def __init__(
self,
y,
w,
transform="R",
permutations=PERMUTATIONS,
star=False,
keep_simulations=True,
):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
self.w = w
self.w_original = w.transform
self.w.transform = self.w_transform = transform.lower()
self.permutations = permutations
self.star = star
self.calc()
self.p_norm = np.array([1 - stats.norm.cdf(np.abs(i)) for i in self.Zs])
if permutations:
self.__crand(keep_simulations)
if keep_simulations:
self.sim = sim = self.rGs.T
self.EG_sim = sim.mean(axis=0)
self.seG_sim = sim.std(axis=0)
self.VG_sim = self.seG_sim * self.seG_sim
self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
def __init__(self, y, w, permutations=PERMUTATIONS):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w.transform = "B"
self.w = w
self.permutations = permutations
self.__moments()
self.y2 = y * y
y = y.reshape(
len(y), 1
) # Ensure that y is an n by 1 vector, otherwise y*y.T == y*y
self.den_sum = (y * y.T).sum() - (y * y).sum()
self.G = self.__calc(self.y)
self.z_norm = (self.G - self.EG) / np.sqrt(self.VG)
self.p_norm = 1.0 - stats.norm.cdf(np.abs(self.z_norm))
if permutations:
sim = [
self.__calc(np.random.permutation(self.y)) for i in range(permutations)
]
self.sim = sim = np.array(sim)
above = sim >= self.G
larger = sum(above)
if (self.permutations - larger) < larger:
larger = self.permutations - larger
self.p_sim = (larger + 1.0) / (permutations + 1.0)
self.EG_sim = sum(sim) / permutations
self.seG_sim = sim.std()
self.VG_sim = self.seG_sim ** 2
self.z_sim = (self.G - self.EG_sim) / self.seG_sim
self.p_z_sim = 1.0 - stats.norm.cdf(np.abs(self.z_sim))
def __init__(self,
y,
w,
transform='R',
permutations=PERMUTATIONS,
star=False):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
self.w = w
self.w_original = w.transform
self.w.transform = self.w_transform = transform.lower()
self.permutations = permutations
self.star = star
self.calc()
self.p_norm = np.array(
[1 - stats.norm.cdf(np.abs(i)) for i in self.Zs])
if permutations:
self.__crand()
sim = np.transpose(self.rGs)
above = sim >= self.Gs
larger = sum(above)
low_extreme = (self.permutations - larger) < larger
larger[low_extreme] = self.permutations - larger[low_extreme]
self.p_sim = (larger + 1.0) / (permutations + 1)
self.sim = sim
self.EG_sim = sim.mean()
self.seG_sim = sim.std()
self.VG_sim = self.seG_sim * self.seG_sim
self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
def __init__(
self,
y,
w,
transform="R",
permutations=PERMUTATIONS,
star=False,
keep_simulations=True,
n_jobs=-1,
seed=None,
island_weight=0,
):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w, star = _infer_star_and_structure_w(w, star, transform)
w.transform = transform
self.w_transform = transform
self.w = w
self.permutations = permutations
self.star = star
self.calc()
self.p_norm = 1 - stats.norm.cdf(np.abs(self.Zs))
if permutations:
self.p_sim, self.rGs = _crand_plus(
y,
w,
self.Gs,
permutations,
keep_simulations,
n_jobs=n_jobs,
stat_func=_g_local_star_crand if star else _g_local_crand,
scaling=y.sum(),
seed=seed,
island_weight=island_weight,
)
if keep_simulations:
self.sim = sim = self.rGs.T
self.EG_sim = sim.mean(axis=0)
self.seG_sim = sim.std(axis=0)
self.VG_sim = self.seG_sim * self.seG_sim
self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))