manyOneUTest() R function from [PMCMRplus]

Multiple Comparisons with One Control (U-test)

Performs pairwise comparisons of multiple group levels with one control.


manyOneUTest(x, ...)

## Default S3 method:
manyOneUTest(
  x,
  g,
  alternative = c("two.sided", "greater", "less"),
  p.adjust.method = c("single-step", p.adjust.methods),
  ...
)

## S3 method for class 'formula'
manyOneUTest(
  formula,
  data,
  subset,
  na.action,
  alternative = c("two.sided", "greater", "less"),
  p.adjust.method = c("single-step", p.adjust.methods),
  ...
)

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 two.sided.
p.adjust.method: method for adjusting p values (see p.adjust)
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").

Returns

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

This functions performs Wilcoxon, Mann and Whitney's U-test for a one factorial design where each factor level is tested against one control ( $m = k -1$ tests). As the data are re-ranked for each comparison, this test is only suitable for balanced (or almost balanced) experimental designs.

For the two-tailed test and p.adjust.method = "single-step"

the multivariate normal distribution is used for controlling Type 1 error and to calculate p-values. Otherwise, the p-values are calculated from the standard normal distribution with any latter p-adjustment as available by p.adjust.

Note

Factor labels for g must be assigned in such a way, that they can be increasingly ordered from zero-dose control to the highest dose level, e.g. integers {0, 1, 2, ..., k} or letters {a, b, c, ...}. Otherwise the function may not select the correct values for intended zero-dose control.

It is safer, to i) label the factor levels as given above, and to ii) sort the data according to increasing dose-levels prior to call the function (see order, factor).

Examples


## Data set PlantGrowth
## Global test
kruskalTest(weight ~ group, data = PlantGrowth)

## Conover's many-one comparison test
## single-step means p-value from multivariate t distribution
ans <- kwManyOneConoverTest(weight ~ group, data = PlantGrowth,
                             p.adjust.method = "single-step")
summary(ans)

## Conover's many-one comparison test
ans <- kwManyOneConoverTest(weight ~ group, data = PlantGrowth,
                             p.adjust.method = "holm")
summary(ans)

## Dunn's many-one comparison test
ans <- kwManyOneDunnTest(weight ~ group, data = PlantGrowth,
                             p.adjust.method = "holm")
summary(ans)

## Nemenyi's many-one comparison test
ans <- kwManyOneNdwTest(weight ~ group, data = PlantGrowth,
                        p.adjust.method = "holm")
summary(ans)

## Many one U test
ans <- manyOneUTest(weight ~ group, data = PlantGrowth,
                        p.adjust.method = "holm")
summary(ans)

## Chen Test
ans <- chenTest(weight ~ group, data = PlantGrowth,
                    p.adjust.method = "holm")
summary(ans)

References

OECD (ed. 2006) Current approaches in the statistical analysis of ecotoxicity data: A guidance to application, OECD Series on testing and assessment, No. 54.

manyOneUTest function