r2dtable {stats} | R Documentation |

## Random 2-way Tables with Given Marginals

### Description

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

### 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 AS159. An efficient method of generating r x c tables
with given row and column totals.
*Applied Statistics* **30**, 91–97.

### Examples

## Fisher's Tea Drinker data.
TeaTasting <-
matrix(c(3, 1, 1, 3),
nr = 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 ...

[Package

*stats* version 2.5.0

Index]