def ancova(Y, factorial_model, covariate, interaction=None, sub=None, v=True, empty=True, ems=None):
"""
OBSOLETE
args
----
Y: dependent variable
factorial model:
covariate:
kwargs
------
interaction: term from the factorial model to check for interaction with
the covariate
v=True: display more information
**anova_kwargs: ems, empty
Based on
--------
Exercise to STATISTICS: AN INTRODUCTION USING R
http://www.bio.ic.ac.uk/research/crawley/statistics/exercises/R6Ancova.pdf
"""
assert isvar(covariate)
anova_kwargs = {"empty": empty, "ems": ems}
if sub != None:
Y = Y[sub]
factorial_model = factorial_model[sub]
covariate = covariate[sub]
if interaction != None:
interaction = interaction[sub]
# if interaction: assert type(interaction) in [factor]
factorial_model = asmodel(factorial_model)
a1 = lm(Y, factorial_model)
if v:
print a1.anova(title="MODEL 1", **anova_kwargs)
print "\n"
a2 = lm(Y, factorial_model + covariate)
if v:
print a2.anova(title="MODEL 2: Main Effect Covariate", **anova_kwargs)
print "\n"
print 'Model with "%s" Covariate > without Covariate' % covariate.name
print comparelm(a1, a2)
if interaction:
logging.debug("%s / %s" % (covariate.name, interaction.name))
logging.debug("%s" % (covariate.__div__))
i_effect = covariate.__div__(interaction)
# i_effect = covariate / interaction
a3 = lm(Y, factorial_model + i_effect)
if v:
print "\n"
print a3.anova(title="MODEL 3: Interaction")
# compare
print '\n"%s"x"%s" Interaction > No Covariate:' % (covariate.name, interaction.name)
print comparelm(a1, a3)
print '\n"%s"x"%s" Interaction > Main Effect:' % (covariate.name, interaction.name)
print comparelm(a2, a3)
def __str__(self):
f_names = [f.name for f in self._factors.values() if _vsl.isfactor(f)]
v_names = [f.name for f in self._factors.values() if _vsl.isvar(f)]
out = 'Variables:\n' + ', '.join(sorted(v_names))
out += '\nFactors:\n' + ', '.join(sorted(f_names))
if hasattr(self, '_stats'):
out += '\n\nSEGMENTS:\n' + ', '.join(f.name for f in self._stats.values())
return out
def _resample(Y, unit=None, replacement=True, samples=1000):
"""
Generator function to resample a dependent variable (Y) multiple times
unit: factor specdifying unit of measurement (e.g. subject). If unit is
specified, resampling proceeds by first resampling the categories of
unit (with or without replacement) and then shuffling the values
within unites (no replacement).
replacement: whether random samples should be drawn with replacement or
without
samples: number of samples to yield
"""
if isvar(Y):
Yout = Y.copy('_resampled')
Y
else:
Y = var(Y)
Yout = var(Y.copy(), name="Y resampled")
if unit:
ct = celltable(Y, unit)
unit_data = ct.get_data(out=list)
unit_indexes = ct.data_indexes.values()
x_out = Yout.x
if replacement:
n = len(ct.indexes)
for sample in xrange(samples):
source_ids = np.random.randint(n, size=n)
for index, source_index in zip(unit_indexes, source_ids):
data = unit_data[source_index]
np.random.shuffle(data)
x_out[index] = data
yield Yout
else:
for sample in xrange(samples):
random.shuffle(unit_data)
for index, data in zip(unit_indexes, unit_data):
np.random.shuffle(data)
x_out[index] = data
yield Yout
else:
if replacement:
N = Y.N
for i in xrange(samples):
index = np.random.randint(N)
Yout.x = Y.x[index]
yield Yout
else:
for i in xrange(samples):
np.random.shuffle(Yout.x)
yield Yout
def _resample(Y, unit=None, replacement=True, samples=1000):
"""
Generator function to resample a dependent variable (Y) multiple times
Y : var | ndvar
Variable which is to be resampled; a copy of ``Y`` is yielded in each
iteration.
unit : categorial
factor specifying unit of measurement (e.g. subject). If unit is
specified, resampling proceeds by first resampling the categories of
unit (with or without replacement) and then shuffling the values
within unites (no replacement).
replacement : bool
whether random samples should be drawn with replacement or
without
samples : int
number of samples to yield
"""
if isvar(Y):
pass
elif isndvar(Y):
if not Y.has_case:
raise ValueError("Need ndvar with cases")
else:
raise TypeError("need var or ndvar")
Yout = Y.copy('{name}_resampled')
if unit: # not implemented
ct = celltable(Y, unit)
unit_data = ct.get_data(out=list)
unit_indexes = ct.data_indexes.values()
x_out = Yout.x
if replacement:
n = len(ct.indexes)
for i in xrange(samples):
source_ids = np.random.randint(n, size=n)
for index, source_index in zip(unit_indexes, source_ids):
data = unit_data[source_index]
np.random.shuffle(data)
x_out[index] = data
yield i, Yout
else:
for i in xrange(samples):
random.shuffle(unit_data)
for index, data in zip(unit_indexes, unit_data):
np.random.shuffle(data)
x_out[index] = data
yield i, Yout
else: # OK
if replacement:
N = len(Y)
for i in xrange(samples):
index = np.random.randint(N, N)
Yout.x = Y.x[index]
yield i, Yout
else: # OK
for i in xrange(samples):
np.random.shuffle(Yout.x)
yield i, Yout
def correlations(Y, Xs, cat=None, levels=[.05, .01, .001], diff=None, sub=None,
pmax=None, nan=True): # , match=None):
"""
:arg var Y: first variable
:arg var X: second variable (or list of variables)
:arg cat: show correlations separately for different groups in the
data. Can be a ``factor`` (the correlation for each level is shown
separately) or an array of ``bool`` values (e.g. from a comparison like
``Stim==1``)
:arg list levels: significance levels to mark
:arg diff: (factor, cat_1, cat_2)
:arg sub: use only a subset of the data
:arg pmax: (None) don't show correlations with p>pmax
:arg nan: ``True``: display correlation which yield NAN;
``False``: hide NANs but mention occurrence in summary (not
implemented);
``None``: don't mention NANs
:rtype: Table
"""
levels = np.array(levels)
if isvar(Xs):
Xs = [Xs]
# SUB
if sub is not None:
Y = Y[sub]
Xs = [X[sub] for X in Xs]
if ismodel(cat) or isfactor(cat):
cat = cat[sub]
if diff is not None:
raise NotImplementedError
if cat is None:
table = fmtxt.Table('l' * 4)
table.cells('Variable', 'r', 'p', 'n')
else:
assert iscategorial(cat)
table = fmtxt.Table('l' * 5)
table.cells('Variable', 'Category', 'r', 'p', 'n')
table.midrule()
table.title("Correlations with %s" % (Y.name))
table._my_nan_count = 0
for X in Xs:
if cat is None:
_corr_to_table(table, Y, X, cat, levels, pmax=pmax, nan=nan)
else:
printXname = True
for cell in cat.cells:
tlen = len(table)
sub = (cat == cell)
_corr_to_table(table, Y, X, sub, levels, pmax=pmax, nan=nan,
printXname=printXname, label=cell_label(cell))
if len(table) > tlen:
printXname = False
# last row
if pmax is None:
p_text = ''
else:
p_text = 'all other p>{p}'.format(p=pmax)
if nan is False and table._my_nan_count > 0:
nan_text = '%s NANs' % table._my_nan_count
else:
nan_text = ''
if p_text or nan_text:
if p_text and nan_text:
text = ', '.join([p_text, nan_text])
else:
text = ''.join([p_text, nan_text])
table.cell("(%s)" % text)
return table
def data(Y, X=None, match=None, cov=[], sub=None, fmt=None, labels=True,
showcase=True):
"""
return a textab.table (printed as tsv table by default)
parameters
----------
Y: variable to display (can be model with several dependents)
X: categories defining cells (factorial model)
match: factor to match values on and return repeated-measures table
cov: covariate to report (WARNING: only works with match, where each value
on the matching variable corresponds with one value in the covariate)
sub: boolean array specifying which values to include (generate e.g.
with 'sub=T==[1,2]')
fmt: Format string
labels: display labels for nominal variables (otherwise display codes)
"""
if hasattr(Y, '_items'): # dataframe
Y = Y._items
Y = _data.asmodel(Y)
if _data.isfactor(cov) or _data.isvar(cov):
cov = [cov]
data = []
names_yname = [] # names including Yi.name for matched table headers
ynames = [] # names of Yi for independent measures table headers
within_list = []
for Yi in Y.effects:
_data, datalabels, names, _within = _data._split_Y(Yi, X, match=match,
sub=sub, datalabels=match)
data += _data
names_yname += ['({c})'.format(c=n) for n in names]
ynames.append(Yi.name)
within_list.append(_within)
within = within_list[0]
assert all([w==within for w in within_list])
# table
n_dependents = len(Y.effects)
n_cells = int(len(data) / n_dependents)
if within:
n, k = len(data[0]), len(data)
table = textab.Table('l' * (k + showcase + len(cov)))
# header line 1
if showcase:
table.cell(match.name)
case_labels = datalabels[0]
assert all([np.all(case_labels==l) for l in datalabels[1:]])
for i in range(n_dependents):
for name in names:
table.cell(name.replace(' ','_'))
for c in cov:
table.cell(c.name)
# header line 2
if n_dependents > 1:
if showcase:
table.cell()
for name in ynames:
[table.cell('(%s)'%name) for i in range(n_cells)]
for c in cov:
table.cell()
# body
table.midrule()
for i in range(n):
case = case_labels[i]
if showcase:
table.cell(case)
for j in range(k):
table.cell(data[j][i], fmt=fmt)
# covariates
indexes = match==case
for c in cov:
# test it's all the same values
case_cov = c[indexes]
if len(np.unique(case_cov.x)) != 1:
msg = 'covariate for case "%s" has several values'%case
raise ValueError(msg)
# get value
first_i = np.nonzero(indexes)[0][0]
cov_value = c[first_i]
if _data.isfactor(c) and labels:
cov_value = c.cells[cov_value]
table.cell(cov_value, fmt=fmt)
else:
table = textab.Table('l'*(1 + n_dependents))
table.cell(X.name)
[table.cell(y) for y in ynames]
table.midrule()
# data is now sorted: (cell_i within dependent_i)
# sort data as (X-cell, dependent_i)
data_sorted = []
for i_cell in range(n_cells):
data_sorted.append([data[i_dep*n_cells + i_cell] for i_dep in \
range(n_dependents)])
# table
for name, cell_data in zip(names, data_sorted):
for i in range(len(cell_data[0])):
table.cell(name)
for dep_data in cell_data:
v = dep_data[i]
table.cell(v, fmt=fmt)
return table