# and initialize them for both groups td = defaultdict(dict) for group in groups: td[group]['numTotal'] = 0.0 # td[group]['coeffsSig'] = [] td[group]['numSig'] = 0.0 # proportions of significant coeffs # td[group]['paramSizes'] = [] td[group]['paramSizesNormed'] = [] td[group]['Rs'] = [] td[group]['adjRs'] = [] td[group]['pvalues'] = [] # let's see if this article is suitable for cognates analysis: originalLHS = article.IVs + article.controls identifyCognatesReturns = GU.identifyCognates(dataCont, originalLHS, article.centralIVs, article.GSSYearsUsed, corrThreshold=0.6) if not identifyCognatesReturns: print 'No suitable cognates. Skipping.' continue else: cIV, cognate, GSSYearsWithCognate = identifyCognatesReturns # if we got this far, then this article does have suitable cognates, so let's estimate models # Now let's estimate the models for DV in article.DVs: for year in GSSYearsWithCognate: # group 2 models (with cognates) group = 'group2' print 'Running cognate models'