Missing data is common in longitudinal studies. We present a package for Farewell’s Linear Increments Model for Missing Data (the FLIM package), which can be used to fit linear models for observed increments of longitudinal processes and impute missing data. The method is valid for data with regular observation patterns. The end result is a list of fitted models and a hypothetical complete dataset corresponding to the data we might have observed had individuals not been missing. The FLIM package may also be applied to longitudinal studies for causal analysis, by considering counterfactual data as missing data for instance to compare the effect of different treatments when only data from observational studies are available. The aim of this article is to give an introduction to the FLIM package and to demonstrate how the package can be applied.
Econometrics, Environmetrics, Finance, TimeSeries
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For attribution, please cite this work as
Hoff, et al., "The R Journal: Farewell's Linear Increments Model for Missing Data: The FLIM package", The R Journal, 2015
BibTeX citation
@article{RJ-2014-030,
author = {Hoff, Rune and Gran, Jon Michael and Farewell, Daniel},
title = {The R Journal: Farewell's Linear Increments Model for Missing Data: The FLIM package},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2014-030},
doi = {10.32614/RJ-2014-030},
volume = {6},
issue = {2},
issn = {2073-4859},
pages = {137-150}
}