CreatePathPlot: Create the fitted sample path plot based on the results from...

In fdapace: Functional Data Analysis and Empirical Dynamics

Create the fitted sample path plot based on the results from FPCA().

Description

Create the fitted sample path plot based on the results from FPCA().

Usage

CreatePathPlot(
  fpcaObj,
  subset,
  K = NULL,
  inputData = fpcaObj[["inputData"]],
  showObs = !is.null(inputData),
  obsOnly = FALSE,
  showMean = FALSE,
  derOptns = list(p = 0),
  ...
)

Arguments

fpcaObj	Returned object from FPCA().
subset	A vector of indices or a logical vector for subsetting the observations.
K	The number of components to reconstruct the fitted sample paths.
inputData	A list of length 2 containing the sparse/dense (unsupported yet) observations. inputData needs to contain two fields: Lt for a list of time points and Ly for a list of observations. Default to the 'inputData' field within 'fpcaObj'.
showObs	Whether to plot the original observations for each subject.
obsOnly	Whether to show only the original curves.
showMean	Whether to plot the mean function as a bold solid curve.
derOptns	A list of options to control derivation parameters; see ‘fitted.FPCA’. (default = NULL)
...	other arguments passed into matplot for plotting options

Examples

set.seed(1)
n <- 20
pts <- seq(0, 1, by=0.05)
sampWiener <- Wiener(n, pts)
sampWiener <- Sparsify(sampWiener, pts, 10)
res <- FPCA(sampWiener$Ly, sampWiener$Lt, 
            list(dataType='Sparse', error=FALSE, kernel='epan',
            verbose=TRUE))
CreatePathPlot(res, subset=1:5)

# CreatePathPlot has a lot of usages:

CreatePathPlot(res)
CreatePathPlot(res, 1:20)
CreatePathPlot(res, 1:20, showObs=FALSE)
CreatePathPlot(res, 1:20, showMean=TRUE, showObs=FALSE)
CreatePathPlot(res, 1:20, obsOnly=TRUE)
CreatePathPlot(res, 1:20, obsOnly=TRUE, showObs=FALSE)
CreatePathPlot(inputData=sampWiener, subset=1:20, obsOnly=TRUE)

fdapace documentation built on July 3, 2024, 5:08 p.m.