Get and plot the estimated smoothing function values

1 2 3 4 5 6 7 |

`object` |
a fitted |

`which` |
(optional) an integer vector or a character vector of names giving the smooths for which fitted values are desired. Defaults to all. |

`n` |
if no |

`newdata` |
An optional data frame in which to look for variables with which to predict |

`interval` |
what mehod should be used to compute pointwise confidence/HPD intervals: RW= bias-adjusted empirical bayes |

`addConst` |
boolean should the global intercept and intercepts for the levels of the by-variable be included in the fitted values (and their CIs) can also be a vector of the same length as |

`varying` |
value of the |

`level` |
level for the confidence/HPD intervals |

`sims` |
how many iterates should be generated for the MCMC-based HPD-intervals |

`trans` |
a function that should be applied to the fitted values and ci's before plotting (e.g. the inverse link function to get plots on the scale of the reponse) |

`auto.layout` |
automagically set plot layout via |

`rug` |
add |

`legendPos` |
a (vector of) keyword(s) where to put labels of by-variables (see |

`...` |
arguments passed on to the low-level plot functions ( |

a list with one `data.frame`

for each function, giving `newdata`

or the values of the generated grid plus the fitted values (and confidence/HPD intervals).

These are from the `amer`

package that has retired from CRAN. The formula used for the pointwise bias-adjusted CIs is taken from Ruppert and Wand's 'Semiparametric Regression' (2003), p. 140.
These leave out the uncertainty associated with the variance component estimates.

Fabian Scheipl fabian.scheipl@googlemail.com

See the vignette for examples

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.