MakeFPCAInputs

Turn vector inputs to the list so they can be used in FPCA

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

MakeFPCAInputs function

<dl><dt>IDs</dt>
<dd>np-by-1 vector of subject IDs (Default: NULL)</dd>
<dt>tVec</dt>
<dd>Either an np-by-1 vector of measurement times, or a p-by-1 vector corresponding to the common time support</dd>
<dt>yVec</dt>
<dd>n-by-1 vector of measurements from the variable of interest, or a n-by-p matrix with each row corresponding to the dense observations.</dd>
<dt>na.rm</dt>
<dd>logical indicating if NA should be omitted (Default: FALSE)</dd>
<dt>sort</dt>
<dd>logical indicating if Lt (and the correspoding Ly values) should be ensured to be sorted (Default: FALSE)</dd>
<dt>deduplicate</dt>
<dd>logical indicating if the Lt should be ensured not to have within-subject duplicated values; the Ly values of repeated Lt values are averaged (Default: FALSE)</dd></dl>

Arguments

Format FPCA input — MakeFPCAInputs

<dl>

<dt>IDs</dt>
<dd>np-by-1 vector of subject IDs (Default: NULL)</dd>


<dt>tVec</dt>
<dd>Either an np-by-1 vector of measurement times, or a p-by-1 vector corresponding to the common time support</dd>


<dt>yVec</dt>
<dd>n-by-1 vector of measurements from the variable of interest, or a n-by-p matrix with each row corresponding to the dense observations.</dd>


<dt>na.rm</dt>
<dd>logical indicating if NA should be omitted (Default: FALSE)</dd>


<dt>sort</dt>
<dd>logical indicating if Lt (and the correspoding Ly values) should be ensured to be sorted (Default: FALSE)</dd>


<dt>deduplicate</dt>
<dd>logical indicating if the Lt should be ensured not to have within-subject duplicated values; the Ly values of repeated Lt values are averaged (Default: FALSE)</dd>

</dl>

Format FPCA input

MakeFPCAInputs: Format FPCA input

Description

Usage

Value

Arguments