# NOT RUN {
# set up a Fourier basis for smoothing temperature data
daybasis65 <- create.fourier.basis(c(0, 365), 65,
axes=list("axesIntervals"))
# smooth the average temperature data using function Data2fd
Daytempfd <- with(CanadianWeather, smooth.basis(day.5,
dailyAv[,,"Temperature.C"], daybasis65)$fd )
daytempfd <- with(CanadianWeather, smooth.basis(day.5,
dailyAv[,,"Temperature.C"],
daybasis65, fdnames=list("Day", "Station", "Deg C"))$fd )
# Plot the temperature data along with the fit to the data for the first
# station, St. John's Newfoundland
# If you want only the fitted functions, use plot(daytempfd)
# To plot only a single fit vs. observations, use argument index
# to request which one you want.
with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C",
drop=FALSE], argvals= day.5, daytempfd, index=1, titles=place) )
# Default ylab = daytempfd[['fdnames']]
with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C"],
argvals= day.5, Daytempfd, index=1, titles=place) )
plot(daytempfd)
# }
# NOT RUN {
# plot all the weather stations, one by one after a click on the plot
# in response to a request.
# This example is within the "dontrun" environment to prevent the
# the R package checking process from pausing: 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) )
# }
# NOT RUN {
# Now plot results for two weather stations.
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$dailyAv[,,"Temperature.C"])
# Force the two plots to have the same scale
with(CanadianWeather, plotfit.fd(dailyAv[,,"Temperature.C"], day.5,
daytempfd, index=2, titles=place, ylim=ylim) )
with(CanadianWeather, plotfit.fd(dailyAv[,,"Temperature.C"], day.5,
daytempfd, index=35, titles=place, ylim=ylim) )
# }
# NOT RUN {
# Plot residuals with interactive display of stations one by one
par(op)
with(CanadianWeather, plotfit.fd(dailyAv[, , "Temperature.C"],
day.5, daytempfd, residual=TRUE) )
# }
# NOT RUN {
# The gait data are bivariate, and this code illustrates how plotfit.fd
# deals with the plotting of two variables at the same time
# First define normalized times and their range
gaittime <- as.numeric(dimnames(gait)[[1]])*20
gaitrange <- c(0,20)
# Define the harmonic acceleration differential operator
harmaccelLfd <- vec2Lfd(c(0, (2*pi/20)^2, 0), rangeval=gaitrange)
# Set up basis for representing gait data.
gaitbasis <- create.fourier.basis(gaitrange, nbasis=21)
# Smooth the data
gaitfd <- smooth.basisPar(gaittime, gait,
gaitbasis, Lfdobj=harmaccelLfd, lambda=1e-2)$fd
# Assign names to the data
names(gaitfd$fdnames) <- c("Normalized time", "Child", "Angle")
gaitfd$fdnames[[3]] <- c("Hip", "Knee")
# }
# NOT RUN {
# plot each pair of curves interactively, two plots per page, the top
# for hip angle, and the bottom for knee angle
plotfit.fd(gait, gaittime, gaitfd)
# Plot the residuals, sorting cases by residual sum of squares summed over
# both hip and knee angles.
# The first series of 39 plots are for hip angle, two plots per page,
# and the second 39 are for knee angle. The plots are sorted by the
# size of the total residual sum of squares, but RMS residual values
# for specific angles are not all going to be in order.
plotfit.fd(gait, gaittime, gaitfd, residual=TRUE, sort=TRUE)
# }
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