Create the fitted sample path plot based on the results from FPCA().
Create the fitted sample path plot based on the results from FPCA().
CreatePathPlot( fpcaObj, subset, K = NULL, inputData = fpcaObj[["inputData"]], showObs = !is.null(inputData), obsOnly = FALSE, showMean = FALSE, derOptns = list(p = 0), ... )
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. |
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 |
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.