Principal components are often easier to interpret if they are rotated. Among the many possible ways in which this rotation can be defined, the VARIMAX criterion seems to give satisfactory results most of the time.
varmx.pca.fd(pcafd, nharm=scoresd[2], nx=501)a rotated principal components analysis object of class pca.fd.
an object of class pca.fd that is produced by function
pca.fd.
the number of harmonics or principal components to be rotated.
the number of argument values in a fine mesh used to define the harmonics to be rotated.
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
varmx,
varmx.cca.fd