diff --git a/GPy/testing/plotting_tests.py b/GPy/testing/plotting_tests.py
index 462e361a..573a2cb0 100644
--- a/GPy/testing/plotting_tests.py
+++ b/GPy/testing/plotting_tests.py
@@ -77,6 +77,9 @@ def testPlot():
     m.optimize()
     m.plot_data(ax=ax)
     m.plot_mean(ax=ax)
+    m.plot_mean(ax=ax, plot_raw=True)
+    m.plot_mean(ax=ax, apply_link=True)
+    m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
     m.plot_confidence(ax=ax)
     m.plot_density(ax=ax)
     return ax
@@ -93,8 +96,17 @@ def testPlotClassification():
     m.optimize()
     m.plot_data(ax=ax)
     m.plot_mean(ax=ax)
+    m.plot_mean(ax=ax, plot_raw=True)
+    m.plot_mean(ax=ax, apply_link=True)
+    m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
     m.plot_confidence(ax=ax)
+    m.plot_confidence(ax=ax, plot_raw=True)
+    m.plot_confidence(ax=ax, apply_link=True)
+    m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
     m.plot_density(ax=ax)
+    m.plot_density(ax=ax, plot_raw=True)
+    m.plot_density(ax=ax, apply_link=True)
+    m.plot_density(ax=ax, plot_raw=True, apply_link=True)
     return ax
 
 @image_comparison(baseline_images=['sparse_gp_class'], extensions=['pdf','png'])
@@ -108,8 +120,18 @@ def testPlotSparseClassification():
     m.optimize()
     m.plot_data(ax=ax)
     m.plot_mean(ax=ax)
+    m.plot_mean(ax=ax, plot_raw=True)
+    m.plot_mean(ax=ax, apply_link=True)
+    m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
     m.plot_confidence(ax=ax)
+    m.plot_confidence(ax=ax, plot_raw=True)
+    m.plot_confidence(ax=ax, apply_link=True)
+    m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
     m.plot_density(ax=ax)
+    m.plot_density(ax=ax, plot_raw=True)
+    m.plot_density(ax=ax, apply_link=True)
+    m.plot_density(ax=ax, plot_raw=True, apply_link=True)
+    m.plot_inducing(ax=ax)
     return ax
 
 @image_comparison(baseline_images=['sparse_gp'], extensions=['pdf','png'])
@@ -123,6 +145,41 @@ def testPlotSparse():
     m.optimize()
     m.plot_data(ax=ax)
     m.plot_mean(ax=ax)
+    m.plot_mean(ax=ax, plot_raw=True)
+    m.plot_mean(ax=ax, apply_link=True)
+    m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
     m.plot_confidence(ax=ax)
+    m.plot_confidence(ax=ax, plot_raw=True)
+    m.plot_confidence(ax=ax, apply_link=True)
+    m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
     m.plot_density(ax=ax)
+    m.plot_density(ax=ax, plot_raw=True)
+    m.plot_density(ax=ax, apply_link=True)
+    m.plot_density(ax=ax, plot_raw=True, apply_link=True)
+    m.plot_inducing(ax=ax)
+    return ax
+
+@image_comparison(baseline_images=['sparse_latent'], extensions=['pdf','png'])
+def testPlotSparse():
+    fig, ax = plt.subplots()
+    np.random.seed(11111)
+    X = np.random.uniform(0, 1, (40, 1))
+    f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
+    Y = f+np.random.normal(0, .1, f.shape)
+    m = GPy.models.SparseGPRegression(X, Y)
+    m.optimize()
+    m.plot_data(ax=ax)
+    m.plot_mean(ax=ax)
+    m.plot_mean(ax=ax, plot_raw=True)
+    m.plot_mean(ax=ax, apply_link=True)
+    m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
+    m.plot_confidence(ax=ax)
+    m.plot_confidence(ax=ax, plot_raw=True)
+    m.plot_confidence(ax=ax, apply_link=True)
+    m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
+    m.plot_density(ax=ax)
+    m.plot_density(ax=ax, plot_raw=True)
+    m.plot_density(ax=ax, apply_link=True)
+    m.plot_density(ax=ax, plot_raw=True, apply_link=True)
+    m.plot_inducing(ax=ax)
     return ax