r2dtable {stats} | R Documentation |
Generate random 2-way tables with given marginals using Patefield's algorithm.
r2dtable(n, r, c)
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 |
c |
a non-negative vector of length at least 2 giving the column
totals, to be coerced to |
A list of length n
containing the generated tables as its
components.
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. doi: 10.2307/2346669.
## 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 ...