skbio.diversity.alpha.lladser_ci#
- skbio.diversity.alpha.lladser_ci(counts, r, alpha=0.95, f=10, ci_type='ULCL')[source]#
Calculate single CI of the conditional uncovered probability.
- Parameters:
- counts1-D array_like, int
Vector of counts.
- rint
Number of new colors that are required for the next prediction.
- alphafloat, optional
Desired confidence level.
- ffloat, optional
Ratio between upper and lower bound.
- ci_type{‘ULCL’, ‘ULCU’, ‘U’, ‘L’}
Type of confidence interval. If
'ULCL', upper and lower bounds with conservative lower bound. If'ULCU', upper and lower bounds with conservative upper bound. If'U', upper bound only, lower bound fixed to 0.0. If'L', lower bound only, upper bound fixed to 1.0.
- Returns:
- tuple
Confidence interval as
(lower_bound, upper_bound).
See also
Notes
This function is just a wrapper around the full CI estimator described in Theorem 2 (iii) in [1], intended to be called for a single best CI estimate on a complete sample.
References
[1]Lladser, Gouet, and Reeder, “Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown” PLoS 2011.