# fitted method for funeigen object

### Description

Returns fitted values for a `funeigen`

object.

### Usage

1 2 |

### Arguments

`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. |

### Details

A `funeigen`

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

### Value

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).

### References

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.