cDurSkiMa {NSM3} | R Documentation |
Computes a critical value for the Durbin, Skillings-Mack D distribution.
Description
This function computes the critical value for the Durbin, Skillings-Mack D distribution at (or typically in the "Exact" and "Monte Carlo" cases, close to) the given alpha level.
Usage
cDurSkiMa(alpha,obs.mat, method=NA, n.mc=10000)
Arguments
alpha |
A numeric value between 0 and 1. |
obs.mat |
The incidence matrix, explained below. |
method |
Either "Exact", "Monte Carlo" or "Asymptotic", indicating the desired distribution. When method=NA, "Exact" will be used if the number of permutations is 10,000 or less. Otherwise, "Monte Carlo" will be used. |
n.mc |
If method="Monte Carlo", the number of Monte Carlo samples used to estimate the distribution. Otherwise, not used. |
Details
The incidence matrix, obs.mat, will be an n x k matrix of ones and zeroes, which indicate where the data are observed and unobserved, respectively. Methods for finding the incidence matrix for various BIBD designs are given in the literature. While the incidence matrix will not be unique for a given (k, n, s, lambda, p) combination, the distribution of D under H0 will be the same.
Value
Returns a list with "NSM3Ch7c" class containing the following components:
k |
number of treatments |
n |
number of blocks |
ss |
number of treatments per block |
pp |
number of observations per treatment |
lambda |
number of times each pair of treatments occurs together within a block |
cutoff.U |
upper tail cutoff at or below user-specified alpha |
true.alpha.U |
true alpha level corresponding to cutoff.U (if method="Exact" or "Monte Carlo") |
Note
The syntax of this procedure differs from the others in the NSM3 package due to the fact that creating a BIBD for a given k,n,s,p,lambda is not trivial. We therefore require obs.mat, the incidence matrix.
Author(s)
Grant Schneider
Examples
##Hollander, Wolfe, Chicken Chapter 7, comment 49
obs.mat<-matrix(c(1,1,0,1,0,1,0,1,1),ncol=3,byrow=TRUE)
cDurSkiMa(.75,obs.mat)