Description Objects from the class Slots Details Author(s) References Examples
Hyperparameters for functional principal component analysis. See fpca
.
Objects can be created by calls of the form
new("fpcCtrl")
. In addition, named lists can be coerced to
fpcCtrl
objects, names are completed if unique.
Objects of class fpcCtrl
have the following slots:
select
:Character string, one of "automatic"
or
"manual"
specifying whether the bandwidth for smoothing is
given or should be calculated based on the data.
h1Dim
:If select
="manual"
, bandwidth for
smoothing the mean.
h2Dim
:If select
="manual"
, bandwidth for
smoothing the covariance.
sm1Dim
:Character string, name of the mean smoothing function. One of "sm.regression"
or "sm1"
.
sm2Dim
:Character string, name of the covariance smoothing function. One of "sm.regression"
or "sm2"
.
coeffsCalc
:Character string, specifying how to
calculate the coefficients. One of "estimate"
or "integrate"
.
nrMaxTime
:Maximum number of evaluation time points for the covariance matrix.
average
:If TRUE, matrix calculation is used and speeds up the calculation if curves have many evaluation time points in common.
"sm.regression"
is a nonparametric regression estimate from
the R-package sm. "sm1"
and "sm2"
are kernel
smoothers defined by Chiou2007.
The coefficients for the basis functions can be computed by
numerical integration (coeffCalc
="integrate"
) or by a
sparse estimation defined by Mueller2005 (coeffCalc
="estimate"
).
Christina Yassouridis
Chiou J-M and Li P-L. "Functional clustering and identifying substructures of longitudinal data". Journal of the Royal Statistical Society: Series B. 69 (4). pp. 679–699. 2007
F. Yao and H-G. Mueller and J-L. Wang. Functional Data Analysis for Sparse Longitudinal Data. Journal of the American Statistical Association. 100 (470). 577–590. 2005
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