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Last data update: 2014.03.03

R: Distribution of the Wald Wolfowitz Runs Statistic

Runs	R Documentation

Distribution of the Wald Wolfowitz Runs Statistic

Description

Probability function, distribution function, quantile function and random generation for the distribution of the Runs statistic obtained from samples with n1 and n2 elements of each type.

Usage

druns(x, n1, n2, log = FALSE)
pruns(q, n1, n2, lower.tail = TRUE, log.p = FALSE)
qruns(p, n1, n2, lower.tail = TRUE, log.p = FALSE)
rruns(n, n1, n2)

Arguments

`x, q`	a numeric vector of quantiles.
`p`	a numeric vector of probabilities.
`n`	number of observations to return.
`n1, n2`	the number of elements of first and second type, respectively.
`log, log.p`	logical; if TRUE, probabilities p are given as log(p).
`lower.tail`	logical; if TRUE (default), probabilities are P[X ≤ x] otherwise, P[X > x].

Details

The Runs distribution has probability function

P(R=r) = 2 choose(n1-1,r/2-1)choose(n2-1,r/2-1)/choose(n1+n2,n1), if r is even P(R=r) =

for r = 2, 3, …, 2 min(n1+n2)+c with c=0 if n1 = n2 or c=1 if n_1 =! n_2.

If an element of x is not integer, the result of druns is zero.

The quantile is defined as the smallest value x such that F(x) ≥ p, where F is the distribution function.

Value

druns gives the probability function, pruns gives the distribution function and qruns gives the quantile function.

References

Swed, F.S. and Eisenhart, C. (1943). Tables for Testing Randomness of Grouping in a Sequence of Alternatives, Ann. Math Statist. 14(1), 66-87.

Examples

##
## Example: Distribution Function
## Creates Table I in Swed and Eisenhart (1943), p. 70,
## with n1 = 2 and n1 <= n2 <= 20
##
m <- NULL
for (i in 2:20){
  m <- rbind(m, pruns(2:5,2,i))  
}
rownames(m)=2:20
colnames(m)=2:5
#
#              2         3         4 5
# 2  0.333333333 0.6666667 1.0000000 1
# 3  0.200000000 0.5000000 0.9000000 1
# 4  0.133333333 0.4000000 0.8000000 1
# 5  0.095238095 0.3333333 0.7142857 1
# 6  0.071428571 0.2857143 0.6428571 1
# 7  0.055555556 0.2500000 0.5833333 1
# 8  0.044444444 0.2222222 0.5333333 1
# 9  0.036363636 0.2000000 0.4909091 1
# 10 0.030303030 0.1818182 0.4545455 1
# 11 0.025641026 0.1666667 0.4230769 1
# 12 0.021978022 0.1538462 0.3956044 1
# 13 0.019047619 0.1428571 0.3714286 1
# 14 0.016666667 0.1333333 0.3500000 1
# 15 0.014705882 0.1250000 0.3308824 1
# 16 0.013071895 0.1176471 0.3137255 1
# 17 0.011695906 0.1111111 0.2982456 1
# 18 0.010526316 0.1052632 0.2842105 1
# 19 0.009523810 0.1000000 0.2714286 1
# 20 0.008658009 0.0952381 0.2597403 1
#