BinOrdNonNor (version 1.5.2)

validate.target.cormat.BinOrdNN: Checks the validity of the target correlation matrix

Description

The function checks the validity of pairwise correlations. In addition, it checks positive definiteness, symmetry, and correct dimensions.

Usage

validate.target.cormat.BinOrdNN(plist, skew.vec, kurto.vec, no.bin, no.ord, 
no.NN, CorrMat)

Arguments

plist

A list of probability vectors corresponding to each binary/ordinal variable. The i-th element of plist is a vector of the cumulative probabilities defining the marginal distribution of the i-th component of the multivariate variables, which is binary/ordinal. If the i-th variable is binary, the i-th vector of plist will contain 1 probability value. If the i-th variable is ordinal with k categories (k > 2), the i-th vector of plist will contain (k-1) probability values. The k-th element is implicitly 1.

skew.vec

The skewness vector for continuous variables.

kurto.vec

The kurtosis vector for continuous variables.

no.bin

Number of binary variables.

no.ord

Number of ordinal variables.

no.NN

Number of continuous variables.

CorrMat

The target correlation matrix which must be positive definite and within the valid limits.

Value

In addition to being positive definite and symmetric, the values of pairwise correlations in the target correlation matrix must also fall within the limits imposed by the marginal distributions of the variables. The function ensures that the supplied correlation matrix is valid for simulation. If a violation occurs, an error message is displayed that identifies the violation. The function returns a logical value TRUE when no such violation occurs.

See Also

valid.limits.BinOrdNN

Examples

# NOT RUN {
Sigma <- diag(4)
Sigma[lower.tri(Sigma)] <- c(0.42, 0.55, 0.29, 0.37, 0.14, 0.26)
Sigma <- Sigma + t(Sigma)
diag(Sigma) <- 1

marginal <- list(0.2, c(0.4, 0.7, 0.9))

validate.target.cormat.BinOrdNN(plist=marginal, skew.vec=c(1,2), kurto.vec=c(2,7), 
                                no.bin=1, no.ord=1, no.NN=2, CorrMat=Sigma)
# }