mpcr {mulSEM}

Multivariate Principal Component Regression (MPCR)

Description

It conducts a multivariate principal component regression 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

mpcr(X_vars, Y_vars, data=NULL, Cov, Means=NULL, numObs, pca=c("COV", "COR"),
     pc_select=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 of raw data.

Cov

A covariance or correlation matrix if data is not available.

Means

An optional mean vector if data is not available.

numObs

A sample size if data is not available.

pca

Whether the principal component analysis is based unstandardized COV or standardized COR variables.

pc_select

PCs selected in the regression analysis. For example, pc_select=c(1,2) to use the first two PCs in the multiple regression analysis.

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 MPCR. It stores the model in OpenMx objects. The fitted object is in the slot of mx.fit.

Author(s)

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

References

Gu, F., & Cheung, M. W.-L. (2023). A Model-based approach to multivariate principal component regression: Selection of principal components and standard error estimates for unstandardized regression coefficients. British Journal of Mathematical and Statistical Psychology, 76(3), 605-622. https://doi.org/10.1111/bmsp.12301

See Also

Nimon21