Permutation tests are increasingly common tests to perform certain types of statistical analyses. They do not rely on assumptions about the distribution of the data, as some other tests do. They are therefore considered to be nonparametric tests. It is my understanding, however, that for certain tests—for example those testing a difference in means—that there are assumptions about the underlying data.

Permutation tests work by resampling the observed data many
times in order to determine a *p*-value for the test. Recall that the *p*-value
is defined as the probability of getting data as extreme as the observed data
when the null hypothesis is true. If the data are shuffled many times in
accordance with the null hypothesis being true, the number of cases with data as
extreme as the observed data could be counted, and a *p*-value
calculated.

The advantages of permutation tests are

• the lack of assumptions about the distribution of the underlying data,

• their flexibility in the kinds of data they can handle (nominal, ordinal, interval/ratio),

• and their being relatively straightforward to conduct and interpret.

The disadvantages of permutation tests are

• the limited complexity of designs they can handle (for the *coin*
package at the time of writing),

• the unfamiliarity of them for many readers.

#### The coin package

Permutation tests in this book will use the *coin *package,
with either of two functions, *independence_test* and *symmetry_test*.
This book with use permutation tests with ordinal dependent variables, but the *coin*
package is able to handle nominal, ordinal, and interval/ratio data.

A few notes on using permutation tests:

• If the dependent variable is to be treated as an ordinal variable,
it must be coded as an ordered factor variable in R. It does not need to have
numerals for levels. For example it could have levels *doctorate* > *masters*
> *bachelors* > *associates* > *high.school*.
But also it could have the levels *5* > *4* > *3* > *2*
> *1*.

• The general interpretation for significant results of these models isn’t that there is a difference among medians, but that there is a significant effect of the independent variable on the dependent variable, or that there is a significant difference among groups.

• Post-hoc tests for factors or groups can be conducted with pairwise
tests of groups, or with pairwise ordinal tests for paired data. The
appropriate functions in the *rcompanion* package are *pairwisePermutationTest*,
*pairwisePermutationMatrix*, *pairwisePermutationSymmetry*, *and pairwisePermutationSymmetryMatrix*.

• Permutation tests for data arranged in contingency
tables are presented in the *Association Tests for Ordinal Tables* chapter.

### References

For more information on permutation tests and the *coin*
package, see:

Hothorn, T., K. Hornik, M.A. van de Wiel, and A. Zeileis.
2015. *Implementing a Class of Permutation Tests: The coin Package*. cran.r-project.org/web/packages/coin/vignettes/coin_implementation.pdf.

library(coin); help(package="coin")

### Packages used in this chapter

The packages used in this chapter include:

• coin

The following commands will install these packages if they are not already installed:

if(!require(coin)){install.packages("coin")}

### Permutation test example

The following example uses the left hand and right hand data
from the *Independent and Paired Values* chapter. For this example, we
are interested in comparing the length of left hands and rights from 16
individuals. First we will compare the left hands to right hands as independent
samples (analogous to a Mann–Whitney test or t-test), then as paired values for
each individual (analogous to a paired rank sum test or paired t-test).

Input = ("

Individual Hand Length

A Left 17.5

B Left 18.4

C Left 16.2

D Left 14.5

E Left 13.5

F Left 18.9

G Left 19.5

H Left 21.1

I Left 17.8

J Left 16.8

K Left 18.4

L Left 17.3

M Left 18.9

N Left 16.4

O Left 17.5

P Left 15.0

A Right 17.6

B Right 18.5

C Right 15.9

D Right 14.9

E Right 13.7

F Right 18.9

G Right 19.5

H Right 21.5

I Right 18.5

J Right 17.1

K Right 18.9

L Right 17.5

M Right 19.5

N Right 16.5

O Right 17.4

P Right 15.6

")

Data = read.table(textConnection(Input),header=TRUE)

### Check the data frame

Data

str(Data)

summary(Data)

### Remove unnecessary objects

rm(Input)

### Summarize data

library(FSA)

Summarize(Length ~ Hand,

data=Data,

digits=3)

Hand n nvalid mean sd min Q1 median Q3 max
percZero

1 Left 16 16 17.356 1.948 13.5 16.35 17.50 18.52 21.1 0

2 Right 16 16 17.594 1.972 13.7 16.35 17.55 18.90 21.5 0

#### Box plot

boxplot(Length ~ Hand,

data=Data,

ylab="Length, cm")

#### Scatter plot with one-to-one line

In the plot below, each point represents a pair of paired values. Points that fall above and to the left of the blue line indicate cases for which the value for Right was greater than for Left.

Left = Data$Length[Data$Hand=="Left"]

Right = Data$Length[Data$Hand=="Right"]

plot(Left, Right,

pch = 16, # shape of points

cex = 1.0, # size of points

xlim=c(13, 22), # limits of x axis

ylim=c(13, 22), # limits of y axis

xlab="Left hand",

ylab="Right hand")

abline(0,1, col="blue", lwd=2) # line
with intercept of 0 and slope of 1

#### Permutation test of independence

This tests treats the two groups (left hand and right hand) as independent samples, and tests if there is a difference in values between the two groups.

library(coin)

independence_test(Length ~ Hand,

data = Data)

Asymptotic General Independence Test

Z = -0.34768, p-value = 0.7281

#### Permutation test of symmetry

This tests treats the two groups (left hand and right hand)
as having paired or repeated data, paired within *Individual*. Practically,
it tests if there is a difference in values between the two groups.

library(coin)

symmetry_test(Length ~ Hand | Individual,

data = Data)

Asymptotic General Symmetry Test

Z = -2.6348, p-value = 0.008418