Merge pull request #118 from pedrovma/spreg1.4

pysal · Sep 19, 2023 · 4ff0b0d · 4ff0b0d
2 parents 3d0b8c0 + c74cf34
commit 4ff0b0d
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Showing 28 changed files with 3,710 additions and 1,823 deletions.
diff --git a/.github/workflows/unittests.yml b/.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

diff --git a/spreg/diagnostics_sp.py b/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
 

diff --git a/spreg/diagnostics_sur.py b/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)