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: VarArg,
x: CategorialArg,
c1: CellArg = None,
c0: CellArg = None,
match: CategorialArg = None,
sub: IndexArg = None,
ds: Dataset = None,
tail: int = 0,
):
y, y1, y0, c1, c0, match, x_name, c1_name, c0_name = _independent_measures_args(
y, x, c1, c0, match, ds, sub)
n1 = len(y1)
n = len(y)
df = n - 2
groups = np.arange(n) < n1
groups.dtype = np.int8
t = stats.t_ind(y.x[:, None], groups)[0]
self._y = dataobj_repr(y)
self._x = x_name
TTest.__init__(self, t, df, tail)
self._c1 = c1_name
self._c0 = c0_name
self._two_y = c1 is None
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):
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)
groups = ct.x == c1
groups.dtype = np.int8
t = stats.t_ind(ct.y.x[:, None], groups)[0]
TTest.__init__(self, t, n - 2, tail)
self._c1 = c1
self._c0 = c0
