# fitted.funeigen: fitted method for funeigen object In funreg: Functional Regression for Irregularly Timed Data

## Description

Returns fitted values for a `funeigen` object.

## Usage

 ```1 2``` ```## S3 method for class 'funeigen' fitted(object, type = "functions", ...) ```

## Arguments

 `object` A `funeigen` object. `type` A character string, one of the following: `functions`, `eigenfunctions`, `loadings`, `eigenvalues`, `mean`, `centered`, `covariance`, `noise.variance`, `midpoints`. These are the constructs for which fitted values can be returned. `...` 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.

funreg documentation built on Oct. 4, 2021, 5:07 p.m.