R: Get and plot the smoothing function values

getF {cplm}

R Documentation

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 the`varying`-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 fabian.scheipl@googlemail.com