fitted.FPCAder

Combines the zero-meaned fitted values and the mean derivative to get the fitted values for the derivative trajectories.
Estimates are given on the work-grid, not on the observation grid. Use ConvertSupport to map the 
estimates to your desired domain.

A versatile package that provides implementation of various
methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
package is Functional Principal Component Analysis (FPCA), a key technique for
functional data analysis, for sparsely or densely sampled random trajectories
and time courses, via the Principal Analysis by Conditional Estimation
(PACE) algorithm. This core algorithm yields covariance and mean functions,
eigenfunctions and principal component (scores), for both functional data and
derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
For sparse designs, it provides fitted continuous trajectories with confidence bands,
even for subjects with very few longitudinal observations. PACE is a viable and
flexible alternative to random effects modeling of longitudinal data. There is also a
Matlab version (PACE) that contains some methods not available on fdapace and vice
versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626.
Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry).
References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Yidong Zhou

fdapace

Functional Data Analysis and Empirical Dynamics

Han Chen

Su I Iao

Poorbita Kundu

Hang Zhou

Satarupa Bhattacharjee

Cody Carroll

Yaqing Chen

Xiongtao Dai

Jianing Fan

Alvaro Gajardo

Pantelis Z. Hadjipantelis

Kyunghee Han

Hao Ji

Changbo Zhu

Paromita Dubey

Shu-Chin Lin

Hans-Georg Müller

Jane-Ling Wang

fitted.FPCAder function

<dl><dt>object</dt>
<dd>A object of class FPCA returned by the function FPCA().</dd>
<dt>K</dt>
<dd>The integer number of the first K components used for the representation. (default: length(derObj$lambda ))</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Fitted functional data for derivatives from the FPCAder object — fitted.FPCAder

<dl>

<dt>object</dt>
<dd>A object of class FPCA returned by the function FPCA().</dd>


<dt>K</dt>
<dd>The integer number of the first K components used for the representation. (default: length(derObj$lambda ))</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

Fitted functional data for derivatives from the FPCAder object

fitted.FPCAder: Fitted functional data for derivatives from the FPCAder object

Description

Usage

Value

Arguments

References

Examples