MultiDiscreteRNG (version 0.1.0)

BinToNB: Convert multivariate binary data back to the original negative binomial scale

Description

This function maps multivariate binary data to multivariate negative binomial outcomes, preserving the original marginal distribution characteristics. Given a binary representation, the function assigns negative binomial values based on the original probability mass functions and the location of the median split for each variable.

Usage

BinToNB(prop.vec.bin, NBprop, Mlocation, bin.data)

Value

A list containing the multivariate negative binomial data and its correlation matrix

Arguments

prop.vec.bin

A numeric vector of binary probabilities

NBprop

A numeric value or vector of negative binomial proportions

Mlocation

Integer indices of the medians in the vector

bin.data

A data frame or matrix of generated multivariate binary data

Examples

NB.r.vec <- c(10, 3, 16)
NB.prob.vec <- c(0.65, 0.4, 0.88)

# Compute binary probabilities, PMFs, and thresholds
p      <- calc.bin.prob.NB(NB.r.vec, NB.prob.vec)
pvec   <- p$p
prop   <- p$prop
Mloc   <- p$Mlocation

# Use only the first two variables for demonstration
pvec.pair      <- pvec[1:2]
Mlocation.pair <- Mloc[1:2]
prop.pair      <- list(prop[[1]], prop[[2]])

# Define a 2 by 2 target correlation matrix
del.next <- matrix(c(1.0, -0.3,
                     -0.3, 1.0),
                  nrow = 2, byrow = TRUE)

# Simulate 100 correlated binary observations
inter_bin <- generate.binaryVar(100, pvec.pair, del.next)

# Reconstruct the negative binomial scaled data
Mydata <- BinToNB(pvec.pair, prop.pair, Mlocation.pair, inter_bin)