def __init__(self, Y='MEG', X='condition', sub=None, ds=None,
p=.05, contours={.01: '.5', .001: '0'}):
self.name = "anova"
Y = self.Y = asndvar(Y, sub=sub, ds=ds)
X = self.X = asmodel(X, sub=sub, ds=ds)
fitter = _glm.lm_fitter(X)
properties = Y.properties.copy()
properties['colorspace'] = _cs.get_sig(p=p, contours=contours)
kwargs = dict(dims=Y.dims[1:], properties=properties)
self.all = []
self.p_maps = {}
for e, _, Ps in fitter.map(Y.x):
name = e.name
P = ndvar(Ps, name=name, **kwargs)
self.all.append(P)
self.p_maps[e] = P
def __init__(self, Y, X, t=.1, samples=1000, replacement=False,
tstart=None, tstop=None, close_time=0,
pmax=1, sub=None, ds=None,
):
"""
Arguments
---------
Y : ndvar
Measurements (dependent variable)
X : categorial
Model
t : scalar
Threshold (uncorrected p-value) to use for finding clusters
samples : int
Number of samples to estimate parameter distributions
replacement : bool
whether random samples should be drawn with replacement or
without
tstart, tstop : None | scalar
Time window for clusters.
**None**: use the whole epoch;
**scalar**: use only a part of the epoch
.. Note:: implementation is not optimal: F-values are still
computed but ignored.
close_time : scalar
Close gaps in clusters that are smaller than this interval. Assumes
that Y is a uniform time series.
sub : index
Apply analysis to a subset of cases in Y, X
pmax : scalar <= 1
Maximum cluster p-values to keep cluster.
.. FIXME:: connectivity for >2 dimensional data. Currently, adjacent
samples are connected.
"""
Y = self.Y = asndvar(Y, sub=sub, ds=ds)
X = self.X = asmodel(X, sub=sub, ds=ds)
lm = _glm.lm_fitter(X)
# get F-thresholds from p-threshold
tF = {}
if lm.full_model:
for e in lm.E_MS:
effects_d = lm.E_MS[e]
if effects_d:
df_d = sum(ed.df for ed in effects_d)
tF[e] = scipy.stats.distributions.f.isf(t, e.df, df_d)
else:
df_d = X.df_error
tF = {e: scipy.stats.distributions.f.isf(t, e.df, df_d) for e in X.effects}
# Estimate statistic distributions from permuted Ys
kwargs = dict(tstart=tstart, tstop=tstop, close_time=close_time, unit='F')
dists = {e: cluster_dist(Y, samples, tF[e], name=e.name, **kwargs) for e in tF}
self.cluster_dists = dists
for _, Yrs in _resample(Y, replacement=replacement, samples=samples):
for e, F in lm.map(Yrs.x, p=False):
dists[e].add_perm(F)
# Find clusters in the actual data
test0 = lm.map(Y.x, p=False)
self.effects = []
self.clusters = {}
self.F_maps = {}
for e, F in test0:
self.effects.append(e)
dist = dists[e]
dist.add_original(F)
self.clusters[e] = dist
self.F_maps[e] = dist.P
self.name = "ANOVA Permutation Cluster Test"
self.tF = tF
self.all = [[self.F_maps[e]] + self.clusters[e].clusters
for e in self.X.effects if e in self.F_maps]
