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

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

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.

Examples

Run this code

# NOT RUN {
## 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)
# }