def test_correct():
"""Testing function correct"""
# Random input 1-D array p of random size and with 2 nan enteries
size = random.randint(1, 5)
p = np.random.rand(size, )
idx = random.randint(0, int(size / 3))
p[idx] = np.nan
p[2 * idx] = np.nan
p_isnan = np.zeros(p.shape, dtype=bool)
p_isnan[idx] = True
p_isnan[2 * idx] = True
p_isnotnan = np.logical_not(p_isnan)
# Test output for different correction values
q_uncorrected = my_crt.correct(p)
q_bonferroni = my_crt.correct(p, correction="bonferroni")
q_fdr = my_crt.correct(p, correction="fdr")
assert_array_almost_equal(q_uncorrected, p)
for q in [q_uncorrected, q_bonferroni, q_fdr]:
assert_array_equal(p_isnan, np.isnan(q))
assert_true(np.all(p[p_isnotnan] <= q[p_isnotnan]))
# Check error is raised for unkown value of correction
with assert_raises_regexp(ValueError, "Unknown correction."):
my_crt.correct(p, correction="blabla")
def statistical_test(df, conditions, estimators={'kind': 'tangent',
'cov_estimator': None},
p_correction="fdr",
n_jobs=1):
grouped = df.groupby(["condition", "subj_id"])
dict_list = list()
entries = ("baseline", "mean signif baseline",
"follow up", "mean signif follow up",
"comparison", "tstat", "pval", "mean signif comparison")
for (ix1_, condition1) in enumerate(conditions):
for (ix2_, condition2) in enumerate(conditions):
if ix1_ <= ix2_:
continue
cond = list()
grouped = df.groupby("subj_id")
for _, group in grouped:
cond.append(group[group["condition"] == condition1]
["region_signals"].iloc[0])
cond.append(group[group["condition"] == condition2]
["region_signals"].iloc[0])
X = CovEmbedding(**estimators).fit_transform(cond)
X = [vec_to_sym(x) for x in X]
X = np.asarray(X)
X = sym_to_vec(X, isometry=False)
Y = var_stabilize(X, estimators['kind'])
t_stat_baseline, p_baseline = scipy.stats.ttest_1samp(Y[::2, ...],
0.0,
axis=0)
q_baseline = pval_correction.correct(p_baseline, correction=p_correction)
q_baseline[np.isnan(q_baseline)] = 0.
baseline_signif = np.tanh(Y[::2, ...]).mean(axis=0) * (q_baseline < 0.05)
t_stat_followup, p_followup = scipy.stats.ttest_1samp(Y[1::2, ...],
0.0,
axis=0)
q_followup = pval_correction.correct(p_followup, correction=p_correction)
q_followup[np.isnan(q_followup)] = 0.
followup_signif = np.tanh(Y[1::2, ...]).mean(axis=0) * (q_followup < 0.05)
t_stat, p = scipy.stats.ttest_rel(Y[::2, ...],
Y[1::2, ...],
axis=0)
q = pval_correction.correct(p, correction=p_correction)
q[np.isnan(q)] = 0.
comp_signif = (np.tanh(Y[1::2, ...])- np.tanh(Y[::2, ...])).mean(axis=0) * \
(q < 0.05) * (np.minimum(q_baseline, q_followup) < 0.05 )
print "{} vs. {}: t_stat = {}, q-val = {}".format(
condition1, condition2, t_stat, q)
dict_list.append(
dict(zip(*[entries,
("{}".format(condition1),
baseline_signif,
"{}".format(condition2),
followup_signif,
"{} vs. {}".format(condition1, condition2),
t_stat, q, comp_signif)])))
return DataFrame(dict_list, columns=entries)
