def params_transform_univariate(params, cov_params, link=None, transform=None, row_labels=None):
 from statsmodels.genmod.families import links
 if ((link is None) and (transform is None)):
  link = links.Log()
 if ((row_labels is None) and hasattr(params, 'index')):
  row_labels = params.index
 params = np.asarray(params)
 predicted_mean = link.inverse(params)
 link_deriv = link.inverse_deriv(params)
 var_pred_mean = ((link_deriv ** 2) * np.diag(cov_params))
 dist = stats.norm
 linpred = PredictionResults(params, np.diag(cov_params), dist=dist, row_labels=row_labels, link=links.identity())
 res = PredictionResults(predicted_mean, var_pred_mean, dist=dist, row_labels=row_labels, linpred=linpred, link=link)
 return res

def params_transform_univariate ( params , cov_params , link = None , transform = None , row_labels = None ) : from statsmodels . genmod . families import links if ( ( link is None ) and ( transform is None ) ) : link = links . Log ( ) if ( ( row_labels is None ) and hasattr ( params , 'index' ) ) : row_labels = params . index params = np . asarray ( params ) predicted_mean = link . inverse ( params ) link_deriv = link . inverse_deriv ( params ) var_pred_mean = ( ( link_deriv ** 2 ) * np . diag ( cov_params ) ) dist = stats . norm linpred = PredictionResults ( params , np . diag ( cov_params ) , dist = dist , row_labels = row_labels , link = links . identity ( ) ) res = PredictionResults ( predicted_mean , var_pred_mean , dist = dist , row_labels = row_labels , linpred = linpred , link = link ) return res
results for univariate .