fda: plot.fd – R documentation

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plot.fd

Plot a Functional Data Object

Description

Functional data observations, or a derivative of them, are plotted. These may be either plotted simultaneously, as matplot does for multivariate data, or one by one with a mouse click to move from one plot to another. The function also accepts the other plot specification arguments that the regular plot does. Calling plot with an fdSmooth or an fdPar object plots its fd component.

Usage

## S3 method for class 'fd'
  plot(x, y, Lfdobj=0, href=TRUE, titles=NULL,
                    xlim=NULL, ylim=NULL, xlab=NULL,
                    ylab=NULL, ask=FALSE, nx=NULL, axes=NULL, ...)
## S3 method for class 'fdPar'
plot(x, y, Lfdobj=0, href=TRUE, titles=NULL,
                    xlim=NULL, ylim=NULL, xlab=NULL,
                    ylab=NULL, ask=FALSE, nx=NULL, axes=NULL, ...)
## S3 method for class 'fdSmooth'
plot(x, y, Lfdobj=0, href=TRUE, titles=NULL,
                    xlim=NULL, ylim=NULL, xlab=NULL,
                    ylab=NULL, ask=FALSE, nx=NULL, axes=NULL, ...)

Arguments

`x`	functional data object(s) to be plotted.
`y`	sequence of points at which to evaluate the functions 'x' and plot on the horizontal axis. Defaults to seq(rangex[1], rangex[2], length = nx). NOTE: This will be the values on the horizontal axis, NOT the vertical axis.
`Lfdobj`	either a nonnegative integer or a linear differential operator object. If present, the derivative or the value of applying the operator is plotted rather than the functions themselves.
`href`	a logical variable: If `TRUE`, add a horizontal reference line at 0.
`titles`	a vector of strings for identifying curves
`xlab`	a label for the horizontal axis.
`ylab`	a label for the vertical axis.
`xlim`	a vector of length 2 containing axis limits for the horizontal axis.
`ylim`	a vector of length 2 containing axis limits for the vertical axis.
`ask`	a logical value: If `TRUE`, each curve is shown separately, and the plot advances with a mouse click
`nx`	the number of points to use to define the plot. The default is usually enough, but for a highly variable function more may be required.
`axes`	Either a logical or a list or `NULL`. logical whether axes should be drawn on the plot list a list used to create custom `axes` used to create axes via `x$axes[[1]]` and `x$axes[-1]`. The primary example of this uses `list("axesIntervals", ...)`, e.g., with `Fourier` bases to create `CanadianWeather` plots
`...`	additional plotting arguments that can be used with function `plot`

Details

Note that for multivariate data, a suitable array must first be defined using the par function.

Value

'done'

Side Effects

a plot of the functional observations

Examples

##
## plot.fd
##

daybasis65 <- create.fourier.basis(c(0, 365), 65,
                    axes=list("axesIntervals"))
harmaccelLfd <- vec2Lfd(c(0,(2*pi/365)^2,0), c(0, 365))

harmfdPar     <- fdPar(daybasis65, harmaccelLfd, lambda=1e5)

daytempfd <- with(CanadianWeather, smooth.basis(day.5,
        dailyAv[,,"Temperature.C"], daybasis65)$fd)

#  plot all the temperature functions for the monthly weather data
plot(daytempfd, main="Temperature Functions")

## Not run: 
# To plot one at a time:
# The following pauses to request page changes.
\dontshow{
# (Without 'dontrun', the package build process
# might encounter problems with the par(ask=TRUE)
# feature.)
}
plot(daytempfd, ask=TRUE)

## End(Not run)

##
## plot.fdSmooth
##
b3.4 <- create.bspline.basis(norder=3, breaks=c(0, .5, 1))
# 4 bases, order 3 = degree 2 =
# continuous, bounded, locally quadratic
fdPar3 <- fdPar(b3.4, lambda=1)



# Penalize excessive slope Lfdobj=1;
# (Can not smooth on second derivative Lfdobj=2 at it is discontinuous.)
fd3.4s0 <- smooth.basis(0:1, 0:1, fdPar3)

# using plot.fd directly
plot(fd3.4s0$fd)




##
## with Date and POSIXct argvals
##
# Date
invasion1 <- as.Date('1775-09-04')
invasion2 <- as.Date('1812-07-12')
earlyUS.Canada <- as.numeric(c(invasion1, invasion2))
BspInvasion    <- create.bspline.basis(earlyUS.Canada)

earlyUSyears <- seq(invasion1, invasion2, length.out=7)
earlyUScubic <- (as.numeric(earlyUSyears-invasion1)/365.24)^3
earlyUSyears <- as.numeric(earlyUSyears)
fitCubic     <- smooth.basis(earlyUSyears, earlyUScubic, BspInvasion)$fd
plot(fitCubic)

# POSIXct
AmRev.ct    <- as.POSIXct1970(c('1776-07-04', '1789-04-30'))
AmRevYrs.ct <- seq(AmRev.ct[1], AmRev.ct[2], length.out=14)
AmRevLin.ct <- as.numeric(AmRevYrs.ct-AmRev.ct[2])
AmRevYrs.ct <- as.numeric(AmRevYrs.ct)
BspRev.ct   <- create.bspline.basis(AmRev.ct)
fitLin.ct   <- smooth.basis(AmRevYrs.ct, AmRevLin.ct, BspRev.ct)$fd
plot(fitLin.ct)

fda

Functional Data Analysis

v5.1.9

GPL (>= 2)

Authors

J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]

Initial release

2020-12-16