falls_long: Falls Intervention Dataset
In gherardovarando/stagedtrees: Staged Event Trees
falls_long R Documentation
Falls Intervention Dataset
Description
Data simulated by Shenvi et al. (2019) describing
an intervention to reduce falls among
the elderly.
Usage
falls_long
Format
A data frame with 50000 observations of 6 binary variables.
Details
As described in Walley et al (2023):
"Under this intervention, a certain proportion of individuals
aged over 65 would be assessed and classified as low or high risk.
Those assessed to be at a high risk are referred to a falls clinic for an
advanced assessment. All those who are referred,
50% of other high risk individuals, and 10% of low risk individuals
go on to receive treatment. It is assumed that those who are not assessed
receive neither referral nor treatment."
Source
The original data have been downloaded from the cegpy
github repository and is also available
as supplementary materials of Walley et al (2023).
In the original data pairs of variables Housing-Assessment and
Referred-Treatment are encoded in variables HousingAssessment and Treatment
respectively. Function colsplit from package reshape2 was
used to split those variables.
References
Walley G., Shenvi A., Strong P., Kobalczyk K. (2023),
cegpy: Modelling with chain event graphs in Python,
Knowledge-Based Systems, Volume 274.
Shenvi, A., Smith, J.Q., Walton, R., Eldridge, S. (2019).
Modelling with Non-stratified Chain Event Graphs.
In: Argiento, R., Durante, D., Wade, S. (eds)
Bayesian Statistics and New Generations. BAYSM 2018.
Springer Proceedings in Mathematics & Statistics, vol 296.
gherardovarando/stagedtrees documentation built on July 5, 2025, 12:35 a.m.
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| falls_long | R Documentation |
Falls Intervention Dataset
Description
Data simulated by Shenvi et al. (2019) describing an intervention to reduce falls among the elderly.
Usage
falls_long
Format
A data frame with 50000 observations of 6 binary variables.
Details
As described in Walley et al (2023): "Under this intervention, a certain proportion of individuals aged over 65 would be assessed and classified as low or high risk. Those assessed to be at a high risk are referred to a falls clinic for an advanced assessment. All those who are referred, 50% of other high risk individuals, and 10% of low risk individuals go on to receive treatment. It is assumed that those who are not assessed receive neither referral nor treatment."
Source
The original data have been downloaded from the cegpy
github repository and is also available
as supplementary materials of Walley et al (2023).
In the original data pairs of variables Housing-Assessment and
Referred-Treatment are encoded in variables HousingAssessment and Treatment
respectively. Function colsplit from package reshape2 was
used to split those variables.
References
Walley G., Shenvi A., Strong P., Kobalczyk K. (2023), cegpy: Modelling with chain event graphs in Python, Knowledge-Based Systems, Volume 274.
Shenvi, A., Smith, J.Q., Walton, R., Eldridge, S. (2019). Modelling with Non-stratified Chain Event Graphs. In: Argiento, R., Durante, D., Wade, S. (eds) Bayesian Statistics and New Generations. BAYSM 2018. Springer Proceedings in Mathematics & Statistics, vol 296.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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