correlation.bound.check: Checks if the pairwise correlation among variables are within the feasible range

Description

This function checks if there are range violations among correlation of Poisson-Poisson, Poisson-binary, Poisson-continuous, binary-binary, binary-continuous, and continuous-continuous combinations.

Usage

correlation.bound.check(n.P, n.B, n.C, lambda.vec = NULL, prop.vec = NULL, 
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.

lambda.vec

Rate vector for Poisson variables.

prop.vec

Proportion vector for binary 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

The function returns TRUE if no specification problem is encountered. Otherwise, it returns an error message.

References

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.

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.

Examples

Run this code

# NOT RUN {
n.P<-1
n.B<-1
n.C<-1
lambda.vec<-c(1)
prop.vec<-c(0.3)
coef.mat<-matrix(c(-0.3137491,0.8263239,0.3137491,0.0227066),4,1,byrow=F) 
corr.mat=matrix(c(1,0.2,0.1,0.2,1,0.5,0.1,0.5,1),3,3)
correlation.bound.check(n.P,n.B,n.C,lambda.vec,prop.vec,coef.mat,corr.vec=NULL, 
corr.mat)

n.P<-2
n.B<-2
n.C<-2
lambda.vec<-c(1,2)
prop.vec<-c(0.3,0.5)
coef.mat<-matrix(c(
-0.3137491,  0.0000000,   
 0.8263239,  1.0857433,    
 0.3137491,  0.0000000,
 0.0227066, -0.0294495),4,2,byrow=F)  
corr.mat=matrix(0.8,6,6)
diag(corr.mat)=1
correlation.bound.check(n.P,n.B,n.C,lambda.vec,prop.vec,coef.mat,corr.vec=NULL, 
corr.mat)
# }