A loading matrix indicates how predictors that have been reduced to components - e.g., in principal covariates regression (De Jong & Kiers, 1992) - relate to these components. Usually, components are interpreted by looking at what the predictors with a clear non-zero loading have in common. To make this easier, this function changes the order of the predictors presented in a loading matrix, so that the firstly, the predictors with clear non-zero loadings on the first component (with decreasing loadings) are presented, then the predictors with clear non-zero loadings on the second component, etc.
Dataframe that contains component loadings (components x predictors)
SortLoadings returns a dataframe with the same dimensions and labels as the original loading matrix, but with the columns (referring to the predictors) presented in a different order.
Marlies Vervloet ([email protected])
De Jong, S., & Kiers, H. A. (1992). Principal covariates regression: Part I. Theory. Chemometrics and Intelligent Laboratory Systems , 155-164.
Marlies Vervloet, Henk A. Kiers, Wim Van den Noortgate, Eva Ceulemans (2015). PCovR: An R Package for Principal Covariates Regression. Journal of Statistical Software, 65(8), 1-14. URL http://www.jstatsoft.org/v65/i08/.
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