cancorr {mulSEM}

Canonical Correlation Analysis

Description

It conducts a canonical correlation 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 on the estimates.

Usage

cancorr(X_vars, Y_vars, data=NULL, Cov, numObs,
        model=c("CORR-W", "CORR-L", "COV-W", "COV-L"),
        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 of raw data.

Cov

A covariance or correlation matrix if data is not available.

numObs

A sample size if data is not available.

model

Four models defined in Gu, Yung, and Cheung (2019). CORR and COV refer to the analysis of correlation structure and covariance structure, respectively.

extraTries

This function calls mxTryHard to obtain the parameter estimates and their standard errors. extraTries indicates the number of extra runs. If extraTries=0, mxRun is called.

...

Additional arguments sent to either mxTryHard or mxRun.

Value

A list of output with class CanCor. It stores the model in OpenMx objects. The fitted object is in the slot of mx.fit.

Note

cancorr expects that there are equal or more number of variables in Y_vars. If there are fewer variables in Y_vars, you may swap between X_vars and Y_vars.

Author(s)

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

References

Gu, F., Yung, Y.-F., & Cheung, M. W.-L. (2019). Four covariance structure models for canonical correlation analysis: A COSAN modeling approach. Multivariate Behavioral Research, 54(2), 192-223. https://doi.org/10.1080/00273171.2018.1512847

See Also

Thorndike00, sas_ex1