fda (version 2.2.5)

plotfit: Plot a Functional Data Object With Data

Description

Plot either functional data observations 'x' with a fit 'fdobj' or residuals from the fit.

This function is useful for assessing how well a functional data object fits the actual discrete data.

The default is to make one plot per functional observation with fit if residual is FALSE and superimposed lines if residual==TRUE.

With multiple plots, the system waits to confirm a desire to move to the next page unless ask==FALSE.

Usage

plotfit.fd(y, argvals, fdobj, rng = NULL, index = NULL,
      nfine = 101, residual = FALSE, sortwrd = FALSE, titles=NULL,
      ylim=NULL, ask=TRUE, type=c("p", "l")[1+residual],
      xlab=NULL, ylab=NULL, sub=NULL, col=1:9, lty=1:9, lwd=1,
      cex.pch=1, axes=NULL, ...)plotfit.fdSmooth(y, argvals, fdSm, rng = NULL, index = NULL,
      nfine = 101, residual = FALSE, sortwrd = FALSE, titles=NULL,
      ylim=NULL, ask=TRUE, type=c("p", "l")[1+residual],
      xlab=NULL, ylab=NULL, sub=NULL, col=1:9, lty=1:9, lwd=1,
      cex.pch=1, axes=NULL, ...)

Arguments

y

a vector, matrix or array containing the discrete observations used to estimate the functional data object.

argvals

a vector containing the argument values corresponding to the first dimension of y.

fdobj

a functional data object estimated from the data.

fdSm

an object of class fdSmooth

rng

a vector of length 2 specifying the limits for the horizontal axis. This must be a subset of fdobj[['basis']][['rangeval']], which is the default.

index

a set of indices of functions if only a subset of the observations are to be plotted. Subsetting can also be achieved by subsetting y; see details, below.

nfine

the number of argument values used to define the plot of the functional data object. This may need to be increased if the functions have a great deal of fine detail.

residual

a logical variable: if TRUE, the residuals are plotted rather than the data and functional data object.

sortwrd

a logical variable: if TRUE, the observations (i.e., second dimension of y) are sorted for plotting by the size of the sum of squared residuals.

titles

a vector containing strings that are titles for each observation.

ylim

a numeric vector of length 2 giving the y axis limits; see 'par'.

ask

If TRUE and if 'y' has more levels than the max length of col, lty, lwd and cex.pch, the user must confirm page change before the next plot will be created.

type

type of plot desired, as described with plot. If residual == FALSE, 'type' controls the representation for 'x', which will typically be 'p' to plot points but not lines; 'fdobj' will always plot

xlab

x axis label.

ylab

Character vector of y axis labels. If(residual), ylab defaults to 'Residuals', else to varnames derived from names(fdnames[[3]] or fdnames[[3]] or dimnames(y)[[3]].

sub

subtitle under the x axis label. Defaults to the RMS residual from the smooth.

col, lty, lwd, cex.pch

Numeric or character vectors specifying the color (col), line type (lty), line width (lwd) and size of plotted character symbols (cex.pch) of the data representation on the plot.

If ask==TRUE, the length of the longest of these determines the

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 do.call(x$axes[[1]],

Value

• A matrix of mean square deviations from predicted is returned invisibly. If fdobj[["coefs"]] is a 3-dimensional array, this is a matrix of dimensions equal to the last two dimensions of fdobj[["coefs"]]. This will typically be the case when x is also a 3-dimensional array with the last two dimensions matching those of fdobj[["coefs"]]. The second dimension is typically replications and the third different variables.

  If x and fobj[["coefs"]] are vectors or 2-dimensional arrays, they are padded to three dimensions, and then MSE is computed as a matrix with the second dimension = 1; if x and fobj[["coefs"]] are vectors, the first dimension of the returned matrix will also be 1.

item

...

code

plot

Side Effects

a plot of the the data 'x' with the function or the deviations between observed and predicted, depending on whether residual is FALSE or TRUE.

Details

plotfit plots discrete data along with a functional data object for fitting the data. It is designed to be used after something like data2fd, smooth.fd, smooth.basis or smoothe.basisPar to check the fit of the data offered by the fd object.

plotfit.fdSmooth calls plotfit for its 'fd' component.

The plot can be restricted to a subset of observations (i.e., second dimension of y) or variables (i.e., third dimension of y) by providing y with the dimnames for its second and third dimensions matching a subset of the dimnames of fdobj[['coef']] (for plotfit.fd or fdSm[['fdobj']][['coef']] for plotfit.fdSmooth). If only one observation or variable is to be plotted, y must include 'drop = TRUE', as, e.g., y[, 2, 3, drop=TRUE]. If y or fdobj[['coef']] does not have dimnames on its second or third dimension, subsetting is acheived by taking the first few colums so the second or third dimensions match. This is acheived using checkDims3(y, fdobj[['coef']], defaultNames = fdobj[['fdnames']]]).

See Also

plot, plot.fd, lines.fd plot.fdSmooth, lines.fdSmooth par data2fd smooth.fd smooth.basis smooth.basisPar checkDims3

Examples

daybasis65 <- create.fourier.basis(c(0, 365), 65,
                    axes=list("axesIntervals"))

daytempfd <- with(CanadianWeather, data2fd(
       dailyAv[,,"Temperature.C"], day.5,
       daybasis65, argnames=list("Day", "Station", "Deg C")) )

with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C"],
     argvals= day.5, daytempfd, index=1, titles=place) )
# Default ylab = daytempfd[['fdnames']]

with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C",
     drop=FALSE], argvals= day.5, daytempfd, index=1, titles=place) )
# Better:  ylab = dimnames(y)[[3]]

# The following pauses to request page changes.
# (Without 'dontrun', the package build process
# might encounter problems with the par(ask=TRUE)
# feature.)
with(CanadianWeather, plotfit.fd(
          dailyAv[,, "Temperature.C"], day.5,
          daytempfd, ask=TRUE) )

# If you want only the fitted functions, use plot(daytempfd)

# To plot only a single fit vs. observations, use index
# to request which one you want.

op <- par(mfrow=c(2,1), xpd=NA, bty="n")
# xpd=NA:  clip lines to the device region,
#       not the plot or figure region
# bty="n":  Do not draw boxes around the plots.
ylim <- range(CanadianWeather$dailyAv[,,"Temperature.C"])
# Force the two plots to have the same scale
with(CanadianWeather, plotfit.fd(dailyAv[,,"Temperature.C"], day.5,
          daytempfd, index=2, titles=place, ylim=ylim) )

with(CanadianWeather, plotfit.fd(dailyAv[,,"Temperature.C"], day.5,
          daytempfd, index=35, titles=place, ylim=ylim) )
par(op)

# plot residuals
with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C"],
          day.5, daytempfd, residual=TRUE) )
# Can't read this, so try with 2 lines per page with ask=TRUE,
# and limiting length(col), length(lty), etc. <=2
with(CanadianWeather, plotfit.fd(
          dailyAv[,,"Temperature.C"], day.5,
          daytempfd, residual=TRUE, col=1:2, lty=1, ask=TRUE) )