Description Usage Arguments Value Author(s) References See Also Examples

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.

1 | ```
SortLoadings(Px)
``` |

`Px` |
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/.

1 2 3 4 5 6 7 8 9 | ```
# Compute loading matrix of alexithymia dataset
data(alexithymia)
results <- pcovr(alexithymia$X,alexithymia$Y)
Px <- results$Px
print(Px)
# Sort loading matrix
sorted_Px <- SortLoadings(results$Px)
print(sorted_Px)
``` |

PCovR documentation built on June 20, 2017, 9:15 a.m.

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