validate.cor.mat: Validates the target correlation matrix

In PoisBinOrdNonNor: Generation of Up to Four Different Types of Variables

Description

This function validates the user-inputted target correlation matrix for the chosen number of Poisson, binary, ordinal, and continuous (via Fleishman polynomials) random variables. The correlation matrix follows the order of Poisson, binary, ordinal, continuous.

Usage

validate.cor.mat(cor.mat, no.pois = 0, no.bin = 0, no.ord = 0, 
    no.nonn = 0, pois.list = list(), bin.list = list(), 
    ord.list = list(), is.ord.list.cum=FALSE, nonn.list = list())

Arguments

cor.mat	The desired target correlation matrix.
no.pois	The number of Poisson random variables desired. Defaults to 0.
no.bin	The number of binary random variables desired. Defaults to 0.
no.ord	The number of ordinal random variables desired. Defaults to 0.
no.nonn	The number of continuous random variables desired, created using Fleishman polynomials. Defaults to 0.
pois.list	A list of the lambda values, which must be greater than 0. Length will be equal to no.pois, or an error will be thrown. Defaults to an empty list.
bin.list	A list of vectors containing the probabilities for each variable. Each vector should have 2 entries between 0 and 1 inclusive, and sum to 1. Length must be equal to no.bin. Defaults to an empty list.
ord.list	A list of vectors containing the probabilities for each variable. If is.ord.list.cum is TRUE, each vector should have entries between 0 and 1, in increasing order. Otherwise, each vector should have entries between 0 and 1 inclusive that sum to 1. Length must be equal to no.ord. Defaults to an empty list.
is.ord.list.cum	Flag for whether the ordinal list supplied contains cumulative probabilities. Defaults to FALSE.
nonn.list	A list of vectors containing the first four moments of each variable, in order. If only two parameters are supplied, they will be assumed to be skew and excess kurtosis, with mean = 0 and variance = 1. If only three parameters are supplied, they will be assumed to be variance, skew and excess kurtosis, with mean = 0. If less than two parameters or more than four parameters are supplied for any variable, an error will be raised. Variance must be positive, and excess kurtosis must be greater than or equal to skew^2-2. Length must be equal to no.nonn. Defaults to an empty list.

Details

First, the matrix is checked for the correct dimensions, then for symmetry, and then for being positive definite. Then each entry is checked to be in [-1, 1] and the diagonal entries must be 1. If not, an error is thrown.

Second, the parameters are transformed and then checked for validity using check.params.

Third, the minimum and maximum correlation matrices are found using lower.upper.cors. If all entries are within the bounds, TRUE is returned. Otherwise, a list of which cells are invalid and their required bounds are printed, and FALSE is returned.

Value

TRUE if the correlation matrix is valid, and FALSE if not.

Examples

validate.cor.mat(cor.mat = .2 * diag(3) + .8, no.pois = 3, 
    pois.list = list(.25, .5, 1))
validate.cor.mat(cor.mat = .7 * diag(3) + .3, no.ord = 3, 
    ord.list = list(c(.2, .8), c(.1, .2, .3, .4), c(.8, 0, .1, .1)))
validate.cor.mat(cor.mat = .25 * diag(3) + .75, no.pois = 1, 
    no.nonn = 1, no.ord = 1, pois.list = list(.5), 
    ord.list = list(c(.8, 0, .1, .1)), nonn.list=list(c(0, 1, 0, 1)))
validate.cor.mat(cor.mat = .35 * diag(3) + .65, no.pois = 1, 
    no.nonn = 1, no.ord = 1, pois.list = list(.5), 
    ord.list = list(c(.8, 0, .1, .1)), nonn.list=list(c(0, 1, 0, 1)))

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