R: Calculate Maximum Support Value for Count Variables:...

max_count_support {SimMultiCorrData}

R Documentation

Calculate Maximum Support Value for Count Variables: Correlation Method 2

Description

This function calculates the maximum support value for count variables by extending the method of Barbiero & Ferrari (2015, doi: 10.1002/asmb.2072) to include Negative Binomial variables. In order for count variables to be treated as ordinal in the calculation of the intermediate MVN correlation matrix, their infinite support must be truncated (made finite). This is done by setting the total cumulative probability equal to 1 - a small user-specified value (pois_eps or nb_eps. The maximum support value equals the inverse cdf applied to this result. The values pois_eps and nb_eps may differ for each variable. The function is used in findintercorr2 and rcorrvar2. This function would not ordinarily be called by the user.

Usage

max_count_support(k_pois, k_nb, lam, pois_eps = NULL, size, prob, mu = NULL,
  nb_eps = NULL)

Arguments

`k_pois`	the number of Poisson variables
`k_nb`	the number of Negative Binomial variables
`lam`	a vector of lambda (> 0) constants for the Poisson variables (see `Poisson`)
`pois_eps`	a vector of length k_pois containing the truncation values (i.e. = rep(0.0001, k_pois); default = NULL)
`size`	a vector of size parameters for the Negative Binomial variables (see `NegBinomial`)
`prob`	a vector of success probability parameters
`mu`	a vector of mean parameters (*Note: either `prob` or `mu` should be supplied for all Negative Binomial variables, not a mixture; default = NULL)
`nb_eps`	a vector of length `k_nb` containing the truncation values (i.e. = rep(0.0001, k_nb); default = NULL)

Value

a data.frame with k_pois + k_nb rows; the column names are:

Distribution Poisson or Negative Binomial

Number the variable index

Max the maximum support value

References

Barbiero A & Ferrari PA (2015). Simulation of correlated Poisson variables. Applied Stochastic Models in Business and Industry, 31: 669-80. doi: 10.1002/asmb.2072.