BUG: fix issues in mstats.ttest_1samp. Add axis keyword.

scipy/stats/mstats_basic.py

@@ -762,18 +762,21 @@ def sen_seasonal_slopes(x):
 #---- --- Inferential statistics ---
 #####--------------------------------------------------------------------------
 
-def ttest_onesamp(a, popmean):
-    a = ma.asarray(a)
-    x = a.mean(axis=None)
-    v = a.var(axis=None,ddof=1)
-    n = a.count(axis=None)
-    df = n-1
-    svar = ((n-1)*v) / float(df)
-    t = (x-popmean)/ma.sqrt(svar*(1.0/n))
-    prob = betai(0.5*df,0.5,df/(df+t*t))
-    return t,prob
-ttest_onesamp.__doc__ = stats.ttest_1samp.__doc__
-ttest_1samp = ttest_onesamp
+def ttest_1samp(a, popmean, axis=0):
+    a, axis = _chk_asarray(a, axis)
+    if a.size == 0:
+        return (np.nan, np.nan)
+
+    x = a.mean(axis=axis)
+    v = a.var(axis=axis, ddof=1)
+    n = a.count(axis=axis)
+    df = n - 1.
+    svar = ((n - 1) * v) / df
+    t = (x - popmean) / ma.sqrt(svar / n)
+    prob = betai(0.5 * df, 0.5, df / (df + t*t))
+    return t, prob
+ttest_1samp.__doc__ = stats.ttest_1samp.__doc__
+ttest_onesamp = ttest_1samp
 
 
 def ttest_ind(a, b, axis=0):

scipy/stats/stats.py

@@ -3150,7 +3150,7 @@ def ttest_1samp(a, popmean, axis=0):
     t = np.divide(d, denom)
     t, prob = _ttest_finish(df, t)
 
-    return t,prob
+    return t, prob
 
 
 def _ttest_finish(df,t):

scipy/stats/tests/test_mstats_basic.py

@@ -549,6 +549,7 @@ def test_nd_input(self):
             assert_allclose(res_2d[1], [res_1d[1]] * 2)
 
 
+#TODO: for all ttest functions, add tests with masked array inputs
 class TestTtest_rel():
 
     def test_vs_nonmasked(self):
@@ -614,5 +615,33 @@ def test_empty(self):
         assert_(np.all(np.isnan(res1)))
 
 
+class TestTtest_1samp():
+
+    def test_vs_nonmasked(self):
+        np.random.seed(1234567)
+        outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
+
+        # 1-D inputs
+        res1 = stats.ttest_1samp(outcome[:, 0], 1)
+        res2 = mstats.ttest_1samp(outcome[:, 0], 1)
+        assert_allclose(res1, res2)
+
+        # 2-D inputs
+        res1 = stats.ttest_1samp(outcome[:, 0], outcome[:, 1], axis=None)
+        res2 = mstats.ttest_1samp(outcome[:, 0], outcome[:, 1], axis=None)
+        assert_allclose(res1, res2)
+        res1 = stats.ttest_1samp(outcome[:, :2], outcome[:, 2:], axis=0)
+        res2 = mstats.ttest_1samp(outcome[:, :2], outcome[:, 2:], axis=0)
+        assert_allclose(res1, res2)
+
+        # Check default is axis=0
+        res3 = mstats.ttest_1samp(outcome[:, :2], outcome[:, 2:])
+        assert_allclose(res2, res3)
+
+    def test_empty(self):
+        res1 = mstats.ttest_1samp([], 1)
+        assert_(np.all(np.isnan(res1)))
+
+
 if __name__ == "__main__":
     run_module_suite()