def search_all_pairwise(all_data):
"""For each pair of variables, look for a significant regression slope."""
results = []
keys = list(set(all_data.keys()) - set(('SubjID',)))
for ki in range(len(keys)):
key1 = keys[ki]
if skip_key(key1):
continue
for ii in range(ki + 1, len(keys)):
key2 = keys[ii]
if skip_key(key2) or skip_pairing(key1, key2):
continue
result = find_one_relationship(all_data, key1, key2, rsq_thresh=0.10)
if result is not None:
results.append(result)
# Now, output the sorted result.
for key, p, r in sorted(results, lambda v1, v2: int(10000 * (abs(v1[2]) - abs(v2[2])))):
print("Significant at %.2e (r=%.3f): %s" % (p, r, key))
def search_all_vs_one(all_data, key, rsq_thresh=0., covariates=[], plot=False):
"""For each pair of variables, look for a significant regression slope."""
results = []
all_keys = list(set(all_data.keys()) - set(('SubjID', key)))
for all_key in all_keys:
if not is_ai_key(all_key):
continue
try:
result = find_one_relationship(all_data,
key,
all_key,
covariates=covariates,
rsq_thresh=rsq_thresh,
plot=plot)
except TypeError as te:
# Happens when the data type is string or other non-regressable.
continue
if result is not None:
results.append(result)
# Now, output the sorted result.
for all_key, p, r in sorted(results, lambda v1, v2: int(10000 * (v1[2] - v2[2]))):
print("Significant at %.2e (r=%.3f): %s" % (p, r, all_key))
