BinNonNor (version 1.5.3)

overall.corr.mat: Computes the final correlation matrix

Description

This function computes the final correlation matrix by combining tetrachoric correlation for binary-binary combinations, biserial correlations for binary-continuous combinations, and intermediate correlation matrix for continuous-continuous combinations.

Usage

overall.corr.mat(n.BB, n.NN, prop.vec = NULL, corr.vec = NULL, corr.mat = NULL, 
coef.mat = NULL)

Arguments

n.BB

Number of binary variables.

n.NN

Number of continuous non-normal variables.

prop.vec

Probability vector for binary variables.

corr.vec

Vector of elements below the diagonal of correlation matrix ordered columnwise.

corr.mat

Specified correlation matrix.

coef.mat

Matrix of coefficients produced from fleishman.coef.

Value

A matrix of size (n.BB+n.NN)*(n.BB+n.NN).

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.

See Also

fleishman.coef, Tetra.Corr.BB, Int.Corr.NN, Biserial.Corr.BN

Examples

# NOT RUN {
n.BB=2
n.NN=4
prop.vec=c(0.4,0.7)
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)

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)

final.corr.mat=overall.corr.mat(n.BB,n.NN,prop.vec,corr.vec=NULL,corr.mat,
coef.mat)

corr.mat.BB=corr.mat[1:2,1:2]
final.corr.mat=overall.corr.mat(n.BB,n.NN=0,prop.vec,corr.vec=NULL,
corr.mat=corr.mat.BB,coef.mat=NULL)

corr.mat.NN=corr.mat[3:6,3:6]
final.corr.mat=overall.corr.mat(n.BB=0,n.NN,prop.vec=NULL,corr.vec=NULL, 
corr.mat=corr.mat.NN,coef.mat)


n.BB=1
n.NN=1
prop.vec=0.6
corr.vec=NULL
corr.mat=matrix(c(1,-0.3,-0.3,1),2,2)
coef.mat=matrix(c(-0.31375,0.82632,0.31375,0.02271),4,1)
final.corr.mat=overall.corr.mat(n.BB,n.NN,prop.vec,corr.vec=NULL,corr.mat,
coef.mat)
# }