cca.ct {ctmva}

Continuous-time canonical correlation analysis

Description

A continuous-time version of canonical correlation analysis (CCA).

Usage

cca.ct(fdobj1, fdobj2)

Arguments

fdobj1, fdobj2

a pair of continuous-time multivariate data sets, of class "fd"

Value

A list consisting of

vex1, vex2

matrices defining the canonical variates. The first columns of each give the coefficients defining the first pair of canonical variates; and so on.

cor

canonical correlations, i.e., correlations between the pairs of canonical variates

Note

Columns of the output matrix vex2 are flipped as needed to ensure positive correlations.

Author(s)

Biplab Paul <[e-mail]> and Philip Tzvi Reiss <[e-mail]>

See Also

cancor, for classical CCA

Examples

## Not run: 

# CCA relating Canadian daily temperature and precipitation data
require(fda)
data(CanadianWeather)
daybasis <- create.bspline.basis(c(0,365), nbasis=80)
tempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"], daybasis)$fd
precfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"log10precip"], daybasis)$fd
tpcor <- cca.ct(tempfd, precfd)
par(mfrow=1:2)
barplot(tpcor$vex1[,1], horiz=TRUE, las=1, main="Temperature",
            sub="First canonical coefficients vector")
barplot(tpcor$vex2[,1], horiz=TRUE, las=1, main="Log precipitation",
            sub="First canonical coefficients vector")


## End(Not run)