# 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'
