Plot Functional Canonical Correlation Weight Functions
A canonical correlation analysis produces a series of pairs of functional
data objects which, when used as weighting functions, successively maximize
the corresponding canonical correlation between two functional data objects.
Like functional principal component weight functions, successive weight
within either side fo the pair are required to be orthogonal to all previous
weight functions. Consequently, each successive canonical correlation will
no larger than its predecessor, and more likely substantially smaller.
This function plots an object of class cca.fd that results from the
use of function cca.fd. Each pair of weight functions is plotted
after a left mouse click indicating that you are ready for the next plot.
## S3 method for class 'cca.fd' plot(x, cexval = 1, ...)
x |
an object of class |
cexval |
A number used to determine label sizes in the plots. |
... |
other arguments for 'plot'. |
Produces a plot of a pair of weight functions corresponding to each canonical correlation between two functional data objects.
invisible(NULL)
# Canonical correlation analysis of knee-hip curves gaittime <- (1:20)/21 gaitrange <- c(0,1) gaitbasis <- create.fourier.basis(gaitrange,21) lambda <- 10^(-11.5) harmaccelLfd <- vec2Lfd(c(0, 0, (2*pi)^2, 0)) gaitfdPar <- fdPar(gaitbasis, harmaccelLfd, lambda) gaitfd <- smooth.basis(gaittime, gait, gaitfdPar)$fd ccafdPar <- fdPar(gaitfd, harmaccelLfd, 1e-8) ccafd0 <- cca.fd(gaitfd[,1], gaitfd[,2], ncan=3, ccafdPar, ccafdPar) # display the canonical correlations round(ccafd0$ccacorr[1:6],3) # plot the unrotated canonical weight functions plot.cca.fd(ccafd0) # compute a VARIMAX rotation of the canonical variables ccafd1 <- varmx.cca.fd(ccafd0) # plot the rotated canonical weight functions plot.cca.fd(ccafd1)
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