This function calculates a functional principal component basis
representation for functional data on one-dimensional domains. The FPCA is
calculated via the PACE
function, which is built on
fpca.sc
in the refund package.
fpcaBasis(
funDataObject,
nbasis = 10,
pve = 0.99,
npc = NULL,
makePD = FALSE,
cov.weight.type = "none"
)
A matrix of scores (coefficients) with dimension
N x K
, reflecting the weights for each principal component in each
observation, where N
is the number of observations in
funDataObject
and K
is the number of functional principal
components.
Logical, set to TRUE
, as basis functions
are orthonormal.
A functional data object, representing the functional principal component basis functions.
The smoothed mean function.
An object of class funData
containing the observed functional data samples and for which the FPCA is
to be calculated.
An integer, representing the number of B-spline basis
functions used for estimation of the mean function and bivariate smoothing
of the covariance surface. Defaults to 10
(cf.
fpca.sc
in refund).
A numeric value between 0 and 1, the proportion of variance
explained: used to choose the number of principal components. Defaults to
0.99
(cf. fpca.sc
in refund).
An integer, giving a prespecified value for the number of
principal components. Defaults to NULL
. If given, this overrides
pve
(cf. fpca.sc
in refund).
Logical: should positive definiteness be enforced for the
covariance surface estimate? Defaults to FALSE
(cf.
fpca.sc
in refund).
The type of weighting used for the smooth covariance
estimate in PACE
. Defaults to "none"
, i.e. no weighting. Alternatively,
"counts"
(corresponds to fpca.sc
in refund) weights the pointwise estimates of the covariance function
by the number of observation points.
univDecomp
, PACE