smooth.fd {fda} | R Documentation |
This function is intended to apply a roughness penalized smooth to
data already set up as a functional data object. For example, data
may have been converted to a functional data object using function
data2fd
using a fairly large set of basis functions, and
subsequently it was desired to smooth the functional data object
that resulted.
smooth.fd(fdobj, fdParobj)
fdobj |
a functional data object to be smoothed. |
fdParobj |
a functional parameter object. This object is defined by a
roughness penalty in slot Lfd and a smoothing
parameter lambda in slot lambda , and this
information is used to further smooth argument fdobj .
|
a functional data object.
# Shows the effects of two levels of smoothing # where the size of the third derivative is penalized. # The null space contains quadratic functions. x <- seq(-1,1,0.02) y <- x + 3*exp(-6*x^2) + rnorm(rep(1,101))*0.2 # set up a saturated B-spline basis basisobj <- create.bspline.basis(c(-1,1),81) # convert to a functional data object that interpolates the data. result <- smooth.basis(x, y, basisobj) yfd <- result$fd # set up a functional parameter object with smoothing # parameter 1e-6 and a penalty on the 3rd derivative. # FIXME: using 3rd derivative here gave error yfdPar <- fdPar(yfd, 2, 1e-6) yfd1 <- smooth.fd(yfd, yfdPar) # set up a functional parameter object with smoothing # parameter 1 and a penalty on the 3rd derivative. yfdPar <- fdPar(yfd, 2, 1) yfd2 <- smooth.fd(yfd, yfdPar) # plot the data and smooth plot(x,y) # plot the data lines(yfd1, lty=1) # add moderately penalized smooth lines(yfd2, lty=3) # add heavily penalized smooth legend(-1,3,c("0.000001","1"),lty=c(1,3)) # plot the data and smoothing using function plotfit.fd plotfit.fd(y, x, yfd1) # plot data and smooth