genBinOrdNN: Generates a data set with binary, ordinal and continuous...

In BinOrdNonNor: Concurrent Generation of Binary, Ordinal and Continuous Data

Description

The function simulates a sample of size n from a multivariate binary, ordinal and continuous variables with intermediate correlation matrix cmat.star, and pre-specified marginal distributions.

Usage

genBinOrdNN(n, plist, mean.vec, var.vec, skew.vec, kurto.vec, no.bin, no.ord, 
no.NN, cmat.star)

Arguments

n	Number of rows.
plist	A list of probability vectors corresponding to each binary/ordinal variable. The i-th element of plist is a vector of the cumulative probabilities defining the marginal distribution of the i-th component of the multivariate variables, which is binary/ordinal. If the i-th variable is binary, the i-th vector of plist will contain 1 probability value. If the i-th variable is ordinal with k categories (k > 2), the i-th vector of plist will contain (k-1) probability values. The k-th element is implicitly 1.
mean.vec	Mean vector for continuous variables.
var.vec	Variance vector for continuous variables
skew.vec	The skewness vector for continuous variables.
kurto.vec	The kurtosis vector for continuous variables.
no.bin	Number of binary variables.
no.ord	Number of ordinal variables.
no.NN	Number of continuous variables.
cmat.star	The intermediate correlation matrix obtained from cmat.star.BinOrdNN function.

Value

A matrix of size n*(no.bin + no.ord + no.NN), of which the first no.bin columns are binary variables, the next no.ord columns are ordinal variables, and the last no.NN columns are continuous variables.

References

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.

Demirtas, H. and Yavuz Y. (2015). Concurrent generation of ordinal and normal data. Journal of Biopharmaceutical Statistics, 25(4), 635-650.

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

See Also

cmat.star.BinOrdNN, Fleishman.coef.NN

Examples

## Not run:
set.seed(54321)
no.bin <- 1
no.ord <- 1
no.NN <- 4
q <- no.bin + no.ord + no.NN

marginal <- list(0.4, cumsum(c(0.4, 0.2, 0.3)))

skewness.vec <- c(2,0,-0.4677,0.6325)
kurtosis.vec <- c(6,-0.5455,-0.3750,0.6)

corr.mat <- matrix(c(1.0,-0.3,-0.3,-0.3,-0.3,-0.3,
                    -0.3, 1.0,-0.3,-0.3,-0.3,-0.3,
                    -0.3,-0.3, 1.0, 0.4, 0.5, 0.6,
                    -0.3,-0.3, 0.4, 1.0, 0.7, 0.8,
                    -0.3,-0.3, 0.5, 0.7, 1.0, 0.9,
                    -0.3,-0.3, 0.6, 0.8, 0.9, 1.0),
                    q,byrow=TRUE)

corr.mat.star <- cmat.star.BinOrdNN(plist=marginal, skew.vec=skewness.vec, 
kurto.vec=kurtosis.vec, no.bin=1, no.ord=1, no.NN=4, CorrMat=corr.mat)

sim.data <- genBinOrdNN(n=100000, plist=marginal, mean.vec=c(2,3,4,5), 
var.vec=c(3,5,10,20), skew.vec=skewness.vec, kurto.vec=kurtosis.vec,
no.bin=1, no.ord=1, no.NN=4, cmat.star=corr.mat.star) 

## End(Not run)

BinOrdNonNor documentation built on March 22, 2021, 9:07 a.m.