def estimate(y, lik, K, M=None, verbose=True):
from numpy_sugar.linalg import economic_qs
from numpy import pi, var, diag
from glimix_core.glmm import GLMMExpFam
from glimix_core.lmm import LMM
from limix._data._assert import assert_likelihood
from limix._data import normalize_likelihood, conform_dataset
from limix.qtl._assert import assert_finite
from limix._display import session_block, session_line
lik = normalize_likelihood(lik)
lik_name = lik[0]
with session_block("Heritability analysis", disable=not verbose):
with session_line("Normalising input...", disable=not verbose):
data = conform_dataset(y, M=M, K=K)
y = data["y"]
M = data["M"]
K = data["K"]
assert_finite(y, M, K)
if K is not None:
# K = K / diag(K).mean()
QS = economic_qs(K)
else:
QS = None
if lik_name == "normal":
method = LMM(y.values, M.values, QS, restricted=True)
method.fit(verbose=verbose)
else:
method = GLMMExpFam(y, lik, M.values, QS, n_int=500)
method.fit(verbose=verbose, factr=1e6, pgtol=1e-3)
g = method.scale * (1 - method.delta)
e = method.scale * method.delta
if lik_name == "bernoulli":
e += pi * pi / 3
v = var(method.mean())
return g, v, e
def estimate(y_phe, lik, kin, marker_mat=None, verbose=True):
''' estimate variance components '''
lik = normalize_likelihood(lik)
lik_name = lik[0]
with session_block("Heritability analysis", disable=not verbose):
with session_line("Normalising input...", disable=not verbose):
data = conform_dataset(y_phe, M=marker_mat, K=kin)
y_phe = data["y"]
marker_mat = data["M"]
kin = data["K"]
assert_finite(y_phe, marker_mat, kin)
if kin is not None:
# K = K / diag(K).mean()
q_s = economic_qs(kin)
else:
q_s = None
if lik_name == "normal":
method = LMM(y_phe.values, marker_mat.values, q_s, restricted=True)
method.fit(verbose=verbose)
else:
method = GLMMExpFam(y_phe, lik, marker_mat.values, q_s, n_int=500)
method.fit(verbose=verbose, factr=1e6, pgtol=1e-3)
v_g = method.scale * (1 - method.delta)
v_e = method.scale * method.delta
if lik_name == "bernoulli":
v_e += pi * pi / 3
v_v = var(method.mean())
return v_g, v_v, v_e
k01 = kmaf01_pd.loc[kfilter, :]
k01 = k01.loc[:, kfilter]
kfilter = kmaf005_pd.index.isin(pc1.iloc[:, 0])
k005 = kmaf005_pd.loc[kfilter, :]
k005 = k005.loc[:, kfilter]
snps_kmaf01 = np.load("k2029_SNPs_MAF0.1_R20.5.npy")
snps_kmaf005 = np.load("k2029_SNPs_MAF0.05_R20.9.npy")
K = np.array(k01)
iscan_model = iscan(snps_kmaf01, pc1.iloc[:, 1], K=K, M=None)
from limix._data import asarray as _asarray, conform_dataset, normalize_likelihood
from limix._display import session_block
from limix.qtl._assert import assert_finite
from limix.qtl._result import IScanResultFactory
from numpy_sugar.linalg import economic_qs
from xarray import concat
from numpy import asarray, empty, ones
lik = normalize_likelihood("normal")
lik_name = lik[0]
y = pc1.iloc[:, 1]
M = np.ones((211, 2))
G = snps_kmaf01
k01 = np.array(k01)
K = k01iscan_model = iscan(G, y, K=k01, M=none)