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

Determines effects of varying each of the given variables while all others are held constant. This function is mainly used to produce plots of residuals versus explanatory variables, also showing component effects.

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`object` |
a model fit, result of a fitting function |

`data` |
data frame in which the variables are found |

`vars` |
character vector of names of variables for which
components are required. Only variables that appear in |

`se` |
if TRUE, standard errors will be returned |

`xm` |
named vector of values of the fixed (central) point from
which the individual variables are varied in turn. |

`xfromdata` |
if TRUE, the components effects will be evaluated for
the data values in |

`noexpand` |
vector determining which variables should not be “filled in”, probably because they are used like factors. Either a character vector of variable names or a vector of logical or numerical values with names, in which case the names corresponding to positive values will be identified. |

`nxcomp` |
number of points used for each (quantitative) variable
if |

The component effect is defined as the curve of fitted values obtained by varying the explanatory variable, keeping all the other variables at their "central value" (the mean of continuous variables and the mode of factors).

A list consisting of

`comp` |
component effects |

`x` |
the values of the x variables for which the effects have been calculated |

`xm` |
the values of the x variables that are held fixed while one of the variables is varied |

`se` |
standard errors of the component effects, if required by the
argument |

Werner A. Stahel, ETH Zurich

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