skbio.diversity.alpha.fisher_alpha¶
- skbio.diversity.alpha.fisher_alpha(counts)[source]¶
Calculate Fisher’s alpha, a metric of diversity.
State: Experimental as of 0.4.0.
Fisher’s alpha is estimated by solving the following equation for \(\alpha\):
\[S=\alpha\ln(1+\frac{N}{\alpha})\]where \(S\) is the number of OTUs and \(N\) is the total number of individuals in the sample.
- Parameters:
counts (1-D array_like, int) – Vector of counts.
- Returns:
Fisher’s alpha.
- Return type:
double
- Raises:
RuntimeError – If the optimizer fails to solve for Fisher’s alpha.
Notes
Fisher’s alpha is defined in [1]. See also [2].
There is no analytical solution to Fisher’s alpha. However, one can use optimization techniques to obtain a numeric solution. This function calls SciPy’s
minimize_scalar
to find alpha. It is deterministic. The result should be reasonably close to the true alpha.Alpha can become large when most species are singletons. Alpha = +inf when all species are singletons.
When the community is empty (i.e., all counts are zero), alpha = 0.
References