# friedman.test

##### Friedman Rank Sum Test

Performs a Friedman rank sum test with unreplicated blocked data.

- Keywords
- htest

##### Usage

`friedman.test(y, …)`# S3 method for default
friedman.test(y, groups, blocks, …)

# S3 method for formula
friedman.test(formula, data, subset, na.action, …)

##### 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. - formula
- a formula of the form
`a ~ b | c`

, where`a`

,`b`

and`c`

give the data values and corresponding groups and blocks, respectively. - 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
`NA`

s. Defaults to`getOption("na.action")`

. - …
- further arguments to be passed to or from methods.

##### Details

`friedman.test`

can be used for analyzing unreplicated complete
block designs (i.e., there is exactly one observation in `y`

for each combination of levels of `groups`

and `blocks`

)
where the normality assumption may be violated. The null hypothesis is that apart from an effect of `blocks`

,
the location parameter of `y`

is the same in each of the
`groups`

. If `y`

is a matrix, `groups`

and `blocks`

are
obtained from the column and row indices, respectively. `NA`

's
are not allowed in `groups`

or `blocks`

; if `y`

contains `NA`

's, corresponding blocks are removed.

##### Value

A list with class `"htest"`

containing the following components:

`"Friedman rank sum test"`

.##### References

Myles Hollander and Douglas A. Wolfe (1973),
*Nonparametric Statistical Methods.*
New York: John Wiley & Sons.
Pages 139--146.

##### See Also

##### Examples

`library(stats)`

```
## Hollander & Wolfe (1973), p. 140ff.
## Comparison of three methods ("round out", "narrow angle", and
## "wide angle") for rounding first base. For each of 18 players
## and the three method, the average time of two runs from a point on
## the first base line 35ft from home plate to a point 15ft short of
## second base is recorded.
RoundingTimes <-
matrix(c(5.40, 5.50, 5.55,
5.85, 5.70, 5.75,
5.20, 5.60, 5.50,
5.55, 5.50, 5.40,
5.90, 5.85, 5.70,
5.45, 5.55, 5.60,
5.40, 5.40, 5.35,
5.45, 5.50, 5.35,
5.25, 5.15, 5.00,
5.85, 5.80, 5.70,
5.25, 5.20, 5.10,
5.65, 5.55, 5.45,
5.60, 5.35, 5.45,
5.05, 5.00, 4.95,
5.50, 5.50, 5.40,
5.45, 5.55, 5.50,
5.55, 5.55, 5.35,
5.45, 5.50, 5.55,
5.50, 5.45, 5.25,
5.65, 5.60, 5.40,
5.70, 5.65, 5.55,
6.30, 6.30, 6.25),
nrow = 22,
byrow = TRUE,
dimnames = list(1 : 22,
c("Round Out", "Narrow Angle", "Wide Angle")))
friedman.test(RoundingTimes)
## => strong evidence against the null that the methods are equivalent
## with respect to speed
wb <- aggregate(warpbreaks$breaks,
by = list(w = warpbreaks$wool,
t = warpbreaks$tension),
FUN = mean)
wb
friedman.test(wb$x, wb$w, wb$t)
friedman.test(x ~ w | t, data = wb)
```

*Documentation reproduced from package stats, version 3.3.3, License: Part of R 3.3.3*