multinom.gen: Functions for generating multinomial outcomes

In CorrBin: Nonparametrics with Clustered Binary and Multinomial Data

Functions for generating multinomial outcomes

Description

These are built-in functions to be used by ran.CMData for generating random multinomial data.

Usage

mg.Resample(n, clustersizes, param)

mg.DirMult(n, clustersizes, param)

mg.LogitNorm(n, clustersizes, param)

mg.MixMult(n, clustersizes, param)

Arguments

n	number of independent clusters to generate
clustersizes	an integer vector specifying the sizes of the clusters
param	a list of parameters for each specific generator

Details

For mg.Resample: the param list should be list(param=...), in which the CMData object to be resampled is passed.

For mg.DirMult: the param list should be list(shape=...), in which the parameter vector of the Dirichlet distribution is passed (see rdirichlet).

For mg.LogitNorm: the param list should be list(mu=...,sigma=...), in which the mean vector and covariance matrix of the underlying Normal distribution are passed. If sigma is NULL (or missing), then an identity matrix is assumed. They should have K-1 dimensions for a K-variate multinomial.

For mg.MixMult: the param list should be list(q=...,m=...), in which the vector of mixture probabilities q and the matrix m of logit-transformed means of each component are passed. For a K-variate multinomial, the matrix m should have K-1 columns and length(q) rows.

CorrBin documentation built on Sept. 11, 2024, 8:25 p.m.