univDecomp {MFPCA} | R Documentation |
Univariate basis decomposition
Description
This function calculates a univariate basis decomposition for a (univariate) functional data object.
Usage
univDecomp(type, funDataObject, ...)
Arguments
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. |
Details
Functional data X_i(t)
can often be approximated by a linear
combination of basis functions b_k(t)
X_i(t) = \sum_{k =
1}^K \theta_{ik} b_k(t), i = 1, \ldots, 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), \ldots,
X_n(t)
. The coefficients (or scores) \theta_{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.
Value
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 |
Warning
The options type = "DCT2D"
and type =
"DCT3D"
have not been tested with ATLAS/MKL/OpenBLAS.
See Also
MFPCA
, univExpansion
,
fpcaBasis
, splineBasis1D
,
splineBasis1Dpen
, splineBasis2D
,
splineBasis2Dpen
, umpcaBasis
,
fcptpaBasis
, fdaBasis
,
dctBasis2D
, dctBasis3D
Examples
# 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)