Get and plot the smoothing function values

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Description

Get and plot the estimated smoothing function values

Usage

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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 fabian.scheipl@googlemail.com

See Also

See the vignette for examples