# quade.test

##### Quade Test

Performs a Quade test with unreplicated blocked data.

- Keywords
- htest

##### Usage

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

# S3 method for formula
quade.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

`quade.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:

`"Quade test"`

.##### References

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. William J. Conover (1999),
*Practical nonparametric statistics*.
New York: John Wiley & Sons.
Pages 373--380.

##### See Also

##### Examples

`library(stats)`

```
## 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)
```

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