esda.Join_Counts_Local_MV¶
- class esda.Join_Counts_Local_MV(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]¶
Multivariate Local Join Count Statistic
- __init__(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]¶
Initialize a Local_Join_Counts_MV estimator
- Parameters:
- connectivity
scipy.sparse
matrix
object
the connectivity structure describing the relationships between observed units. Need not be row-standardized.
- permutations
int
number of random permutations for calculation of pseudo p_values
- n_jobs
int
Number of cores to be used in the conditional randomisation. If -1, all available cores are used.
- keep_simulationsbool (default
True
) If
True
, the entire matrix of replications under the null is stored in memory and accessible; otherwise, replications are not saved- seed
int
(defaultNone
) Seed to ensure reproducibility of conditional randomizations. Must be set here, and not outside of the function, since numba does not correctly interpret external seeds nor numpy.random.RandomState instances.
- island_weight
int
orfloat
(default 0) value to use as a weight for the “fake” neighbor for every island. If
numpy.nan
, will propagate to the final local statistic depending on thestat_func
. If0
, then the lag is always zero for islands.- drop_islandsbool (default
True
) Whether or not to preserve islands as entries in the adjacency list. By default, observations with no neighbors do not appear in the adjacency list. If islands are kept, they are coded as self-neighbors with zero weight. See
libpysal.weights.to_adjlist()
.
- connectivity
Methods
__init__
([connectivity, permutations, ...])Initialize a Local_Join_Counts_MV estimator
fit
(variables[, n_jobs, permutations])- Parameters:
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(variables, n_jobs=1, permutations=999)[source]¶
- Parameters:
- variables
numpy.ndarray
array(s) containing binary (0/1) data
- Returns
- ——-
- the fitted estimator.
- variables
Notes
Technical details and derivations can be found in [].
Examples
>>> import libpysal >>> w = libpysal.weights.lat2W(4, 4) >>> x = np.ones(16) >>> x[0:8] = 0 >>> z = [0,1,0,1,1,1,1,1,0,0,1,1,0,0,1,1] >>> y = [0,1,1,1,1,1,1,1,0,0,0,1,0,0,1,1] >>> LJC_MV = Local_Join_Counts_MV(connectivity=w).fit([x, y, z]) >>> LJC_MV.LJC >>> LJC_MV.p_sim
Guerry data extending GeoDa tutorial >>> import libpysal >>> import geopandas as gpd >>> guerry = libpysal.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> guerry_ds[‘infq5’] = 0 >>> guerry_ds[‘donq5’] = 0 >>> guerry_ds[‘suic5’] = 0 >>> guerry_ds.loc[(guerry_ds[‘Infants’] > 23574), ‘infq5’] = 1 >>> guerry_ds.loc[(guerry_ds[‘Donatns’] > 10973), ‘donq5’] = 1 >>> guerry_ds.loc[(guerry_ds[‘Suicids’] > 55564), ‘suic5’] = 1 >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> LJC_MV = Local_Join_Counts_MV( … connectivity=w … ).fit([guerry_ds[‘infq5’], guerry_ds[‘donq5’], guerry_ds[‘suic5’]]) >>> LJC_MV.LJC >>> LJC_MV.p_sim