# overall.corr.mat: Computes the final correlation matrix In BinNonNor: Data Generation with Binary and Continuous Non-Normal Components

## 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

 ```1 2``` ```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.

`fleishman.coef`, `Tetra.Corr.BB`, `Int.Corr.NN`, `Biserial.Corr.BN`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37``` ```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) ```