Canonical correlation analysis
Canonical correlation analysis
DESCRIPTION:
Carry out a functional canonical correlation analysis with
regularization or roughness penalties on the estimated
canonical variables.
USAGE:
cca.fd(fd, ncan=2, lambda=0.00025, Lfd=2)
REQUIRED ARGUMENTS:
- fd
-
A bivariate functional data object. That is, it is assumed that there
are two functions for each replication. The coefficient matrix for this
object will be three-dimensional, with the final dimension being of
length 2.
OPTIONAL ARGUMENTS:
- ncan
-
Number of pairs of canonical variates to be found.
- lambda
-
Smoothing or regularization parameter.
If lambda is a 2-vector then the first component
will be applied to the "x" and the second to the "y" functions.
- Lfd
-
The order of derivative to be penalized if it is a scalar, or the
linear differential operator whose value is to be penalized if it is a
functional data object.
VALUE:
An object of class cca.fd with these entries is returned.
- "weight functions"
A functional data object for the "ncan" pairs of canonical variate weight
functions. Each replicate corresponds to a pair of weight functions.
- "correlations"
The complete set of canonical correlations. The number is equal to the
number of basis functions.
- "variates"
An array of the values taken by the canonical variates, that is,
the scores on the variates. This is a 3-way array with
first dimension corresponding to replicates, second to the
different variates (dimension NCAN) and third (dimension 2) to
the "x" and "y" scores.
REFERENCES:
Ramsay, J. O. and Silverman, B. W. (1997) Functional Data
Analysis. New York: Springer. Canonical correlation analysis is
described in Chapter 12.
SEE ALSO:
plot.cca.fd, cca.2fun.fd
EXAMPLES: