kwManyOneConoverTest: Conover's Many-to-One Rank Comparison Test

In PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended

Conover's Many-to-One Rank Comparison Test

Description

Performs Conover's non-parametric many-to-one comparison test for Kruskal-type ranked data.

Usage

kwManyOneConoverTest(x, ...)

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

## S3 method for class 'formula'
kwManyOneConoverTest(
  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").

Details

For many-to-one comparisons (pairwise comparisons with one control) in an one-factorial layout with non-normally distributed residuals Conover's non-parametric test can be performed. Let there be k groups including the control, then the number of treatment levels is m = k - 1. Then m pairwise comparisons can be performed between the i-th treatment level and the control. H_i: \theta_0 = \theta_i is tested in the two-tailed case against A_i: \theta_0 \ne \theta_i, ~~ (1 \le i \le m).

If p.adjust.method == "single-step" is selected, the p-values will be computed from the multivariate t distribution. Otherwise, the p-values are computed from the t-distribution using any of the p-adjustment methods as included in p.adjust.

Value

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.

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).

References

Conover, W. J, Iman, R. L. (1979) On multiple-comparisons procedures, Tech. Rep. LA-7677-MS, Los Alamos Scientific Laboratory.

See Also

pmvt, TDist, kruskalTest, kwManyOneDunnTest, kwManyOneNdwTest

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)

PMCMRplus documentation built on Nov. 27, 2023, 1:08 a.m.