def Zstar(self):
        """ predict the value of the fixed effect """
        RV = self.Ystar().copy()
        for term_i in range(self.n_terms):
            if self.identity_trick and self.A_identity[term_i]:
                RV-=np.dot(self.Fstar()[term_i],self.B_hat()[term_i])
            else:
                RV-=np.dot(self.Fstar()[term_i],np.dot(self.B_hat()[term_i],self.Astar()[term_i]))
        self.clear_cache('DLZ')
        return RV
def Zstar ( self ) : RV = self . Ystar ( ) . copy ( ) for term_i in range ( self . n_terms ) : if self . identity_trick and self . A_identity [ term_i ] : RV -= np . dot ( self . Fstar ( ) [ term_i ] , self . B_hat ( ) [ term_i ] ) else : RV -= np . dot ( self . Fstar ( ) [ term_i ] , np . dot ( self . B_hat ( ) [ term_i ] , self . Astar ( ) [ term_i ] ) ) self . clear_cache ( 'DLZ' ) return RV
predict the value of the fixed effect