Goodness of fit fix (#473)

* fix mean of residuals in plot_goodness_of_fit

* add possibility to choose between normalized and unnormalized errors

Author: plakrisenko

petab/v1/visualize/plot_residuals.py

@@ -136,6 +136,7 @@ def plot_goodness_of_fit(
     size: tuple = (10, 7),
     color=None,
     ax: plt.Axes | None = None,
+    normalized_error: bool = True,
 ) -> matplotlib.axes.Axes:
     """
     Plot goodness of fit.
@@ -154,6 +155,10 @@ def plot_goodness_of_fit(
         `matplotlib.pyplot.scatter`.
     ax:
         Axis object.
+    normalized_error:
+        Type of error to display.
+        If True, mean of squared normalized residuals is shown,
+        otherwise mean of squared residuals.
 
     Returns
     -------
@@ -168,12 +173,26 @@ def plot_goodness_of_fit(
             "are needed for goodness_of_fit"
         )
 
-    residual_df = calculate_residuals(
-        measurement_dfs=petab_problem.measurement_df,
-        simulation_dfs=simulations_df,
-        observable_dfs=petab_problem.observable_df,
-        parameter_dfs=petab_problem.parameter_df,
-    )[0]
+    if normalized_error:
+        residual_df = calculate_residuals(
+            measurement_dfs=petab_problem.measurement_df,
+            simulation_dfs=simulations_df,
+            observable_dfs=petab_problem.observable_df,
+            parameter_dfs=petab_problem.parameter_df,
+            normalize=True,
+        )[0]
+        error_name = "mean of squared\nnormalized residuals"
+    else:
+        residual_df = calculate_residuals(
+            measurement_dfs=petab_problem.measurement_df,
+            simulation_dfs=simulations_df,
+            observable_dfs=petab_problem.observable_df,
+            parameter_dfs=petab_problem.parameter_df,
+            normalize=False,
+        )[0]
+        error_name = "mean of squared residuals"
+    error = np.mean(np.power(residual_df["residual"], 2))
+
     slope, intercept, r_value, p_value, std_err = stats.linregress(
         simulations_df["simulation"],
         petab_problem.measurement_df["measurement"],
@@ -199,15 +218,14 @@ def plot_goodness_of_fit(
     ax.plot(x, x, linestyle="--", color="gray")
     ax.plot(x, intercept + slope * x, "r", label="fitted line")
 
-    mse = np.mean(np.abs(residual_df["residual"]))
     ax.text(
         0.1,
         0.70,
         f"$R^2$: {r_value**2:.2f}\n"
         f"slope: {slope:.2f}\n"
         f"intercept: {intercept:.2f}\n"
         f"p-value: {p_value:.2e}\n"
-        f"mean squared error: {mse:.2e}\n",
+        f"{error_name}: {error:.2e}\n",
         transform=ax.transAxes,
     )