Get and plot the estimated smoothing function values
getF(object, which, n=100, newdata, interval=c("NONE", "MCMC",
"RW"), addConst=TRUE, varying=1, level=0.9, sims=1000)
plotF(object, which, n=100, interval="RW", addConst=TRUE,
trans=I, level=0.9, sims=1000, auto.layout=TRUE, rug=TRUE,
legendPos="topright", ...)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).
a fitted cpglmm object.
(optional) an integer vector or a character vector of names giving the smooths for which fitted values are desired. Defaults to all.
if no newdata is given, fitted values for a regular grid with n values in the range of the respective covariates are returned
An optional data frame in which to look for variables with which to predict
what mehod should be used to compute pointwise confidence/HPD intervals: RW= bias-adjusted empirical bayes
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
value of thevarying-covariate (see tp) to be used if no newdata is supplied.
Defaults to 1.
level for the confidence/HPD intervals
how many iterates should be generated for the MCMC-based HPD-intervals
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)
automagically set plot layout via par()$mfrow
add rug-plots of the observed covariate locations
a (vector of) keyword(s) where to put labels of by-variables (see legend). "none" if you don't want a legend.
arguments passed on to the low-level plot functions (plot, matlines), legend, and title
Fabian Scheipl fabian.scheipl@googlemail.com
See the vignette for examples