def _get_rg_table(rg_paths, RG, args):
'''Print a table of genetic correlations.'''
t = lambda attr: lambda obj: getattr(obj, attr, 'NA')
x = pd.DataFrame()
x['p1'] = [rg_paths[0] for i in range(1, len(rg_paths))]
x['p2'] = rg_paths[1:len(rg_paths)]
x['rg'] = list(map(t('rg_ratio'), RG))
x['se'] = list(map(lambda y: round(y, 3), map(t('rg_se'), RG)))
x['z'] = list(map(lambda y: round(y, 3), map(t('z'), RG)))
x['p'] = list(map(t('p'), RG))
if args.samp_prev is not None and args.pop_prev is not None and\
all((i is not None for i in args.samp_prev)) and all((i is not None for i in args.pop_prev)):
c = reg.h2_obs_to_liab(1, args.samp_prev[1], args.pop_prev[1])
x['h2_liab'] = list(
map(lambda x: c * x, map(t('tot'), map(t('hsq2'), RG))))
x['h2_liab_se'] = list(
map(lambda x: c * x, map(t('tot_se'), map(t('hsq2'), RG))))
else:
x['h2_obs'] = list(map(t('tot'), map(t('hsq2'), RG)))
x['h2_obs_se'] = list(map(t('tot_se'), map(t('hsq2'), RG)))
x['h2_int'] = list(map(t('intercept'), map(t('hsq2'), RG)))
x['h2_int_se'] = list(map(t('intercept_se'), map(t('hsq2'), RG)))
x['gcov_int'] = list(map(t('intercept'), map(t('gencov'), RG)))
x['gcov_int_se'] = list(map(t('intercept_se'), map(t('gencov'), RG)))
return x.to_string(header=True, index=False) + '\n'
def _get_rg_table(rg_paths, RG, args):
'''Print a table of genetic correlations.'''
t = lambda attr: lambda obj: getattr(obj, attr, 'NA')
x = pd.DataFrame()
x['p1'] = [rg_paths[0].split(os.sep)[-1]] * (len(rg_paths)-1)
x['p2'] = [p2.split(os.sep)[-1] for p2 in rg_paths[1:]]
x['rg'] = list(map(t('rg_ratio'), RG))
x['se'] = list(map(t('rg_se'), RG))
x['z'] = list(map(t('z'), RG))
x['p'] = list(map(t('p'), RG))
if (
args.samp_prev is not None and
args.pop_prev is not None and
all((i is not None for i in args.samp_prev)) and
all((i is not None for i in args.pop_prev))
):
c = map(lambda x, y: reg.h2_obs_to_liab(1, x, y), args.samp_prev[1:], args.pop_prev[1:])
x['h2_liab'] = list(map(lambda x, y: x * y, c, map(t('tot'), map(t('hsq2'), RG))))
x['h2_liab_se'] = list(map(lambda x, y: x * y, c, map(t('tot_se'), map(t('hsq2'), RG))))
else:
x['h2_obs'] = list(map(t('tot'), map(t('hsq2'), RG)))
x['h2_obs_se'] = list(map(t('tot_se'), map(t('hsq2'), RG)))
x['h2_int'] = list(map(t('intercept'), map(t('hsq2'), RG)))
x['h2_int_se'] = list(map(t('intercept_se'), map(t('hsq2'), RG)))
x['gcov_int'] = list(map(t('intercept'), map(t('gencov'), RG)))
x['gcov_int_se'] = list(map(t('intercept_se'), map(t('gencov'), RG)))
return x
def test_approx_scz(self):
# conversion for a balanced study of a 1% phenotype is about 1/2
x = reg.h2_obs_to_liab(1, 0.5, 0.01)
assert_array_almost_equal(x, 0.551907298063)
def test_approx_scz(self):
# conversion for a balanced study of a 1% phenotype is about 1/2
x = reg.h2_obs_to_liab(1, 0.5, 0.01)
assert_array_almost_equal(x, 0.551907298063)