Description Usage Arguments Details Value Warning See Also Examples
This function calculates a univariate basis decomposition for a (univariate) functional data object.
1  univDecomp(type, funDataObject, ...)

type 
A character string, specifying the basis for which the decomposition is to be calculated. 
funDataObject 
A 
... 
Further parameters, passed to the function for the particular basis to use. 
Functional data X_i(t) can often be approximated by a linear combination of basis functions b_k(t)
X_i(t) = ∑_{k = 1}^K θ_{ik} b_k(t), i = 1, …, N.
The basis functions may be prespecified (such as spline basis functions or Fourier bases) or can be estimated from the data (e.g. by functional principal component analysis) and are the same for all observations X_1(t), …, X_n(t). The coefficients (or scores) θ_{ik} reflect the weight of each basis function b_k(t) for the observed function X_i(t) and can be used to characterize the individual observations.
scores 
A matrix of scores (coefficients) for each observation based on the prespecified basis functions. 
B 
A
matrix containing the scalar products of the basis functions. Can be

ortho 
Logical. If 
functions 
A functional data object, representing
the basis functions. Can be 
The options type = "DCT2D"
and type =
"DCT3D"
have not been tested with ATLAS/MKL/OpenBLAS.
MFPCA
, univExpansion
,
fpcaBasis
, splineBasis1D
,
splineBasis1Dpen
, splineBasis2D
,
splineBasis2Dpen
, umpcaBasis
,
fcptpaBasis
, fdaBasis
,
dctBasis2D
, dctBasis3D
1 2 3 4 5 6 7 8 9 10 11  # generate some data
dat < simFunData(argvals = seq(0,1,0.01), M = 5,
eFunType = "Poly", eValType = "linear", N = 100)$simData
# decompose the data in univariate functional principal components...
decFPCA < univDecomp(type = "uFPCA", funDataObject = dat, npc = 5)
str(decFPCA)
# or in splines (penalized)
decSplines < univDecomp(type = "splines1Dpen", funDataObject = dat) # use mgcv's default params
str(decSplines)

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