norm_ord: Calculate Correlations of Ordinal Variables Obtained from...

Description Usage Arguments Value References See Also

View source: R/norm_ord.R


This function calculates the correlation of ordinal variables (or variables treated as "ordinal"), with given marginal distributions, obtained from discretizing standard normal variables with a specified correlation matrix. The function modifies Barbiero & Ferrari's contord function in GenOrd-package. It uses pmvnorm function from the mvtnorm package to calculate multivariate normal cumulative probabilities defined by the normal quantiles obtained at marginal and the supplied correlation matrix Sigma. This function is used within ord_norm and would not ordinarily be called by the user.


norm_ord(marginal = list(), Sigma = NULL, support = list(),
  Spearman = FALSE)



a list of length equal to the number of variables; the i-th element is a vector of the cumulative probabilities defining the marginal distribution of the i-th variable; if the variable can take r values, the vector will contain r - 1 probabilities (the r-th is assumed to be 1)


the correlation matrix of the multivariate standard normal variable


a list of length equal to the number of variables; the i-th element is a vector of containing the r ordered support values; if not provided (i.e. support = list()), the default is for the i-th element to be the vector 1, ..., r


if TRUE, Spearman's correlations are used (and support is not required); if FALSE (default) Pearson's correlations are used


the correlation matrix of the ordinal variables


Please see references in ord_norm.

Alan Genz, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch, Fabian Scheipl, Torsten Hothorn (2018). mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-8.

Alan Genz, Frank Bretz (2009), Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195., Springer-Verlag, Heidelberg. ISBN 978-3-642-01688-2.

See Also


SimCorrMix documentation built on May 2, 2019, 1:24 p.m.