Description Usage Arguments Value Examples
Estimate highest density intervals and success rates from hap.py counts using a Binomial model and empirical Bayes. See package docs for details on method implementation.
1 2 | estimate_hdi(df, successes_col, totals_col, group_cols, aggregate_only = TRUE,
significance = 0.05, sample_size = 1e+05, max_alpha1 = 1000)
|
df |
A |
successes_col |
Name of the column that contains success counts. |
totals_col |
Name of the column that contains total counts. |
group_cols |
Vector of columns to group counts by. Observations within the same group will be treated as replicates. |
aggregate_only |
Estimate HDIs for aggregate replicate only (speeds up execution). Default: TRUE. |
significance |
Significance for HDI estimation. Default: 0.05 (= 95% HDIs). |
sample_size |
Number of observations to draw from the Beta posterior to estimate HDIs. Default: 1e5. |
max_alpha1 |
Upper bound for alpha hyperparameter in the aggregate Beta posterior. |
A data.frame
with performance counts, model hyperparameters,
success rate and HDI estimates.
1 2 3 4 5 | ## Not run:
hdi <- estimate_hdi(df, successes_col = 'TRUTH.TP', totals_col = 'TRUTH.TOTAL',
group_cols = c('Group.Id', 'Subset', 'Type', 'Subtype'))
## End(Not run)
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