def __init__(self,
y,
x,
c1=None,
c0=None,
match=None,
sub=None,
ds=None,
tail=0):
ct = Celltable(y, x, match, sub, cat=(c1, c0), ds=ds, coercion=asvar)
c1, c0 = ct.cat
n = len(ct.y)
if n <= 2:
raise ValueError("Not enough observations for t-test (n=%i)" % n)
self._y = dataobj_repr(ct.y)
self._x = dataobj_repr(ct.x)
self.df = n - 2
groups = ct.x == c1
groups.dtype = np.int8
self.t = stats.t_ind(ct.y.x[:, None], groups)[0]
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
self._c1 = c1
self._c0 = c0
def __init__(self,
y,
x,
c1=None,
c0=None,
match=None,
sub=None,
ds=None,
tail=0):
if match is None:
raise TypeError("The `match` argument needs to be specified for a "
"related measures t-test.")
ct = Celltable(y, x, match, sub, cat=(c1, c0), ds=ds, coercion=asvar)
c1, c0 = ct.cat
if not ct.all_within:
raise ValueError(
"conditions %r and %r do not have the same values "
"on %s" % (c1, c0, dataobj_repr(ct.match)))
n = len(ct.y) // 2
if n <= 2:
raise ValueError("Not enough observations for t-test (n=%i)" % n)
self._y = dataobj_repr(ct.y)
self._x = dataobj_repr(ct.x)
self.c1_mean = ct.y[:n].mean()
self.c0_mean = ct.y[n:].mean()
self.diff = ct.y[:n] - ct.y[n:]
self.df = n - 1
self.t = stats.t_1samp(self.diff.x[:, None])[0]
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
self._c1 = c1
self._c0 = c0
def __init__(self, y, match=None, sub=None, ds=None, tail=0):
ct = Celltable(y, None, match, sub, ds=ds, coercion=asvar)
n = len(ct.Y)
if n <= 2:
raise ValueError("Not enough observations for t-test (n=%i)" % n)
self._y = dataobj_repr(ct.Y)
self.df = n - 1
self.t = stats.t_1samp(ct.Y.x[:, None])[0]
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
def __init__(self, y, match=None, sub=None, ds=None, tail=0):
ct = Celltable(y, None, match, sub, ds=ds, coercion=asvar)
n = len(ct.y)
if n <= 2:
raise ValueError("Not enough observations for t-test (n=%i)" % n)
self._y = dataobj_repr(ct.y)
self.df = n - 1
self.t = stats.t_1samp(ct.y.x[:, None])[0]
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
def test_t_ind():
"Test independent samples t-test"
ds = datasets.get_uts(True)
y = ds.eval("utsnd.x")
n_cases = len(y)
n = n_cases / 2
t = stats.t_ind(y, n, n)
p = stats.ttest_p(t, n_cases - 2)
t_sp, p_sp = scipy.stats.ttest_ind(y[:n], y[n:])
assert_equal(t, t_sp)
assert_equal(p, p_sp)
assert_allclose(stats.ttest_t(p, n_cases - 2), np.abs(t))
# permutation
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
stats.t_ind(y, n, n, out=t, perm=perm)
y_perm[perm] = y
t_sp, _ = scipy.stats.ttest_ind(y_perm[:n], y_perm[n:])
assert_allclose(t, t_sp)
def test_t_ind():
"Test independent samples t-test"
ds = datasets.get_uts(True)
y = ds.eval("utsnd.x")
n_cases = len(y)
n = n_cases / 2
t = stats.t_ind(y, n, n)
p = stats.ttest_p(t, n_cases - 2)
t_sp, p_sp = scipy.stats.ttest_ind(y[:n], y[n:])
assert_equal(t, t_sp)
assert_equal(p, p_sp)
assert_allclose(stats.ttest_t(p, n_cases - 2), np.abs(t))
# permutation
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
stats.t_ind(y, n, n, out=t, perm=perm)
y_perm[perm] = y
t_sp, _ = scipy.stats.ttest_ind(y_perm[:n], y_perm[n:])
assert_allclose(t, t_sp)
def __init__(self, y, x, c1=None, c0=None, match=None, sub=None, ds=None,
tail=0):
if match is None:
raise TypeError("The `match` argument needs to be specified for a "
"related measures t-test.")
ct = Celltable(y, x, match, sub, cat=(c1, c0), ds=ds, coercion=asvar)
c1, c0 = ct.cat
if not ct.all_within:
raise ValueError("conditions %r and %r do not have the same values "
"on %s" % (c1, c0, dataobj_repr(ct.match)))
n = len(ct.Y) // 2
if n <= 2:
raise ValueError("Not enough observations for t-test (n=%i)" % n)
self._y = dataobj_repr(ct.Y)
self._x = dataobj_repr(ct.X)
self.diff = ct.Y[:n] - ct.Y[n:]
self.df = n - 1
self.t = stats.t_1samp(self.diff.x[:, None])[0]
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
self._c1 = c1
self._c0 = c0
def __init__(self, t, df, tail):
self.t = t
self.df = df
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
def __init__(self, t, df, tail):
self.t = t
self.df = df
self.p = stats.ttest_p(self.t, self.df, tail)
self.tail = tail
