# r2dtable

##### Random 2-way Tables with Given Marginals

Generate random 2-way tables with given marginals using Patefield's algorithm.

- Keywords
- distribution

##### Usage

`r2dtable(n, r, c)`

##### Arguments

- n
a non-negative numeric giving the number of tables to be drawn.

- r
a non-negative vector of length at least 2 giving the row totals, to be coerced to

`integer`

. Must sum to the same as`c`

.- c
a non-negative vector of length at least 2 giving the column totals, to be coerced to

`integer`

.

##### Value

A list of length `n`

containing the generated tables as its
components.

##### References

Patefield, W. M. (1981).
Algorithm AS 159: An efficient method of generating r x c tables
with given row and column totals.
*Applied Statistics*, **30**, 91--97.
10.2307/2346669.

##### Examples

`library(stats)`

```
# NOT RUN {
## Fisher's Tea Drinker data.
TeaTasting <-
matrix(c(3, 1, 1, 3),
nrow = 2,
dimnames = list(Guess = c("Milk", "Tea"),
Truth = c("Milk", "Tea")))
## Simulate permutation test for independence based on the maximum
## Pearson residuals (rather than their sum).
rowTotals <- rowSums(TeaTasting)
colTotals <- colSums(TeaTasting)
nOfCases <- sum(rowTotals)
expected <- outer(rowTotals, colTotals, "*") / nOfCases
maxSqResid <- function(x) max((x - expected) ^ 2 / expected)
simMaxSqResid <-
sapply(r2dtable(1000, rowTotals, colTotals), maxSqResid)
sum(simMaxSqResid >= maxSqResid(TeaTasting)) / 1000
## Fisher's exact test gives p = 0.4857 ...
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*

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