pda.overlay function

Stability Analysis for Principle Differential Analysis

Overlays the results of a univariate, second-order principal differential analysis on a bifurcation diagram to demonstrate stability.

pda.overlay(pdaList,nfine=501,ncoarse=11,...)

Arguments

• pdaList: a list object returned by pda.fd.
• nfine: number of plotting points to use.
• ncoarse: number of time markers to place along the plotted curve.
• ...: other arguments for 'plot'.

Details

Overlays a bivariate plot of the functional parameters in a univariate second-order principal differential analysis on a bifurcation diagram.

Returns

None.

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

pda.fd

plot.pda.fd

eigen.pda

Examples

oldpar <- par(no.readonly=TRUE)
# This example looks at a principal differential analysis of the lip data
# in Ramsay and Silverman (2005).

# First smooth the data

lipfd <- smooth.basisPar(liptime, lip, 6, Lfdobj=int2Lfd(4),
                         lambda=1e-12)$fd
names(lipfd$fdnames) <- c("time(seconds)", "replications", "mm")

# Now we'll set up functional parameter objects for the beta coefficients.

lipbasis <- lipfd$basis
lipfd0   <- fd(matrix(0,lipbasis$nbasis,1),lipbasis)
lipfdPar <- fdPar(lipfd0,2,0)
bwtlist  <- list(lipfdPar,lipfdPar)
xfdlist  <- list(lipfd)

# Call pda

pdaList <- pda.fd(xfdlist, bwtlist)

# And plot the overlay

pda.overlay(pdaList,lwd=2,cex.lab=1.5,cex.axis=1.5)
par(oldpar)