TY - JOUR
T1 - brolgar
T2 - an R package to BRowse Over Longitudinal Data Graphically and Analytically in R
AU - Tierney, Nicholas
AU - Cook, Dianne
AU - Prvan, Tania
N1 - Funding Information:
We would like to thank Stuart Lee, Mitchell O’Hara Wild, Earo Wang, and Miles McBain for their discussion on the design of brolgar. We would also like to thank Rob Hyndman, Monash University and ACEMS for their support of this research
Publisher Copyright:
© 2022, R Journal.All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a “spaghetti plot”, where a line plot is drawn for each individual. When overlaid in one plot, it can have the appearance of a bowl of spaghetti. With even a small number of subjects, these plots are too overloaded to be read easily. The interesting aspects of individual differences are lost in the noise. Longitudinal data is often modelled with a hierarchical linear model to capture the overall trends, and variation among individuals, while accounting for various levels of dependence. However, these models can be difficult to fit, and can miss unusual individual patterns. Better visual tools can help to diagnose longitudinal models, and better capture the individual experiences.
AB - Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a “spaghetti plot”, where a line plot is drawn for each individual. When overlaid in one plot, it can have the appearance of a bowl of spaghetti. With even a small number of subjects, these plots are too overloaded to be read easily. The interesting aspects of individual differences are lost in the noise. Longitudinal data is often modelled with a hierarchical linear model to capture the overall trends, and variation among individuals, while accounting for various levels of dependence. However, these models can be difficult to fit, and can miss unusual individual patterns. Better visual tools can help to diagnose longitudinal models, and better capture the individual experiences.
UR - http://www.scopus.com/inward/record.url?scp=85150776034&partnerID=8YFLogxK
U2 - 10.32614/RJ-2022-023
DO - 10.32614/RJ-2022-023
M3 - Article
AN - SCOPUS:85150776034
SN - 2073-4859
VL - 14
SP - 6
EP - 25
JO - The R Journal
JF - The R Journal
IS - 2
ER -