ds.hetcor: Heterogeneous Correlation Matrix

View source: R/ds.hetcor.R

ds.hetcorR Documentation

Heterogeneous Correlation Matrix


This function is based on the hetcor function from the R package polycor.


  data = NULL,
  ML = TRUE,
  std.err = TRUE,
  bins = 4,
  pd = TRUE,
  use = "complete.obs",
  datasources = NULL



the name of a data frame consisting of factors, ordered factors, logical variables, character variables, and/or numeric variables, or the first of several variables.


if TRUE, compute maximum-likelihood estimates; if FALSE (default), compute quick two-step estimates.


if TRUE (default), compute standard errors.


number of bins to use for continuous variables in testing bivariate normality; the default is 4.


if TRUE (default) and if the correlation matrix is not positive-definite, an attempt will be made to adjust it to a positive-definite matrix, using the nearPD function in the Matrix package. Note that default arguments to nearPD are used (except corr=TRUE); for more control call nearPD directly.


if "complete.obs", remove observations with any missing data; if "pairwise.complete.obs", compute each correlation using all observations with valid data for that pair of variables.


a list of DSConnection-class objects obtained after login. If the datasources argument is not specified the default set of connections will be used: see datashield.connections_default.


Computes a heterogenous correlation matrix, consisting of Pearson product-moment correlations between numeric variables, polyserial correlations between numeric and ordinal variables, and polychoric correlations between ordinal variables.


Returns an object of class "hetcor" from each study, with the following components: the correlation matrix; the type of each correlation: "Pearson", "Polychoric", or "Polyserial"; the standard errors of the correlations, if requested; the number (or numbers) of observations on which the correlations are based; p-values for tests of bivariate normality for each pair of variables; the method by which any missing data were handled: "complete.obs" or "pairwise.complete.obs"; TRUE for ML estimates, FALSE for two-step estimates.


Demetris Avraam for DataSHIELD Development Team

datashield/dsBaseClient documentation built on April 19, 2022, 5:25 a.m.