def _corr_to_table(table, Y, X, categories, levels, printXname=True, label=False,
pmax=None, nan=True):
r, p, n = _corr(X, Y, categories)
if (pmax is None) or (p <= pmax):
if nan or (not np.isnan(r)):
nstars = np.sum(p <= levels)
if printXname:
table.cell(X.name)
else:
table.cell()
if label:
table.cell(label)
table.cells(fmtxt.texstr(r) + fmtxt.Stars(nstars, of=len(levels)), p, n)
else:
table._my_nan_count += 1
def test(Y, X=None, against=0, match=None, sub=None,
par=True, corr='Hochberg',
title='{desc}'):
"""
One-sample tests.
kwargs
------
X: perform tests separately for all categories in X.
Against: can be
- value
- string (category in X)
"""
ct = celltable(Y, X, match, sub)
if par:
title_desc = "t-tests against %s" % against
statistic_name = 't'
else:
raise NotImplementedError
names = []; ts = []; dfs = []; ps = []
if isinstance(against, str):
k = len(ct.indexes) - 1
assert against in ct.cells
for id in ct.indexes:
label = ct.cells[id]
if against == label:
baseline_id = id
baseline = ct.data[id]
for id in ct.indexes:
if id == baseline_id:
continue
names.append(ct.cells[id])
if (ct.within is not False) and ct.within[id, baseline_id]:
t, p = scipy.stats.ttest_rel(baseline, ct.data[id])
df = len(baseline) - 1
else:
data = ct.data[id]
t, p = scipy.stats.ttest_ind(baseline, data)
df = len(baseline) + len(data) - 2
ts.append(t)
dfs.append(df)
ps.append(p)
elif np.isscalar(against):
k = len(ct.cells)
for id in ct.indexes:
label = ct.cells[id]
data = ct.data[id]
t, p = scipy.stats.ttest_1samp(data, against)
df = len(data) - 1
names.append(label); ts.append(t); dfs.append(df); ps.append(p)
if corr:
ps_adjusted = mcp_adjust(ps, corr)
else:
ps_adjusted = np.zeros(len(ps))
stars = star(ps, out=str) # , levels=levels, trend=trend, corr=corr
if len(np.unique(dfs)) == 1:
df_in_header = True
else:
df_in_header = False
table = fmtxt.Table('l' + 'r' * (3 - df_in_header + bool(corr)))
table.title(title.format(desc=title_desc))
if corr:
table.caption(_get_correction_caption(corr, k))
# header
table.cell("Effect")
if df_in_header:
table.cell([statistic_name,
fmtxt.texstr(dfs[0], property='_'),
], mat=True)
else:
table.cell(statistic_name, mat=True)
table.cell('df', mat=True)
table.cell('p', mat=True)
if corr:
table.cell(fmtxt.symbol('p', df=corr))
table.midrule()
# body
for name, t, mark, df, p, p_adj in zip(names, ts, stars, dfs, ps, ps_adjusted):
table.cell(name)
tex_stars = fmtxt.Stars(mark, of=3)
tex_t = fmtxt.texstr(t, fmt='%.2f')
table.cell([tex_t, tex_stars])
if not df_in_header:
table.cell(df)
table.cell(fmtxt.p(p))
if corr:
table.cell(fmtxt.p(p_adj))
return table
def pairwise(Y, X, match=None, sub=None, # data in
par=True, corr='Hochberg', trend=True, # stats
title='{desc}', mirror=False, # layout
):
"""
pairwise comparison according to factor structure
"""
# ct = celltable(Y, X, match=match, sub=sub)
# test
data, datalabels, names, within = _split_Y(Y, X, match=match, sub=sub)
test = _pairwise(data, within=within, parametric=par, corr=corr, #levels=levels,
trend=trend)
# extract test results
k = len(data)
indexes = test['pw_indexes']
statistic = test['statistic']
_K = test[statistic]
_P = test['p']
if corr:
_Pc = mcp_adjust(_P, corr)
_df = test['df']
_NStars = test['stars']
symbols = test['symbols']
# create TABLE
table = textab.Table('l'+'l'*(k-1+mirror))
title_desc = "Pairwise {0}".format(test['test'])
table.title(title.format(desc=title_desc))
table.caption(test['caption'])
# headings
table.cell()
for name in names[1-mirror:]:
table.cell(name)
table.midrule()
tex_peq = textab.texstr("p=")
#tex_df = textab.Element(df, "_", digits=0)
if corr and not mirror:
subrows = range(3)
else:
subrows = range(2)
for row in range(0, k-1+mirror):
for subrow in subrows: # contains t/p
# names column
if subrow is 0:
table.cell(names[row], r"\textbf")
else:
table.cell()
# rows
for col in range(1-mirror, k):
if row == col:
table.cell()
elif col > row:
index = indexes[(row, col)]
#(col-1) + ((k-2)*row) sum(range(k-1, k-1-row, -1))
K = _K[index]
p = _P[index]
df = _df[index]
# nstars = _NStars[index]
if subrow is 0:
tex_cell = textab.eq(statistic, K, df=df,
stars=symbols[index],
of=3+trend)
elif subrow is 1:
tex_cell = textab.texstr([tex_peq, texstr(p, fmt='%.3f')],
mat=True)
elif subrow is 2:
tex_cell = textab.eq('p', _Pc[index], df='c',
fmt='%.3f', drop0=True)
table.cell(tex_cell)
else:
if mirror and corr and subrow==0:
index = indexes[(col, row)]
p = _Pc[index]
table.cell(p, fmt='%.3f', drop0=True)
else:
table.cell()
return table
