Description Usage Arguments Value References Examples
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).
1 2 |
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 |
Returns a data matrix of size norowx(n1+n2). By design, the first n1 variables are count, and the last n2 variables are continuous.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## 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)
## End(Not run)
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