Description Usage Arguments Details Value Author(s) References See Also
A function to estimate the functional principal component scores by the best linear unbiased predictors (Yao et al. 2005).
1 | fpca.score(data.m,grids.u,muhat,eigenvals,eigenfuncs,sig2hat,K)
|
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. |
'fpca.score' uses best linear unbiased predictors (BLUP) to estimate the functional principal component scores for each subject
An n by K matrix containing the first K functional principal component scores for each 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.pred for prediction
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