fpca.pred: Predicted trajectories
In fpca: Restricted MLE for Functional Principal Components Analysis

Description Usage Arguments Details Value Author(s) References See Also

View source: R/fpca_pred.r

A function to predict trajectory for each subject

1	fpca.pred(fpcs, muhat,eigenfuncs)

`fpcs`	Functional principal component scores. An estimate is returned by fpca.score
`muhat`	Mean curve evaluated on a grid. An estimate is returned by fpca.mle.
`eigenfuncs`	Eigenfunctions evaluated on the same grids as in 'muhat'. An estimate is returned by fpca.mle.

'fpca.pred' gives predicted trajectories (evaluated on a fine grid).

A matrix where each column corresponds to the predicted trajectory of a subject.

J. Peng, D. Paul

Peng, J. and Paul, D. (2009). A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data. Journal of Computational and Graphical Statistics. December 1, 2009, 18(4): 995-1015

James, G. M., Hastie, T. J. and Sugar, C. A. (2000) Principal component models for sparse functional data. Biometrika, 87, 587-602.

Yao, F., Mueller, H.-G. and Wang, J.-L. (2005) Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association 100, 577-590

fpca.mle for model fitting, fpca.score for fpc scores

fpca documentation built on May 1, 2019, 10:26 p.m.