def summary_find_nfact(self):
'''provides a summary for the selection of the number of factors
Returns
-------
sumstr : string
summary of the results for selecting the number of factors
'''
if not hasattr(self, 'results_find_nfact'):
self.fit_find_nfact()
results = self.results_find_nfact
sumstr = ''
sumstr += '\n' + 'Best result for k, by AIC, BIC, R2_adj, L1O'
# best = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
# np.argmin(results[:,-1],0))]
sumstr += '\n' + ' ' * 19 + '%5d %4d %6d %5d' % tuple(self.best_nfact)
from statsmodels.iolib.table import SimpleTable
headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ')
numformat = ['%6d'] + ['%10.3f'] * 4 #'%10.4f'
txt_fmt1 = dict(data_fmts=numformat)
tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1)
sumstr += '\n' + "PCA regression on simulated data,"
sumstr += '\n' + "DGP: 2 factors and 4 explanatory variables"
sumstr += '\n' + tabl.__str__()
sumstr += '\n' + "Notes: k is number of components of PCA,"
sumstr += '\n' + " constant is added additionally"
sumstr += '\n' + " k=0 means regression on constant only"
sumstr += '\n' + " L1O: sum of squared prediction errors for leave-one-out"
return sumstr
def summary_find_nfact(self):
'''provides a summary for the selection of the number of factors
Returns
-------
sumstr : string
summary of the results for selecting the number of factors
'''
if not hasattr(self, 'results_find_nfact'):
self.fit_find_nfact()
results = self.results_find_nfact
sumstr = ''
sumstr += '\n' + 'Best result for k, by AIC, BIC, R2_adj, L1O'
# best = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
# np.argmin(results[:,-1],0))]
sumstr += '\n' + ' '*19 + '%5d %4d %6d %5d' % tuple(self.best_nfact)
from statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
default_latex_fmt, default_html_fmt)
headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ')
numformat = ['%6d'] + ['%10.3f']*4 #'%10.4f'
txt_fmt1 = dict(data_fmts = numformat)
tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1)
sumstr += '\n' + "PCA regression on simulated data,"
sumstr += '\n' + "DGP: 2 factors and 4 explanatory variables"
sumstr += '\n' + tabl.__str__()
sumstr += '\n' + "Notes: k is number of components of PCA,"
sumstr += '\n' + " constant is added additionally"
sumstr += '\n' + " k=0 means regression on constant only"
sumstr += '\n' + " L1O: sum of squared prediction errors for leave-one-out"
return sumstr
