fcr-package: Dynamic prediction in functional concurrent regression with...

In fcr: Functional Concurrent Regression for Sparse Data

Description

This package contains the functions for fitting dynamic functional concurrent regression with sparse data.

Notation

Let $y_ij$ denote some outcome measured at $t_ij$ on the functional domain (e.g. time) for subject $i$ at observation $j$. We focus on fitting models of the form

y_{ij} = f_0(t_{ij}) + f_1(t_{ij})X_{ij} + \cdots + b_i(t_{ij}) + ε_{ij}

Estimation

Estimation is performed using an iterative procedure described in Leroux et. al (2017). Initially, a model is fit without $b_i(t_ij)$. Using the residuals from this initial fit, the covariance function is estimated. The model is then re-fit using this covariance function. This procedure can be iterated as many times as desired.

References

Leroux A, Xiao L, Crainiceanu C, Checkley W (2017). Dynamic prediction in functional concurrent regression with an application to child growth.

fcr documentation built on May 1, 2019, 8:26 p.m.