segregation.inference.SingleValueTest¶
-
class
segregation.inference.
SingleValueTest
(seg_class, iterations_under_null=500, null_approach='systematic', two_tailed=True, **kwargs)[source]¶ Perform inference for a single segregation measure
- Parameters
- seg_classa PySAL segregation object
- iterations_under_nullnumber of iterations under null hyphothesis
- null_approachargument that specifies which type of null hypothesis the inference will iterate. Please take a look at Notes (1).
“systematic” : assumes that every group has the same probability with restricted conditional probabilities p_0_j = p_1_j = p_j = n_j/n (multinomial distribution). “bootstrap” : generates bootstrap replications of the units with replacement of the same size of the original data. “evenness” : assumes that each spatial unit has the same global probability of drawing elements from the minority group of the fixed total unit population (binomial distribution).
“permutation” : randomly allocates the units over space keeping the original values.
“systematic_permutation” : assumes absence of systematic segregation and randomly allocates the units over space. “even_permutation” : assumes the same global probability of drawning elements from the minority group in each spatial unit and randomly allocates the units over space.
- two_tailedboolean. Please take a look at Notes (2).
If True, p_value is two-tailed. Otherwise, it is right one-tailed.
- **kwargscustomizable parameters to pass to the segregation measures. Usually they need to be the same input that the seg_class was built.
Notes
1) The different approaches for the null hypothesis affect directly the results of the inference depending on the combination of the index type of seg_class and the null_approach chosen. Therefore, the user needs to be aware of how these approaches are affecting the data generation process of the simulations in order to draw meaningful conclusions. For example, the Modified Dissimilarity (ModifiedDissim) and Modified Gini (ModifiedGiniSeg) indexes, rely exactly on the distance between evenness through sampling which, therefore, the “evenness” value for null approach would not be the most appropriate for these indexes.
The one-tailed p_value attribute might not be appropriate for some measures, as the two-tailed. Therefore, it is better to rely on the est_sim attribute.
Examples
Several examples can be found here https://github.com/pysal/segregation/blob/master/notebooks/inference_wrappers_example.ipynb.
- Attributes
- p_valuefloat
Pseudo One or Two-Tailed p-value estimated from the simulations
- est_simnumpy array
Estimates of the segregation measure under the null hypothesis
- statisticfloat
The point estimation of the segregation measure that is under test
Methods
plot
([ax])Plot the Infer_Segregation class
-
__init__
(seg_class, iterations_under_null=500, null_approach='systematic', two_tailed=True, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(seg_class[, iterations_under_null, …])Initialize self.
plot
([ax])Plot the Infer_Segregation class