ran.CMData: Generate a random CMData object

Description

Generates random exchangeably correlated multinomial data based on a parametric distribution or using resampling. The Dirichlet-Multinomial, Logistic-Normal multinomial, and discrete mixture multinomial parametric distributions are implemented. All observations will be assigned to the same treatment group, and there is no guarantee of a specific order of the observations in the output.

Usage

ran.CMData(n, ncat, clustersize.gen, distribution)

Value

a CMData object with randomly generated number of responses with sample sizes specified in the call

Arguments

n: number of independent clusters to generate
ncat: number of response categories
clustersize.gen: either an integer vector specifying the sizes of the clusters, which will be recycled to achieve the target number of clusters n; or a function with one parameter that returns an integer vector of cluster sizes when the target number of clusters n is passed to it as a parameter
distribution: a list with two components: "multinom.gen" and "param" that specifies the generation process for each cluster. The "multinom.gen" component should be a function of three parameters: number of clusters, vector of cluster sizes, and parameter list, that a matrix of response counts where each row is a cluster and each column is the number of responses of a given type. The "param" component should specify the list of parameters needed by the multinom.gen function.

Author

Aniko Szabo

Examples

Run this code


# Resample from the dehp dataset
data(dehp)
ran.dehp <- ran.CMData(20, 3, 10, list(multinom.gen=mg.Resample, param=list(data=dehp)))

# Dirichlet-Multinomial distribution with two treatment groups and random cluster sizes
binom.cs <- function(n){rbinom(n, p=0.3, size=10)+1}
dm1 <- ran.CMData(15, 4, binom.cs, 
                  list(multinom.gen=mg.DirMult, param=list(shape=c(2,3,2,1))))
dm2 <- ran.CMData(15, 4, binom.cs, 
                  list(multinom.gen=mg.DirMult, param=list(shape=c(1,1,4,1))))
ran.dm <- rbind(dm1, dm2)

# Logit-Normal multinomial distribution
ran.ln <- ran.CMData(13, 3, 3, 
                     list(multinom.gen=mg.LogitNorm,
                          param=list(mu=c(-1, 1), sigma=matrix(c(1,0.8,0.8,2), nr=2))))

# Mixture of two multinomial distributions
unif.cs <- function(n){sample(5:9, size=n, replace=TRUE)}
ran.mm <- ran.CMData(6, 3, unif.cs, 
                     list(multinom.gen=mg.MixMult,
                          param=list(q=c(0.8,0.2), m=rbind(c(-1,0), c(0,2)))))