Learn R Programming

PMCMR (version 4.3)

posthoc.friedman.conover.test: Pairwise post-hoc Test for Multiple Comparisons of Rank Sums for Unreplicated Blocked Data (Conover-Test)

Description

Calculate pairwise comparisons using Conover post-hoc test for unreplicated blocked data. This test is usually conducted post-hoc after significant results of the friedman.test. The statistics refer to the student-t-distribution (TDist).

Usage

posthoc.friedman.conover.test(y, …)

# S3 method for default posthoc.friedman.conover.test (y, groups, blocks, 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.

p.adjust.method

Method for adjusting p values (see p.adjust).

further arguments to be passed to or from methods.

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 student-t-distribution.

statistic

The estimated quantiles of the student-t-distribution.

p.adjust.method

The applied method for p-value adjustment.

%% \item{p.adjust.method}{Defaults to "none"} %% ...

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 friedman.test. The consequent post-hoc pairwise multiple comparison test according to Conover 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.

References

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

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

See Also

friedman.test, posthoc.friedman.nemenyi.test, TDist p.adjust

Examples

Run this code
# NOT RUN {
##
## Sachs, 1997, p. 675
## Six persons (block) received six different diuretics
## (A to F, treatment).
## The responses are the Na-concentration (mval)
## in the urine measured 2 hours after each treatment.
##
y <- matrix(c(
3.88, 5.64, 5.76, 4.25, 5.91, 4.33, 30.58, 30.14, 16.92,
23.19, 26.74, 10.91, 25.24, 33.52, 25.45, 18.85, 20.45, 
26.67, 4.44, 7.94, 4.04, 4.4, 4.23, 4.36, 29.41, 30.72,
32.92, 28.23, 23.35, 12, 38.87, 33.12, 39.15, 28.06, 38.23,
26.65),nrow=6, ncol=6, 
dimnames=list(1:6,c("A","B","C","D","E","F")))
print(y)
friedman.test(y)
posthoc.friedman.conover.test(y=y, p.adjust="none")
# }

Run the code above in your browser using DataLab