lib5c.algorithms.variance.combined module¶
-
lib5c.algorithms.variance.combined.
cross_rep_plus_deviation_variance
(obs, exp, rep, model='lognorm', min_disp=1e-08)[source]¶ Estimates pixel-wise variance as the squared deviation between observed and expected values.
- Parameters
obs (dict or list of np.ndarray) – Dict values or list entries are are square, symmetric count matrices across replicates.
exp (np.ndarray) – Square, symmetric matrix of expected values.
rep (int or str) – The index into
obs
identifying which replicate to compute variance estimates for.model ({'lognorm', 'norm'}) – Statistical model to use.
min_disp (float) – Force a minimum value of the dispersion parameter.
- Returns
The first three elements are the mean parameter estimate, dispersion estimate, and variance estimate, respectively, for each pixel. The fourth element is a boolean matrix showing which pixels are considered to be overdispersed.
- Return type
tuple of np.ndarray