fpca.score: Functional principal component scores

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

View source: R/fpca_score.r

Description

A function to estimate the functional principal component scores by the best linear unbiased predictors (Yao et al. 2005).

Usage

1
fpca.score(data.m,grids.u,muhat,eigenvals,eigenfuncs,sig2hat,K)

Arguments

data.m

Matrix with three columns. Each row corresponds to one measurement for one subject. Column One: subject ID (numeric or string); Column 2: measurement (numeric); Column 3: corresponding measurement time (numeric); Missing values are not allowed. Same format as the data input for fpca.mle.

grids.u

Grid of time points used in evaluating the mean and eigenfunctions (on the original scale). Same as 'grid' returned by fpca.mle.

muhat

Mean evaluated on the same grids as in grids.u. An estimate is returned by fpca.mle.

eigenvals

Eigenvalues. An estimate is returned by fpca.mle.

eigenfuncs

Eigenfunctions evaluated on the same grids as in grids.u. An estimate is returned by fpca.mle.

sig2hat

Noise variance. An estimate is returned by fpca.mle.

K

Number of eigenfunctions used to derive the fpc scores.

Details

'fpca.score' uses best linear unbiased predictors (BLUP) to estimate the functional principal component scores for each subject

Value

An n by K matrix containing the first K functional principal component scores for each subject.

Author(s)

J. Peng, D. Paul

References

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

See Also

fpca.mle for model fitting, fpca.pred for prediction


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

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