treetime/treeregression.py
def explained_variance(self):
        """calculate standard explained variance

        Returns
        -------
        float
            r-value of the root-to-tip distance and time.
            independent of regression model, but dependent on root choice
        """
        self.tree.root._v=0
        for n in self.tree.get_nonterminals(order='preorder'):
            for c in n:
                c._v = n._v + self.branch_value(c)
        raw = np.array([(self.tip_value(n), n._v) for n in self.tree.get_terminals()
                         if self.tip_value(n) is not None])
        return np.corrcoef(raw.T)[0,1]
def explained_variance(self):
        self.tree.root._v=0
        for n in self.tree.get_nonterminals(order='preorder'):
            for c in n:
                c._v = n._v + self.branch_value(c)
        raw = np.array([(self.tip_value(n), n._v) for n in self.tree.get_terminals()
                         if self.tip_value(n) is not None])
        return np.corrcoef(raw.T)[0,1]
def explained_variance ( self ) : self . tree . root . _v = 0 for n in self . tree . get_nonterminals ( order = 'preorder' ) : for c in n : c . _v = n . _v + self . branch_value ( c ) raw = np . array ( [ ( self . tip_value ( n ) , n . _v ) for n in self . tree . get_terminals ( ) if self . tip_value ( n ) is not None ] ) return np . corrcoef ( raw . T ) [ 0 , 1 ]
calculate standard explained variance

        Returns
        -------
        float
            r-value of the root-to-tip distance and time.
            independent of regression model, but dependent on root choice
calculate standard explained variance