PoisNonNor (version 1.6.1)

RNG.P.NN: Simultaneously generates count and continuous data

Description

This function simulates count and continuous data, where the count part is assumed to follow a multivariate Poisson distribution and the continuous part can take any shape allowed by the Fleishman polynomials. A correlation matrix and marginal features (rate parameter for the count variables, and skewness and kurtosis parameters for the continuous variables must be supplied by users).

Usage

RNG.P.NN(lamvec, cmat, rmat, norow, mean.vec, variance.vec) 
RNG_P_NN(lamvec, cmat, rmat, norow, mean.vec, variance.vec) #Deprecated

Arguments

lamvec

a vector of lambda values of length n1

cmat

specified correlation matrix

rmat

a n2x2 matrix that includes skewness and kurtosis values for each continuous variable

norow

number of rows in the multivariate mixed data

mean.vec

mean vector for continuous variables of length n2

variance.vec

variance vector for continuous variables of length n2

Value

Returns a data matrix of size norowx(n1+n2). By design, the first n1 variables are count, and the last n2 variables are continuous.

References

Amatya, A. and Demirtas, H. (2017). PoisNor: An R package for generation of multivariate data with Poisson and normal marginals. Communications in Statistics--Simulation and Computation, 46(3), 2241-2253.

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2):104-109.

Demirtas, H., Hedeker, D. and Mermelstein, R.J. (2012). Simulation of massive public health data by power polynomials. Statistics in Medicine, 31(27), 3337-3346.

Fleishman A.I. (1978). A method for simulating non-normal distributions. Psychometrika, 43(4), 521-532.

Vale, C.D. and Maurelli, V.A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48(3), 465-471.

Yahav, I. and Shmueli, G. (2012). On generating multivariate poisson data in management science applications. Applied Stochastic Models in Business and Industry, 28(1), 91-102.

Examples

# NOT RUN {
lamvec = c(0.5,0.7,0.9)

cmat = matrix(c(
  1.000,  0.352,  0.265, 0.342,  0.090, 0.141,
  0.352,  1.000,  0.121, 0.297, -0.022, 0.177,
  0.265,  0.121,  1.000, 0.294, -0.044, 0.129,
  0.342,  0.297,  0.294, 1.000,  0.100, 0.354,
  0.090, -0.022, -0.044, 0.100,  1.000, 0.386,
  0.141,  0.177,  0.129, 0.354,  0.386, 1.000), nrow=6, byrow=TRUE)

rmat = matrix(c(-0.5486,-0.2103, 0.3386, 0.9035, 1.0283, 0.9272), byrow=TRUE, ncol=2)

norow=10e+5

mean.vec=c(1,0.5,100)
variance.vec=c(1,0.02777778,1000)

P_NN_data = RNG.P.NN(lamvec, cmat, rmat, norow, mean.vec, variance.vec)
# }