# Moderated regression with residual centering

### Description

Fit moderated linear regression with both residual centering and mean centering methods.

### Usage

1 2 3 |

### Arguments

`formula` |
an object of class "formula": a symbolic description of the model to be fitted. |

`data` |
a data frame |

`centered` |
variables wich must be centered |

`residual_centering` |
"FALSE" generate a moderated using standard lm regression, "TRUE" generate a moderated regression with residuals centering |

`...` |

### Details

Moderated regression without residual centering : For any interaction term, the product is computed and entered in the final model. In order to perform a mean centered moderated regression, predictors must be centered Moderated regression with residual centering: For any interaction term with order n, a regression with low order terms (n-1) is computed, and Interaction residuals are entered in the final model.

### Value

lmres returns an object of class "lmres".

An object of class "lmres" is a list containing at least the following components:

`regr.order` |
the numeric order of the fitted linear model |

`formula.StepI` |
the formula of the first order regression |

`formula.StepII` |
(only where relevant) the formula of the second order regression |

`formula.Stepfin` |
the formula of the x (max(x)=3) order regression |

`beta.StepI` |
a named vector of standardized coefficients for the first order regression |

`beta.StepII` |
(only where relevant) a named vector of standardized coefficients for the second order regression |

`beta.Stepfin` |
a named vector of standardized coefficients for the x (max(x)=3) order regression |

`StepI` |
a lm object for the first order regression |

`StepII` |
(only where relevant) a lm object for the second order regression |

`Stepfin` |
a lm object for the x (max(x)=3) order regression |

`F_change` |
is a list containing F change statistics |

### Author(s)

Alberto Mirisola and Luciano Seta

### References

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions Among Latent Variables. Structural Equation Modeling, 13(4), 497-519.

Cohen, J., Cohen, P.,West, S. G.,&Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

### See Also

“summary.lmres”

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## moderated regression with mean centering
library(car)
data(Ginzberg)
model1<-lmres(adjdep~adjsimp*adjfatal, centered=c("adjsimp", "adjfatal"),
data=Ginzberg)
## moderated regression with mean centering
library(car)
data(Ginzberg)
model1<-lmres(adjdep~adjsimp*adjfatal, centered=c("adjsimp", "adjfatal"),
data=Ginzberg)
## moderated regression with mean centering
model2<-lmres(adjdep~adjsimp*adjfatal,residual_centering=TRUE,
centered=c("adjsimp", "adjfatal"), data=Ginzberg)
## three way interaction with mean centering
library(car)
data(Highway1)
model3<-lmres(rate~len*trks*sigs1, centered=c("len","trks","sigs1"),data=Highway1)
## The function is currently defined as
function (formula, data, centered, ...)
UseMethod("lmres")
``` |

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