varmx.pca.fd: Rotation of Functional Principal Components with VARIMAX...

In fda: Functional Data Analysis

Rotation of Functional Principal Components with VARIMAX Criterion

Description

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.

Usage

varmx.pca.fd(pcafd, nharm=scoresd[2], nx=501)

Arguments

pcafd	an object of class pca.fd that is produced by function pca.fd.
nharm	the number of harmonics or principal components to be rotated.
nx	the number of argument values in a fine mesh used to define the harmonics to be rotated.

Value

a rotated principal components analysis object of class pca.fd.

References

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.

See Also

varmx, varmx.cca.fd

fda documentation built on Sept. 30, 2024, 9:19 a.m.