rda: Redundancy analysis

In mulSEM: Some Multivariate Analyses using Structural Equation Modeling

Redundancy analysis

Description

This function conducts redundancy analysis using the OpenMx package. Missing data are handled with the full information maximum likelihood method when raw data are available. It provides standard errors for the standardized estimates.

Usage

rda(
  X_vars,
  Y_vars,
  data = NULL,
  Cov = NULL,
  numObs = NULL,
  extraTries = 50,
  ...
)

Arguments

X_vars	A vector of characters of the X variables.
Y_vars	A vector of characters of the Y variables.
data	A data frame containing raw data. If NULL, Cov and numObs must be provided.
Cov	A covariance or correlation matrix. Required when data is NULL.
numObs	A sample size. Required when data is NULL.
extraTries	This function calls OpenMx::mxTryHard() to obtain parameter estimates and their standard errors. extraTries is the number of extra runs. If extraTries=0, OpenMx::mxRun() is called.
...	Additional arguments passed to either OpenMx::mxTryHard() or OpenMx::mxRun().

Value

A list with class RDA. It stores the model in OpenMx objects. The fitted object is stored in mx.fit.

Author(s)

Mike W.-L. Cheung [e-mail]

References

Gu, F., Yung, Y.-F., Cheung, M. W.-L., Joo, B.-K., & Nimon, K. (2023). Statistical inference in redundancy analysis: A direct covariance structure modeling approach. Multivariate Behavioral Research, 58(5), 877-893. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2022.2141675")}

See Also

Chittum19, sas_ex2

Examples

## Redundancy Analysis
rda(X_vars=c("x1", "x2", "x3", "x4"),
    Y_vars=c("y1", "y2", "y3"),
    data=sas_ex2)

mulSEM documentation built on Feb. 17, 2026, 1:07 a.m.