validation.skewness.kurtosis: Validates the marginal specification of the continuous...

In BinNonNor: Data Generation with Binary and Continuous Non-Normal Components

Description

Checks whether the marginal specification of the continuous non-normal part is valid and consistent.

Usage

validation.skewness.kurtosis(n.NN, skewness.vec = NULL, kurtosis.vec = NULL)

Arguments

n.NN	Number of continuous non-normal variables.
skewness.vec	Skewness vector for continuous non-normal variables.
kurtosis.vec	Kurtosis vector for continuous non-normal variables.

Value

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

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.

Examples

n.NN<-3
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6,8)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

## Not run: 
n.NN<--1
skewness.vec=c(0)
kurtosis.vec=c(-1.2)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

n.NN<-3
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6,5)
validation.skewness.kurtosis(3)

n.NN<-3
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6,5)
validation.skewness.kurtosis(n.NN,skewness.vec)
validation.skewness.kurtosis(n.NN,kurtosis.vec)

n.NN<-0
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6,8)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

n.NN<-2
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6,8)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

n.NN<-2
skewness.vec=c(0,2,3)
kurtosis.vec=c(-1.2,6)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

skewness.vec=c(2,3)
kurtosis.vec=c(1,5)
validation.skewness.kurtosis(n.NN,skewness.vec,kurtosis.vec)

## End(Not run)

BinNonNor documentation built on March 22, 2021, 9:08 a.m.