Dataset
Accreditation of new technologies for predicting intramuscular fat percentage: Combining Bayesian models and industry rules for transparent decisions
Murdoch University. Food Futures Institute
2024
DOI:
https://doi.org/10.60867/00000024
Abstract
These R scripts provide the code to reproduce the data and model outputs.
file1_simulate_data.r - provides the script to simulate 1000 test sets with sample size ranging from 5 to 200. In addition, these simulations are extended to provide data on 5 hypothetical devices with a range of standard deviations from the simulated ‘true’ value of {0.80, 0.95, 1.05 , 1.10 and 1.20}.
file2_simulate_rules.r - requires the data from file1_simulate_data.r. This script uses the simulated data to assess the pass/fail rate of the 5 devices using the rules based approach. The final dataframe provides the data required to plot figure 5.
file3_simulate_bayesian.r - requires the data from file1_simulate_data.r. This script uses the simulated data to assess the pass/fail rate of the 5 devices using the Bayesian regression approach. The final dataframe provides the data required to plot figure 7.
Details
- Title
- Accreditation of new technologies for predicting intramuscular fat percentage: Combining Bayesian models and industry rules for transparent decisions
- Authors/Creators
- Graham Gardner - Murdoch University, Centre for Animal Production and HealthC. Alston-Knox - Predictive Science (United States)
- Publisher
- Murdoch University. Food Futures Institute
- Identifiers
- 991005707265807891
- Murdoch Affiliation
- Centre for Animal Production and Health
- Language
- English
- Resource Type
- Dataset
Metrics
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