Farewell’s Linear Increments Model for Missing Data: The FLIM package

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.

Rune Hoff , Jon Michael Gran , Daniel Farewell
2015-01-09

CRAN packages used

zoo

CRAN Task Views implied by cited packages

Econometrics, Environmetrics, Finance, TimeSeries

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Hoff, et al., "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 = {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}
}