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

In PoisBinOrd: Data Generation with Poisson, Binary and Ordinal Components

Description

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

Usage

correlation.bound.check(n.P, n.B, n.O, lambda.vec = NULL, prop.vec = NULL, 
prop.list = NULL, corr.vec = NULL, corr.mat = NULL)

Arguments

n.P	Number of Poisson variables.
n.B	Number of binary variables.
n.O	Number of ordinal variables.
lambda.vec	Rate vector for Poisson variables.
prop.vec	Probability vector for binary variables.
prop.list	A list of probability vectors for ordinal variables.
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.

See Also

validation.corr, correlation.limits

Examples

## Not run: 
n.P<-1
n.B<-1
n.O<-1
lambda.vec<-c(1)
prop.vec<-c(0.3)
prop.list<-list(c(0.3,0.6))
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.O,lambda.vec,prop.vec,prop.list,corr.vec=NULL,
corr.mat)

n.P<-2
n.B<-2
n.O<-2
lambda.vec<-c(1,2)
prop.vec<-c(0.3,0.5)
prop.list<-list(c(0.3,0.6),c(0.5,0.6))
corr.mat=matrix(0.8,6,6)
diag(corr.mat)=1
correlation.bound.check(n.P,n.B,n.O,lambda.vec,prop.vec,prop.list,corr.vec=NULL,
corr.mat)

## End(Not run)

PoisBinOrd documentation built on March 22, 2021, 9:06 a.m.