R: Pairwise post-hoc Test for Multiple Comparisons of Rank Sums...

posthoc.quade.test {PMCMR}

R Documentation

Pairwise post-hoc Test for Multiple Comparisons of Rank Sums for Unreplicated Blocked Data (Quade multiple comparison test)

Description

Calculate pairwise comparisons using Quade's post-hoc test for unreplicated blocked data. This test is usually conducted post-hoc after significant results of the omnibus test, quade.test.

Usage

posthoc.quade.test(y, ...)

## Default S3 method:
 posthoc.quade.test(y, groups, blocks, dist =
 c("TDist", "Normal"), p.adjust.method = p.adjust.methods, ...)

Arguments

`y`	either a numeric vector of data values, or a data matrix.
`groups`	a vector giving the group for the corresponding elements of `y` if this is a vector; ignored if `y` is a matrix. If not a factor object, it is coerced to one.
`blocks`	a vector giving the block for the corresponding elements of `y` if this is a vector; ignored if `y` is a matrix. If not a factor object, it is coerced to one.
`dist`	the method for determining the p-value. The default distribution is `"TDist"`, else `"Normal"`.
`p.adjust.method`	Method for adjusting p values (see `p.adjust`).
`...`	further arguments to be passed to or from methods.

Details

A one-way ANOVA with repeated measures that is also referred to as ANOVA with unreplicated block design can also be conducted via the quade.test. The consequent post-hoc pairwise multiple comparison test according to Quade is conducted with this function.

If y is a matrix, than the columns refer to the treatment and the rows indicate the block.

See vignette("PMCMR") for details.

Value

A list with class "PMCMR"

`method`	The applied method.
`data.name`	The name of the data.
`p.value`	The two-sided p-value according to the selected distribution.
`statistic`	The estimated quantiles of the selected distribution.
`p.adjust.method`	The applied method for p-value adjustment.

Author(s)

Thorsten Pohlert

References

W. J. Conover (1999), Practical nonparametric Statistics, 3rd. Edition, Wiley.

N. A. Heckert and J. J. Filliben (2003). NIST Handbook 148: Dataplot Reference Manual, Volume 2: Let Subcommands and Library Functions. National Institute of Standards and Technology Handbook Series, June 2003.

D. Quade (1979), Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association, 74, 680-683.

Examples

## Conover (1999, p. 375f):
## Numbers of five brands of a new hand lotion sold in seven stores
## during one week.
y <- matrix(c( 5,  4,  7, 10, 12,
               1,  3,  1,  0,  2,
              16, 12, 22, 22, 35,
               5,  4,  3,  5,  4,
              10,  9,  7, 13, 10,
              19, 18, 28, 37, 58,
              10,  7,  6,  8,  7),
            nrow = 7, byrow = TRUE,
            dimnames =
            list(Store = as.character(1:7),
                 Brand = LETTERS[1:5]))
y
quade.test(y)
posthoc.quade.test(y, dist="TDist", p.adj="none")

[Package PMCMR version 4.3 Index]