getF: Get and plot the smoothing function values

In cplm: Compound Poisson Linear Models

Get and plot the smoothing function values

Description

Get and plot the estimated smoothing function values

Usage

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", ...)
    

Arguments

object	a fitted cpglmm object.
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
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
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 par()$mfrow
rug	add rug-plots of the observed covariate locations
legendPos	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

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).

Note

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.

Author(s)

Fabian Scheipl [e-mail]

See Also

See the vignette for examples

cplm documentation built on Sept. 21, 2024, 9:06 a.m.