def __init__(self, Y, X, norm=None, sub=None, ds=None,
contours={.05: (.8, .2, .0), .01: (1., .6, .0), .001: (1., 1., .0)},
tp=.1, samples=1000, replacement=False,
tstart=None, tstop=None, close_time=0, pmax=1):
"""
Y : ndvar
Dependent variable.
X : continuous | None
The continuous predictor variable.
norm : None | categorial
Categories in which to normalize (z-score) X.
"""
Y = asndvar(Y, sub=sub, ds=ds)
X = asvar(X, sub=sub, ds=ds)
self.name = name = "%s corr %s" % (Y.name, X.name)
# calculate threshold
# http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Inference
self.n = n = len(X)
df = n - 2
tt = scipy.stats.distributions.t.isf(tp, df)
tr = tt / np.sqrt(df + tt ** 2)
cs = _cs.Colorspace(cmap=_cs.cm_xpolar, vmax=1, vmin= -1)
cdist = cluster_dist(Y, N=samples, t_upper=tr, t_lower= -tr,
tstart=tstart, tstop=tstop, close_time=close_time,
unit='r', pmax=pmax, name=name, cs=cs)
# normalization is done before the permutation b/c we are interested in the variance associated with each subject for the z-scoring.
Y = Y.copy()
Y.x = Y.x.reshape((n, -1))
if norm is not None:
for cell in norm.cells:
idx = (norm == cell)
Y.x[idx] = scipy.stats.mstats.zscore(Y.x[idx])
x = X.x.reshape((n, -1))
m_x = np.mean(x)
if np.isnan(m_x):
raise ValueError("np.mean(x) is nan")
self.x = x - m_x
for _, Yrs in _resample(Y, replacement=replacement, samples=samples):
r = self._corr(Yrs)
cdist.add_perm(r)
r = self._corr(Y)
cdist.add_original(r)
self.r_map = cdist.P
self.all = [[self.r_map] + cdist.clusters]
self.clusters = cdist
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]