estimate_positivity_rate_model: Train a simple Bayesian logistic model on classification...
In bakaburg1/BaySREn: BaySREn. An R package to automatise citation collection and screening in Systematic Reviews. Based on Bayesian active machine learning
estimate_positivity_rate_model R Documentation
Train a simple Bayesian logistic model on classification results
Description
The function uses an Annotation data set to train a Bayesian logistic model
that estimates the probability of a relevant record given the lower
boundaries of the PPD produced by the classification model for the records
whose label was manually reviewed.
Usage
estimate_positivity_rate_model(train_data, seed = 14129189)
Arguments
train_data
An Annotation data set with predictions produced by
enrich_annotation_file().
seed
An integer to replicate results
Details
The produced model can be used to predict the distribution of the cumulative
number of total unseen positive records.
Usually this function is not to be used directly but through
estimate_performance().
Value
An object of class brmsfit. See brms::brm() for mode info.
bakaburg1/BaySREn documentation built on March 30, 2022, 12:16 a.m.
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| estimate_positivity_rate_model | R Documentation |
Train a simple Bayesian logistic model on classification results
Description
The function uses an Annotation data set to train a Bayesian logistic model that estimates the probability of a relevant record given the lower boundaries of the PPD produced by the classification model for the records whose label was manually reviewed.
Usage
estimate_positivity_rate_model(train_data, seed = 14129189)
Arguments
train_data |
An Annotation data set with predictions produced by
|
seed |
An integer to replicate results |
Details
The produced model can be used to predict the distribution of the cumulative number of total unseen positive records.
Usually this function is not to be used directly but through
estimate_performance().
Value
An object of class brmsfit. See brms::brm() for mode info.
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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