intermediate.corr.CC: Computes an intermediate correlation matrix for continuous variables given the specified correlation matrix

Description

This function computes the intermediate correlation matrix for continuous-continuous combinations as formulated in Demirtas et al. (2012).

Usage

intermediate.corr.CC(n.P, n.B, n.C, coef.mat = NULL, corr.vec = NULL, corr.mat = NULL)

Arguments

n.P

Number of Poisson variables.

n.B

Number of binary variables.

n.C

Number of continuous variables.

coef.mat

Matrix of coefficients produced from fleishman.coef.

corr.vec

Vector of elements below the diagonal of correlation matrix ordered column-wise.

corr.mat

Specified correlation matrix.

Value

A correlation matrix of size n.C*n.C.

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.

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

Examples

Run this code

# NOT RUN {
n.P=2
n.C=4
coef.mat=matrix(c(
 -0.31375,  0.00000,  0.10045, -0.10448,
  0.82632,  1.08574,  1.10502,  0.98085,
  0.31375,  0.00000, -0.10045,  0.10448,
  0.02271, -0.02945, -0.04001,  0.00272),4,byrow=TRUE)
corr.vec = NULL
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),6,byrow=TRUE)

intmatCC=intermediate.corr.CC(n.P,n.B=0,n.C,coef.mat,corr.vec=NULL,corr.mat)
intmatCC
# }