def cinference(self, nperm=99, maxiter=1000):
"""Compare the within sum of squares for the solution against
conditional simulated solutions where areas are randomly assigned to
regions that maintain the cardinality of the original solution and
respect contiguity relationships.
Parameters
----------
nperm : int
number of random permutations for calculation of
pseudo-p_values
maxiter : int
maximum number of attempts to find each permutation
Attributes
----------
pvalue : float
pseudo p_value
feas_sols : int
number of feasible solutions found
Notes
-----
it is possible for the number of feasible solutions (feas_sols) to be
less than the number of permutations requested (nperm); an exception
is raised if this occurs.
Examples
--------
Setup is the same as shown above except using a 5x5 community.
>>> import numpy as np
>>> import pysal
>>> np.random.seed(100)
>>> w=pysal.weights.lat2W(5,5)
>>> z=np.random.random_sample((w.n,2))
>>> p=np.ones((w.n,1),float)
>>> floor=3
>>> solution=pysal.region.Maxp(w,z,floor,floor_variable=p,initial=100)
Set nperm to 9 meaning that 9 random regions are computed and used for
the computation of a pseudo-p-value for the actual Max-p solution. In
empirical work this would typically be set much higher, e.g. 999 or
9999.
>>> solution.cinference(nperm=9, maxiter=100)
>>> solution.cpvalue
0.1
"""
ids = self.w.id_order
num_regions = len(self.regions)
wsss = np.zeros(nperm + 1)
self.cwss = self.objective_function()
cards = [len(i) for i in self.regions]
sim_solutions = RR.Random_Regions(ids,
num_regions,
cardinality=cards,
contiguity=self.w,
maxiter=maxiter,
permutations=nperm)
self.cfeas_sols = len(sim_solutions.solutions_feas)
if self.cfeas_sols < nperm:
raise Exception('not enough feasible solutions found')
cv = 1
c = 1
for solution in sim_solutions.solutions_feas:
wss = self.objective_function(solution.regions)
wsss[c] = wss
if wss <= self.cwss:
cv += 1
c += 1
self.cpvalue = cv / (1. + self.cfeas_sols)
self.cwss_perm = wsss
self.cwss_perm[0] = self.cwss
def inference(self, nperm=99):
"""Compare the within sum of squares for the solution against
simulated solutions where areas are randomly assigned to regions that
maintain the cardinality of the original solution.
Parameters
----------
nperm : int
number of random permutations for calculation of
pseudo-p_values
Attributes
----------
pvalue : float
pseudo p_value
Examples
--------
Setup is the same as shown above except using a 5x5 community.
>>> import numpy as np
>>> import pysal
>>> np.random.seed(100)
>>> w=pysal.weights.lat2W(5,5)
>>> z=np.random.random_sample((w.n,2))
>>> p=np.ones((w.n,1),float)
>>> floor=3
>>> solution=pysal.region.Maxp(w,z,floor,floor_variable=p,initial=100)
Set nperm to 9 meaning that 9 random regions are computed and used for
the computation of a pseudo-p-value for the actual Max-p solution. In
empirical work this would typically be set much higher, e.g. 999 or
9999.
>>> solution.inference(nperm=9)
>>> solution.pvalue
0.1
"""
ids = self.w.id_order
num_regions = len(self.regions)
wsss = np.zeros(nperm + 1)
self.wss = self.objective_function()
cards = [len(i) for i in self.regions]
sim_solutions = RR.Random_Regions(ids,
num_regions,
cardinality=cards,
permutations=nperm)
cv = 1
c = 1
for solution in sim_solutions.solutions_feas:
wss = self.objective_function(solution.regions)
wsss[c] = wss
if wss <= self.wss:
cv += 1
c += 1
self.pvalue = cv / (1. + len(sim_solutions.solutions_feas))
self.wss_perm = wsss
self.wss_perm[0] = self.wss