Species == "lobata"),
           nestedRanksTest(y = Distance, x = Year, groups = Granary,
                           n.iter = 1000))
plot(res)