Compute (penalized) principal components for functional data. fdata2ppc is deprecated.
fdata2pc(fdataobj, ncomp = 2,norm = TRUE,lambda=0,P=c(0,0,1),...)
fdata2ppc(fdataobj, ncomp = 2,norm = TRUE,lambda=0,P=c(0,0,1),...)fdata class object.
Number of principal comoponents.
=TRUE the norm of eigenvectors (rotation) is 1.
Amount of penalization. Default value is 0, i.e. no penalization is used.
If P is a vector: coefficients to define the penalty matrix object. By default P=c(0,0,1) penalize the second derivative (curvature) or acceleration. If P is a matrix: the penalty matrix object.
Further arguments passed to or from other methods.
The standard deviations of the functional principal components.
are also known as loadings. A fdata class object whose rows contain the eigenvectors.
are also known as scores. The value of the rotated functional data is returned.
The centered fdataobj object.
The functional mean of fdataobj object.
Vector of index of principal components.
The matched call.
Amount of penalization.
Penalty matrix.
Smoothing is achieved by penalizing the integral of the square of the derivative of order m over rangeval:
m = 0 penalizes the squared difference from 0 of the function
m = 1 penalize the square of the slope or velocity
m = 2 penalize the squared acceleration
m = 3 penalize the squared rate of change of acceleration
Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S. Springer-Verlag.
N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems, 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
# NOT RUN {
# }
# NOT RUN {
n= 100;tt= seq(0,1,len=51)
x0<-rproc2fdata(n,tt,sigma="wiener")
x1<-rproc2fdata(n,tt,sigma=0.1)
x<-x0*3+x1
pc=fdata2ppc(x,lambda=1)
summary(pc)
# }
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