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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