Description Usage Arguments Value References Examples
GenMVGpois
simulates a sample of size sample.size from a set of multivariate generalized
Poisson variables with correlation matrix cmat.star and pre-specified marginals.
1 2 3 4 5 6 7 8 | GenMVGpois(
sample.size,
no.gpois,
cmat.star,
theta.vec,
lambda.vec,
details = TRUE
)
|
sample.size |
desired sample size (number of rows) for the multivariate generalized Poisson data |
no.gpois |
dimension of the multivariate generalized Poisson distribution. |
cmat.star |
intermediate correlation matrix. |
theta.vec |
rate parameters in the generalized Poisson distribution. It is assumed that the length of the vector is at least two, and each value has to be a positive number. |
lambda.vec |
dispersion parameters in the generalized Poisson distribution. It is assumed that the length of the vector is at least two. All lambda values have to be less than 1. For lambda < 0, lambda must be greater than or equal to -theta/4. |
details |
index of whether to display the specified and empirical values of parameters. Default is set to TRUE. |
Data that follow multivariate generalized Poisson distribution.
Amatya, A. and Demirtas, H. (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals. Journal of Statistical Computation and Simulation, 85(15), 3129-3139.
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. (2017). On accurate and precise generation of generalized Poisson variates. Communications in Statistics - Simulation and Computation, 46(1), 489-499.
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.
1 2 3 4 5 6 7 8 9 10 11 12 | sample.size = 10000; no.gpois = 3
lambda.vec = c(-0.2, 0.2, -0.3); theta.vec = c(1, 3, 4)
M = c(0.352, 0.265, 0.342); N = diag(3); N[lower.tri(N)] = M
TV = N + t(N); diag(TV) = 1
cstar = CmatStarGpois(TV, theta.vec, lambda.vec, verbose = TRUE)
data = GenMVGpois(sample.size, no.gpois, cstar, theta.vec, lambda.vec, details = FALSE)
apply(data, 2, mean) # empirical means
theta.vec/(1 - lambda.vec) # theoretical means
apply(data, 2, var) # empirical variances
theta.vec/(1 - lambda.vec)^3 # theoretical variances
cor(data) # empirical correlation matrix
TV # specified correlation matrix
|
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