Performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time'' is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. The development of this package was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health.
|Author||John Dziak [aut, cre], Mariya Shiyko [aut]|
|Date of publication||2016-08-24 18:38:32|
|Maintainer||John Dziak <email@example.com>|
|License||GPL (>= 2)|
coef.funreg: coef method for funreg object
fitted.funeigen: fitted method for funeigen object
fitted.funreg: fitted method for funreg object
funeigen: Perform eigenfunction decomposition on functional covariate
funreg: Perform penalized functional regression
funreg.permutation: Do a permutation test for functional regression
generate.data.for.demonstration: Generate data for some demonstration examples
make.funreg.basis: Make basis for functional regression (for internal use by...
marginal.cor: Calculate marginal correlations with response
marginal.cor.funeigen: Calculate marginal correlations with response, from a...
num.functional.covs.in.model: Count the functional covariates in a model (for internal use...
plot.funeigen: plot method for funeigen object
plot.funreg: plot method for funreg object
print.funreg: print method for funreg object
redo.funreg: Redo a funreg with different data (for internal use by...
SampleFunregData: Sample dataset for funreg
summary.funreg: summary method for funreg object