#NEWLINE# def summary( self , xname = None , alpha = 0.05 , title = None ) : #NEWLINE# #INDENT# 'Summarize the Results of the hypothesis test\n\n        Parameters\n        -----------\n\n        xname : list of strings, optional\n            Default is `c_##` for ## in p the number of regressors\n        alpha : float\n            significance level for the confidence intervals. Default is\n            alpha = 0.05 which implies a confidence level of 95%.\n        title : string, optional\n            Title for the params table. If not None, then this replaces the\n            default title\n\n        Returns\n        -------\n        smry : string or Summary instance\n            This contains a parameter results table in the case of t or z test\n            in the same form as the parameter results table in the model\n            results summary.\n            For F or Wald test, the return is a string.\n\n        ' #NEWLINE# if ( self . effect is not None ) : #NEWLINE# #INDENT# if ( title is None ) : #NEWLINE# #INDENT# title = 'Test for Constraints' #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# if ( title == '' ) : #NEWLINE# #INDENT# title = None #NEWLINE# #UNINDENT# #UNINDENT# use_t = ( self . distribution == 't' ) #NEWLINE# yname = 'constraints' #NEWLINE# if ( xname is None ) : #NEWLINE# #INDENT# xname = [ ( 'c%d' % ii ) for ii in range ( len ( self . effect ) ) ] #NEWLINE# #UNINDENT# from statsmodels.iolib.summary import summary_params #NEWLINE# pvalues = np . atleast_1d ( self . pvalue ) #NEWLINE# summ = summary_params ( ( self , self . effect , self . sd , self . statistic , pvalues , self . conf_int ( alpha ) ) , yname = yname , xname = xname , use_t = use_t , title = title ) #NEWLINE# return use_t #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# if hasattr ( self , 'fvalue' ) : #NEWLINE# #INDENT# return ( '<F test: F=%s, p=%s, df_denom=%d, df_num=%d>' % ( repr ( self . fvalue ) , self . pvalue , self . df_denom , self . df_num ) ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# return ( '<Wald test: statistic=%s, p-value=%s>' % ( self . statistic , self . pvalue ) )