fpca: Functional Principal Component Analysis
In RFgroove: Importance Measure and Selection for Groups of Variables with Random Forests

Description Usage Arguments Details Value Author(s) References See Also Examples

Compute the functional PCA from a set of curves.

1 2	fpca( x, nbasisInit, propVar = 0.9, reconstruct = FALSE, varName = NULL, verbose = FALSE)

`x`	The set of curves.
`nbasisInit`	The number of initial spline coefficients.
`propVar`	The proportion of explained variance.
`reconstruct`	Should the data be reconstruct after dimension reduction ?
`varName`	The name of the current functional variable.
`verbose`	Should the details be printed.

The Functional PCA is performed in two steps. First we express each discretized curves as a linear combination of ‘nbasisInit’ spline basis functions. Then a multivariate PCA is computed on the spline coefficients. The final number of principal components is chosen such that the proportion of explained variance is larger than ‘propVar’.

A list with two components:

`design`	The matrix of the principal components ;
`smoothData`	The reconstructed data if ‘reconstruct == TRUE’.

Baptiste Gregorutti

Ramsay, J. O., and Silverman, B. W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

hardThresholding

  data(toyRegFD)
  x <- toyRegFD$FDlist[[1]]
  PCs <- fpca(x=x, nbasisInit=32, propVar=.9, reconstruct=TRUE)
  plot(x[1,])
  lines(PCs$smoothData[1,], col=2)

Loading required package: randomForest
randomForest 4.6-14
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Loading required package: wmtsa
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

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