Updates for spreg 1.4

.github/workflows/unittests.yml

@@ -47,7 +47,7 @@
 
  - name: install bleeding edge libpysal (only Ubuntu / Python 3.9)
  shell: bash -l {0}
- run: pip install git+https://github.com/pysal/libpysal.git@master
+ run: pip install git+https://github.com/pysal/libpysal.git@main
  if: matrix.os == 'ubuntu-latest' && contains(matrix.environment-file, 'DEV')
 
  - name: run tests - bash

spreg/diagnostics_sp.py

@@ -18,7 +18,6 @@
 
 
 class LMtests:
-
  """
  Lagrange Multiplier tests. Implemented as presented in :cite:`Anselin1996a`
 
@@ -142,7 +141,6 @@ def __init__(self, ols, w, tests=["all"]):
 
 
 class MoranRes:
-
  """
  Moran's I for spatial autocorrelation in residuals from OLS regression
 
@@ -396,7 +394,6 @@ def __init__(self, iv, w, case="nosp"):
 
 
 class spDcache:
-
  """
  Helper class to compute reusable pieces in the spatial diagnostics module
  ...
@@ -669,7 +666,7 @@ def get_vI(ols, w, ei, spDcache):
  B = spDcache.AB[1]
  trB = np.sum(B.diagonal()) * 4.0
  vi = (w.n ** 2 / (w.s0 ** 2 * (w.n - ols.k) * (w.n - ols.k + 2.0))) * (
- w.s1 + 2.0 * trA2 - trB - ((2.0 * (spDcache.trA ** 2)) / (w.n - ols.k))
+  w.s1 + 2.0 * trA2 - trB - ((2.0 * (spDcache.trA ** 2)) / (w.n - ols.k))
  )
  return vi
 
@@ -716,9 +713,6 @@ def akTest(iv, w, spDcache):
  p : float
  P-value of the test
 
- ToDo:
- * Code in as Nancy
- * Compare both
  """
  mi = get_mI(iv, w, spDcache)
  # Phi2
@@ -731,6 +725,42 @@ def akTest(iv, w, spDcache):
  return (mi, ak[0][0], pval[0][0])
 
 
+def comfac_test(lambd, beta, gamma, vm):
+ """
+ Computes the Spatial Common Factor Hypothesis test as shown in Anselin (1988, p. 226-229)
+
+ Parameters
+ ----------
+
+ lambd : float
+ Spatial autoregressive coefficient (as in lambd*Wy)
+ beta : array
+ Coefficients of the exogenous (not spatially lagged) variables, without the constant (as in X*beta)
+ gamma : array
+ coefficients of the spatially lagged exogenous variables (as in WX*gamma)
+ vm : array
+ Variance-covariance matrix of the coefficients
+ Obs. Needs to match the order of theta' = [beta', gamma', lambda]
+
+ Returns
+ -------
+ W : float
+ Wald statistic
+ pvalue : float
+ P value for Wald statistic calculated as a Chi sq. distribution
+ with k-1 degrees of freedom
+
+ """
+ g = lambd * beta + gamma
+ G = np.vstack((lambd * np.eye(beta.shape[0]), np.eye(beta.shape[0]), beta.T))
+
+ GVGi = la.inv(np.dot(G.T, np.dot(vm, G)))
+ W = np.dot(g.T, np.dot(GVGi, g))[0][0]
+ df = G.shape[1]
+ pvalue = chisqprob(W, df)
+ return W, pvalue
+
+
 def _test():
  import doctest
 

spreg/diagnostics_sur.py

@@ -221,7 +221,7 @@ def surLMlag(n_eq, WS, bigy, bigX, bigE, bigYP, sig, varb):
 
  """
  # Score
- Y = np.hstack((bigy[r]) for r in range(n_eq))
+ Y = np.hstack([bigy[r] for r in range(n_eq)])
  WY = WS * Y
  EWY = np.dot(bigE.T, WY)
  sigi = la.inv(sig)