add r-squared metric

fooof/core/info.py

@@ -25,7 +25,7 @@ def get_description():
     """
 
     attributes = {'results' : ['aperiodic_params_', 'gaussian_params_', 'peak_params_',
-                               'r_squared_', 'error_'],
+                               'r_squared_', 'adj_r_squared_', 'error_'],
                   'settings' : ['peak_width_limits', 'max_n_peaks',
                                 'min_peak_height', 'peak_threshold',
                                 'aperiodic_mode'],

fooof/core/strings.py

@@ -312,6 +312,7 @@ def gen_results_fm_str(fm, concise=False):
         # Goodness if fit
         'Goodness of fit metrics:',
         'R^2 of model fit is {:5.4f}'.format(fm.r_squared_),
+        'Adjusted R^2 of model fit is {:5.4f}'.format(fm.adj_r_squared_),
         'Error of the fit is {:5.4f}'.format(fm.error_),
         '',
 
@@ -351,6 +352,7 @@ def gen_results_fg_str(fg, concise=False):
     # Extract all the relevant data for printing
     n_peaks = len(fg.get_params('peak_params'))
     r2s = fg.get_params('r_squared')
+    adj_r2s = fg.get_params('adj_r_squared')
     errors = fg.get_params('error')
     exps = fg.get_params('aperiodic_params', 'exponent')
     kns = fg.get_params('aperiodic_params', 'knee') \
@@ -399,6 +401,8 @@ def gen_results_fg_str(fg, concise=False):
         'Goodness of fit metrics:',
         '   R2s -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
         .format(np.nanmin(r2s), np.nanmax(r2s), np.nanmean(r2s)),
+        'Adj R2s -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
+        .format(np.nanmin(adj_r2s), np.nanmax(adj_r2s), np.nanmean(adj_r2s)),
         'Errors -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
         .format(np.nanmin(errors), np.nanmax(errors), np.nanmean(errors)),
         '',

fooof/data/data.py

@@ -56,7 +56,8 @@ class FOOOFMetaData(namedtuple('FOOOFMetaData', ['freq_range', 'freq_res'])):
 
 
 class FOOOFResults(namedtuple('FOOOFResults', ['aperiodic_params', 'peak_params',
-                                               'r_squared', 'error', 'gaussian_params'])):
+                                               'r_squared', 'adj_r_squared', 'error',
+                                                'gaussian_params'])):
     """Model results from parameterizing a power spectrum.
 
     Parameters
@@ -68,6 +69,9 @@ class FOOOFResults(namedtuple('FOOOFResults', ['aperiodic_params', 'peak_params'
         Fitted parameter values for the peaks. Each row is a peak, as [CF, PW, BW].
     r_squared : float
         R-squared of the fit between the full model fit and the input data.
+    adj_r_squared : float
+        R-squared of the fit between the full model fit and the input data, 
+        adjusted for the number of parameters in the model.
     error : float
         Error of the full model fit.
     gaussian_params : 2d array

fooof/objs/fit.py

@@ -127,6 +127,9 @@ class FOOOF():
         Each row is a gaussian, as [mean, height, standard deviation].
     r_squared_ : float
         R-squared of the fit between the input power spectrum and the full model fit.
+    adj_r_squared_ : float
+        Adjusted R-squared of the fit between the input power spectrum and the full model fit,
+         adjusted for the number of parameters in the model.
     error_ : float
         Error of the full model fit.
     n_peaks_ : int
@@ -281,6 +284,7 @@ def _reset_data_results(self, clear_freqs=False, clear_spectrum=False, clear_res
             self.gaussian_params_ = np.empty([0, 3])
             self.peak_params_ = np.empty([0, 3])
             self.r_squared_ = np.nan
+            self.adj_r_squared_ = np.nan
             self.error_ = np.nan
 
             self.fooofed_spectrum_ = None
@@ -367,6 +371,7 @@ def add_results(self, fooof_result):
         self.gaussian_params_ = fooof_result.gaussian_params
         self.peak_params_ = fooof_result.peak_params
         self.r_squared_ = fooof_result.r_squared
+        self.adj_r_squared_ = fooof_result.adj_r_squared
         self.error_ = fooof_result.error
 
         self._check_loaded_results(fooof_result._asdict())
@@ -567,7 +572,7 @@ def get_params(self, name, col=None):
 
         Parameters
         ----------
-        name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared'}
+        name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared', 'adj_r_squared'}
             Name of the data field to extract.
         col : {'CF', 'PW', 'BW', 'offset', 'knee', 'exponent'} or int, optional
             Column name / index to extract from selected data, if requested.
@@ -1098,8 +1103,14 @@ def _drop_peak_overlap(self, guess):
     def _calc_r_squared(self):
         """Calculate the r-squared goodness of fit of the model, compared to the original data."""
 
+        # compute r-squared
         r_val = np.corrcoef(self.power_spectrum, self.fooofed_spectrum_)
         self.r_squared_ = r_val[0][1] ** 2
+
+        # compute adjusted r-squared
+        n = len(self.power_spectrum) # number of data points
+        k = len(self.peak_params_) * 3 + 2 # number of parameters
+        self.adj_r_squared_ = 1 - (1 - self.r_squared_) * (n - 1) / (n - k - 1)
 
 
     def _calc_error(self, metric=None):

fooof/objs/group.py

@@ -88,8 +88,8 @@ class FOOOFGroup(FOOOF):
       and the BW of the peak, is 2*std of the gaussian (as 'two sided' bandwidth).
     - The FOOOFGroup object inherits from the FOOOF object. As such it also has data
       attributes (`power_spectrum` & `fooofed_spectrum_`), and parameter attributes
-      (`aperiodic_params_`, `peak_params_`, `gaussian_params_`, `r_squared_`, `error_`)
-      which are defined in the context of individual model fits. These attributes are
+      (`aperiodic_params_`, `peak_params_`, `gaussian_params_`, `r_squared_`, `adj_r_squared_`,
+       `error_`) which are defined in the context of individual model fits. These attributes are
       used during the fitting process, but in the group context do not store results
       post-fitting. Rather, all model fit results are collected and stored into the
       `group_results` attribute. To access individual parameters of the fit, use
@@ -334,7 +334,7 @@ def get_params(self, name, col=None):
 
         Parameters
         ----------
-        name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared'}
+        name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared', 'adj_r_squared'}
             Name of the data field to extract across the group.
         col : {'CF', 'PW', 'BW', 'offset', 'knee', 'exponent'} or int, optional
             Column name / index to extract from selected data, if requested.

fooof/objs/utils.py

@@ -98,10 +98,11 @@ def average_fg(fg, bands, avg_method='mean', regenerate=True):
 
     # Goodness of fit measures: extract & average
     r2 = avg_func(fg.get_params('r_squared'))
+    adj_r2 = avg_func(fg.get_params('adj_r_squared'))
     error = avg_func(fg.get_params('error'))
 
     # Collect all results together, to be added to FOOOF object
-    results = FOOOFResults(ap_params, peak_params, r2, error, gauss_params)
+    results = FOOOFResults(ap_params, peak_params, r2, adj_r2, error, gauss_params)
 
     # Create the new FOOOF object, with settings, data info & results
     fm = FOOOF()

fooof/plts/fg.py

@@ -11,6 +11,7 @@
 from fooof.plts.templates import plot_scatter_1, plot_scatter_2, plot_hist
 from fooof.plts.utils import savefig
 from fooof.plts.style import style_plot
+from fooof.plts.utils import check_ax
 
 plt = safe_import('.pyplot', 'matplotlib')
 gridspec = safe_import('.gridspec', 'matplotlib')
@@ -104,8 +105,17 @@ def plot_fg_gf(fg, ax=None, **plot_kwargs):
         Keyword arguments to pass into the ``style_plot``.
     """
 
-    plot_scatter_2(fg.get_params('error'), 'Error',
-                   fg.get_params('r_squared'), 'R^2', 'Goodness of Fit', ax=ax)
+    ax = check_ax(ax)
+    ax1 = ax.twinx()
+
+    plot_scatter_1(fg.get_params('error'), 'Error', 'Goodness of Fit', color='#1f77b4', ax=ax)
+    plot_scatter_1(fg.get_params('r_squared'), 'R^2', x_val=1, color='#1f77b4', ax=ax1)
+    plot_scatter_1(fg.get_params('adj_r_squared'), x_val=2, color='#1f77b4', ax=ax1)
+
+    ax.set(xlim=[-0.5, 2.5],
+           xticks=[0, 1, 2],
+           xticklabels=['Error', 'R^2', 'Adj. R^2'])
+    ax.tick_params(axis='x', labelsize=16)
 
 
 @savefig

fooof/plts/templates.py

@@ -17,7 +17,7 @@
 ###################################################################################################
 
 @check_dependency(plt, 'matplotlib')
-def plot_scatter_1(data, label=None, title=None, x_val=0, ax=None):
+def plot_scatter_1(data, label=None, title=None, x_val=0, color=None, ax=None):
     """Plot a scatter plot, with a single y-axis.
 
     Parameters
@@ -30,6 +30,9 @@ def plot_scatter_1(data, label=None, title=None, x_val=0, ax=None):
         Title for the plot.
     x_val : int, optional, default: 0
         Position along the x-axis to plot set of data.
+    color : color, optional, default: None
+        Color of data points plotted ('c' argument for pyplot.scatter).
+        None will use default color.
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
 
@@ -43,7 +46,11 @@ def plot_scatter_1(data, label=None, title=None, x_val=0, ax=None):
     # Create x-axis data, with small jitter for visualization purposes
     x_data = np.ones_like(data) * x_val + np.random.normal(0, 0.025, data.shape)
 
-    ax.scatter(x_data, data, s=36, alpha=set_alpha(len(data)))
+    # Plot the data
+    if color is None:
+        ax.scatter(x_data, data, s=36, alpha=set_alpha(len(data)))
+    else:
+        ax.scatter(x_data, data, s=36, alpha=set_alpha(len(data)), c=color)
 
     if label:
         ax.set_ylabel(label, fontsize=16)

fooof/tests/data/test_data.py

@@ -29,7 +29,7 @@ def test_fooof_meta_data():
 
 def test_fooof_results():
 
-    results = FOOOFResults([1, 1], [10, 0.5, 1], 0.95, 0.05, [10, 0.5, 0.5])
+    results = FOOOFResults([1, 1], [10, 0.5, 1], 0.95, 0.94, 0.05, [10, 0.5, 0.5])
     assert results
 
     results_fields = OBJ_DESC['results']

fooof/tests/objs/test_fit.py

@@ -243,7 +243,7 @@ def test_add_data():
 
     # Test that prior data does not get cleared, when requesting not to clear
     tfm._reset_data_results(True, True, True)
-    tfm.add_results(FOOOFResults([1, 1], [10, 0.5, 0.5], 0.95, 0.02, [10, 0.5, 0.25]))
+    tfm.add_results(FOOOFResults([1, 1], [10, 0.5, 0.5], 0.95, 0.94, 0.02, [10, 0.5, 0.25]))
     tfm.add_data(freqs, pows, clear_results=False)
     assert tfm.has_data
     assert tfm.has_model
@@ -285,7 +285,7 @@ def test_add_results():
     tfm = get_tfm()
 
     # Test adding results
-    fooof_results = FOOOFResults([1, 1], [10, 0.5, 0.5], 0.95, 0.02, [10, 0.5, 0.25])
+    fooof_results = FOOOFResults([1, 1], [10, 0.5, 0.5], 0.95, 0.94, 0.02, [10, 0.5, 0.25])
     tfm.add_results(fooof_results)
     assert tfm.has_model
     for setting in OBJ_DESC['results']: