BUG/ENH: fix RLM start_params closes #9233. enable RLMDetS for 0 or 1…

… start_exog

statsmodels/robust/covariance.py

@@ -1288,6 +1288,18 @@ def _get_detcov_startidx(z, h, options_start=None, methods_cov="all"):
     scale_func = options_start.get("scale_func", mad)
     z = (z - loc_func(z)) / scale_func(z)
 
+    if np.squeeze(z).ndim == 1:
+        # only one random variable
+        z = np.squeeze(z)
+        nobs = z.shape[0]
+        idx_sel = np.argpartition(np.abs(z), h)[:h]
+        idx_all = [(idx_sel, "abs-resid")]
+        idx_sorted = np.argsort(z)
+        h_tail = (nobs - h) // 2
+        idx_all.append((idx_sorted[h_tail : h_tail + h], "trimmed-tail"))
+        return idx_all
+
+    # continue if more than 1 random variable
     cov_all = _cov_starting(z, standardize=False, quantile=0.5)
 
     idx_all = []

statsmodels/robust/resistant_linear_model.py

@@ -39,6 +39,7 @@ def __init__(self, endog, exog, norm=None, breakdown_point=0.5,
         # data for robust mahalanobis distance of starting sets
         self.breakdown_point = breakdown_point
 
+        # TODO: detect constant
         if col_indices is None:
             exog_start = self.exog[:, 1:]
         else:
@@ -51,6 +52,13 @@ def __init__(self, endog, exog, norm=None, breakdown_point=0.5,
 
     def _get_start_params(self, h):
         # I think we should use iterator with yield
+
+        if self.data_start.shape[1] == 0 and self.exog.shape[1] == 1:
+            quantiles = np.quantile(self.endog, [0.25, 0.5, 0.75])
+            start_params_all = [np.atleast_1d([q]) for q in quantiles]
+            return start_params_all
+
+
         starts = _get_detcov_startidx(
             self.data_start, h, options_start=None, methods_cov="all")
 

statsmodels/robust/robust_linear_model.py

@@ -263,6 +263,7 @@ def fit(self, maxiter=50, tol=1e-8, scale_est='mad', init=None, cov='H1',
             wls_results = lm.WLS(self.endog, self.exog).fit()
         else:
             start_params = np.asarray(start_params, dtype=np.double).squeeze()
+            start_params = np.atleast_1d(start_params)
             if (start_params.shape[0] != self.exog.shape[1] or
                     start_params.ndim != 1):
                 raise ValueError('start_params must by a 1-d array with {} '