Returns fitted values for a `funeigen`

object.

1 2 |

`object` |
A |

`type` |
A character string, one of the following: |

`...` |
Other optional arguments which may be passed from other methods but ignored by this one. |

A `funeigen`

object represents a principal component analysis
of irregular longitudinal data, following the method used by Goldsmith et al. (2011).

A matrix or vector containing the appropriate fitted values. What is
returned depends on the `type`

parameter. `functions`

gives the fitted
values of the smooth latent x(t) functions at a grid of time points.
`eigenfunctions`

gives the estimated eigenfunctions at each time point.
`loadings`

gives the loading of each subject on each estimated eigenfunction.
`mean`

gives the mean value for the smooth latent x(t) functions.
`centered`

gives the centered x(t) functions (the estimated function
subtracting the mean function) . `covariance`

gives the estimated
covariance matrix of x(s) and x(t) on a grid of time points s and t.
`noise.variance`

gives the estimated measurement error variance on
the x(t) functions. `midpoints`

gives the time points for the grid, on
which `functions`

, `mean`

, `centered`

, and `covariance`

are defined; they are viewed as midpoints of bins of observation times (see
Goldsmith et al., 2011).

Goldsmith, J., Bobb, J., Crainiceanu, C. M., Caffo, B., and Reich, D. (2011). Penalized functional regression. Journal of Computational and Graphical Statistics, 20(4), 830-851. DOI: 10.1198/jcgs.2010.10007.

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