covidfear {bnRep} | R Documentation |
covidfear Bayesian Network
Description
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear.
Format
A discrete Bayesian network to understand the effect of demographic factors on the answers to the COVID-19 fear scale and the relationship between the scale items. The Bayesian network was learned as in the referenced paper. The vertices are:
- Age
(Young, Adult);
- Gender
(Female, Male);
- Fear
I am most afraid of COVID-19 (Disagree, Neither, Agree);
- Think
It makes me uncomfortable to think about COVID-19 (Disagree, Neither, Agree);
- Hands
My hands become clammy when I think about COVID-19 (Disagree, Neither, Agree);
- Life
I fear losing my life because of COVID-19 (Disagree, Neither, Agree);
- News
I become nervous or anxious when watching news and stories about COVID-19 on social media (Disagree, Neither, Agree);
- Sleep
I cannot sleep because I am worried about getting COVID-19 (Disagree, Neither, Agree);
- Hearth
My heart races or palpitates when I think about getting COVID-19 (Disagree, Neither, Agree);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Leonelli, M., & Varando, G. (2024). Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. Applied Intelligence, 54(2), 1734-1750.