PMCMRplus (version 1.9.3)

frdManyOneExactTest: Exact Many-to-One Test for Unreplicated Blocked Data

Description

Performs an exact non-parametric many-to-one comparison test for Friedman-type ranked data according to Eisinga et al. (2017).

Usage

frdManyOneExactTest(y, ...)
# S3 method for default
frdManyOneExactTest(y, groups, blocks, p.adjust.method = p.adjust.methods, ...)

Arguments

y

a numeric vector of data values, or a list of numeric data vectors.

groups

a vector or factor object giving the group for the corresponding elements of "x". Ignored with a warning if "x" is a list.

blocks

a vector or factor object giving the block for the corresponding elements of "x". Ignored with a warning if "x" is a list.

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" 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

For many-to-one comparisons (pairwise comparisons with one control) in a two factorial unreplicated complete block design with non-normally distributed residuals, an exact test can be performed on Friedman-type ranked data.

Let there be \(k\) groups including the control, then the number of treatment levels is \(m = k - 1\). A total of \(m\) 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\(_i: \theta_0 \ne \theta_i, ~~ (1 \le i \le m)\).

The exact \(p\)-values are computed using the code of "pexactfrsd.R" that was a supplement to the publication of Eisinga et al. (2017). Additionally, any of the \(p\)-adjustment methods as included in p.adjust can be selected, for \(p\)-value adjustment.

References

Eisinga, R., Heskes, T., Pelzer, B., Te Grotenhuis, M. (2017) Exact p-values for Pairwise Comparison of Friedman Rank Sums, with Application to Comparing Classifiers, BMC Bioinformatics, 18:68.

See Also

friedmanTest, friedman.test, frdManyOneDemsarTest, frdManyOneNemenyiTest.

Examples

# 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.
 ## Assume A is the control.

 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, LETTERS[1:6]))

 ## Global Friedman test
 friedmanTest(y)

 ## Demsar's many-one test
 frdManyOneDemsarTest(y=y, p.adjust = "bonferroni")

 ## Exact many-one test
 frdManyOneExactTest(y=y, p.adjust = "bonferroni")

 ## Nemenyi's many-one test
 frdManyOneNemenyiTest(y=y)
 
# }