getF {amer}R Documentation

get the estimated function values from an amer-Fit...

Description

get the estimated function values from an amer-Fit

Usage

getF(object, which, n=100, newdata, interval=c("NONE", "MCMC",
    "RW"), addConst=TRUE, varying=1, level=0.9, sims=1000)

Arguments

object a fitted additive (mixed) model of class amer-class
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 is given, fitted values for a regular grid with n values in the range of the respective covariates are returned
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, MCMC uses mcmcsamp
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 which
varying value of thevarying-covariate (see tp) to be used if no newdata is supplied. Defaults to 1.
level level for the confidence/HPD intervals
sims how many iterates should be generated for the MCMC-based HPD-intervals

Value

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) if MCMC-intervals were rquested, the listhas an attribute "mcmc" containing the result of the call to mcmcsamp, a merMCMC-class object.

Note

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. MCMC-intervals based on results from mcmcsamp don't seem to be very reliable yet and should be used with caution, especially for more complex models.

Author(s)

Fabian Scheipl

See Also

plotF, tests/optionsTests.r and the vignette for examples


[Package amer version 0.5 Index]