Add expected summary output to unit tests for examples in documentation

tests/test_anova.py

@@ -50,3 +50,6 @@ def test_regress_ftest_ol8_2():
 	assert result.dof1 == 2
 	assert result.dof2 == 21
 	assert result.pvalue < 0.001
+	
+	expected_summary = 'F(2, 21) = 29.57; p < 0.001*'
+	assert result.summary() == expected_summary

tests/test_chi2.py

@@ -67,3 +67,14 @@ def test_chi2_ol10_18():
 	assert result.oddsratio.point == approx(1.333, abs=0.001)
 	assert result.oddsratio.ci_lower == approx(1.113, abs=0.001)
 	assert result.oddsratio.ci_upper == approx(1.596, abs=0.001)
+	
+	expected_summary = '''Stress    False  True
+Response             
+False       250   400
+True        750  1600
+
+χ²(1) = 9.82; p = 0.002*
+OR (95% CI) = 1.33 (1.11–1.60)
+RR (95% CI) = 1.11 (1.03–1.18)'''
+	
+	assert result.summary() == expected_summary

tests/test_mannwhitney.py

@@ -32,3 +32,8 @@ def test_mannwhitney_ol6_6():
 	result = yli.mannwhitney(df, 'Oxygen', 'Sample', method='asymptotic', alternative='less')
 	
 	assert result.pvalue == approx(0.00007, abs=0.00001)
+	
+	expected_summary = '''U = 6.0; p < 0.001*
+r = 0.92, Before > After'''
+	
+	assert result.summary() == expected_summary

tests/test_regress.py

@@ -113,15 +113,38 @@ def test_regress_logit_ol12_23():
 	assert lrtest_result.dof == 2
 	assert lrtest_result.pvalue == approx(0.0191, rel=0.02)
 	
+	# Can't validate intercept as very negative
+	
 	expbeta_fib = np.exp(result.terms['Fibrinogen'].beta)
 	assert expbeta_fib.point == approx(6.756, rel=0.01)
 	assert expbeta_fib.ci_lower == approx(1.007, rel=0.01)
 	assert expbeta_fib.ci_upper == approx(45.308, rel=0.02)
+	assert result.terms['Fibrinogen'].pvalue == approx(0.0491, rel=0.01)
 	
 	expbeta_gam = np.exp(result.terms['GammaGlobulin'].beta)
 	assert expbeta_gam.point == approx(1.169, abs=0.001)
 	assert expbeta_gam.ci_lower == approx(0.924, abs=0.001)
 	assert expbeta_gam.ci_upper == approx(1.477, abs=0.001)
+	assert result.terms['GammaGlobulin'].pvalue == approx(0.1925, rel=0.01)
+	
+	expected_summary = '''             Logistic Regression Results              
+======================================================
+Dep. Variable:  Unhealthy  |  No. Observations:     32
+        Model:      Logit  |         Df. Model:      2
+       Method:        MLE  |     Df. Residuals:     29
+         Date: {0:%Y-%m-%d}  |         Pseudo R²:   0.26
+         Time:   {0:%H:%M:%S}  |          LL-Model: -11.47
+  Std. Errors: Non-Robust  |           LL-Null: -15.44
+                           |            p (LR):  0.02*
+======================================================
+                exp(β)   (95% CI)          p   
+-----------------------------------------------
+  (Intercept)     0.00 (0.00 -  0.24)    0.03* 
+   Fibrinogen     6.80 (1.01 - 45.79)    0.049*
+GammaGlobulin     1.17 (0.92 -  1.48)    0.19  
+-----------------------------------------------'''.format(result.fitted_dt)
+	
+	assert result.summary() == expected_summary
 
 def test_regress_logit_ol10_18():
 	"""Compare odds ratios via yli.regress for Ott & Longnecker (2016) example 10.18"""
@@ -172,3 +195,22 @@ def test_regress_penalisedlogit_kleinman():
 	assert lrtest_result.statistic == approx(78.95473)
 	assert lrtest_result.dof == 1
 	assert lrtest_result.pvalue < 0.0001
+	
+	expected_summary = '''          Penalised Logistic Regression Results          
+=========================================================
+Dep. Variable:      Outcome  |  No. Observations:     240
+        Model:        Logit  |         Df. Model:       1
+       Method: Penalised ML  |         Pseudo R²:    0.37
+         Date:   {0:%Y-%m-%d}  |          LL-Model:  -66.43
+         Time:     {0:%H:%M:%S}  |           LL-Null: -105.91
+  Std. Errors:   Non-Robust  |            p (LR): <0.001*
+=========================================================
+                β      (95% CI)          p   
+---------------------------------------------
+(Intercept)   -2.28 (-2.77 - -1.85)   <0.001*
+       Pred    5.99  (3.95 - 10.85)   <0.001*
+---------------------------------------------'''.format(result.fitted_dt)
+	
+	assert result.summary() == expected_summary
+
+# TODO: Test for logit_then_regress

tests/test_ttest.py

@@ -38,3 +38,8 @@ def test_ttest_ind_ol6_1():
 	assert result.delta.point == approx(0.54, abs=0.01)
 	assert result.delta.ci_lower == approx(0.272, abs=0.01)
 	assert result.delta.ci_upper == approx(0.808, abs=0.01)
+	
+	expected_summary = '''t(18) = 4.24; p < 0.001*
+Δμ (95% CI) = 0.54 (0.27–0.81), Fresh > Stored'''
+	
+	assert result.summary() == expected_summary