skbio.math.diversity.alpha.lladser_ci(counts, r, alpha=0.95, f=10, ci_type='ULCL')[source]

Calculate single CI of the conditional uncovered probability.


counts : 1-D array_like, int

Vector of counts.

r : int

Number of new colors that are required for the next prediction.

alpha : float, optional

Desired confidence level.

f : float, 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.



Confidence interval as (lower_bound, upper_bound).

See also



This function is just a wrapper around the full CI estimator described in Theorem 2 (iii) in [R50], intended to be called for a single best CI estimate on a complete sample.


[R50](1, 2) Lladser, Gouet, and Reeder, “Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown” PLoS 2011.