def test_lmm_scan_interface():
y = array([-1.0449132, 1.15229426, 0.79595129])
low_rank_K = array([[5.0, 14.0, 23.0], [14.0, 50.0, 86.0], [23.0, 86.0, 149.0]])
QS = economic_qs(low_rank_K)
X = ones((3, 1))
y[0] = nan
with pytest.raises(ValueError):
FastScanner(y, X, QS, 0.5)
y[0] = inf
with pytest.raises(ValueError):
FastScanner(y, X, QS, 0.5)
y[0] = 1
X[0, 0] = nan
with pytest.raises(ValueError):
FastScanner(y, X, QS, 0.5)
y[0] = 1
X[0, 0] = 1
with pytest.raises(ValueError):
FastScanner(y, X, QS, -1)
with pytest.raises(ValueError):
FastScanner(y, X, QS, nan)
def test_lmm_kron_scan_with_lmm():
random = RandomState(0)
n = 15
Y = random.randn(n, 3)
A = random.randn(3, 3)
A = A @ A.T
F = random.randn(n, 2)
G = random.randn(n, 6)
klmm = Kron2Sum(Y, A, F, G, restricted=True)
klmm.fit(verbose=False)
kscan = klmm.get_fast_scanner()
K = klmm.covariance()
X = kron(A, F)
QS = economic_qs(K)
scan = FastScanner(vec(Y), X, QS, 0.0)
assert_allclose(klmm.covariance(), K)
assert_allclose(kscan.null_scale, scan.null_scale)
assert_allclose(kscan.null_beta, scan.null_beta)
assert_allclose(kscan.null_lml(), scan.null_lml())
assert_allclose(kscan.null_beta_covariance, scan.null_beta_covariance)
A1 = random.randn(3, 2)
F1 = random.randn(n, 2)
M = kron(A1, F1)
kr = kscan.scan(A1, F1)
r = scan.scan(M)
assert_allclose(kr["lml"], r["lml"])
assert_allclose(kr["scale"], r["scale"])
assert_allclose(vec(kr["effsizes0"]), r["effsizes0"])
assert_allclose(vec(kr["effsizes1"]), r["effsizes1"])
assert_allclose(vec(kr["effsizes0_se"]), r["effsizes0_se"])
assert_allclose(vec(kr["effsizes1_se"]), r["effsizes1_se"])
def test_lmm_scan_very_low_rank():
y = array([-1.0449132, 1.15229426, 0.79595129, 2.1])
X = array([
[-0.40592765, 1.04348945],
[0.92275415, -0.32394197],
[-0.98197991, 1.22912219],
[-1.0007991, 2.22912219],
])
G = array([
[-0.14505449, -1.1000817],
[0.45714984, 1.82214436],
[-1.23763742, 1.38771103],
[-2.27377329, 0.9577192],
])
K = G @ G.T
low_rank_QS = economic_qs(K)
M = array([
[0.88766985, -1.80940339],
[0.00822629, -0.4488265],
[0.55807272, -2.00868376],
[3.2, 2.1],
])
scanner = FastScanner(y, X, low_rank_QS, 0)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [46.512791016862764, 46.512791016862764],
atol=1e-6,
rtol=1e-6)
assert_allclose(
r["effsizes0"],
[
[3.8616635463341358, 0.43233789455471455],
[4.534162667593971, 3.573393734139044],
],
)
assert_allclose(r["effsizes1"], [2.1553245206596263, -0.684698367443129],
atol=1e-6,
rtol=1e-6)
assert_allclose(r["scale"],
[5.551115123125783e-17, 2.5326962749261384e-16],
atol=1e-6)
X = ones((4, 1))
M = array([
[0.88766985, -1.80940339],
[0.00822629, -0.4488265],
[0.55807272, -2.00868376],
[3.2, 2.1],
])
scanner = FastScanner(y, X, low_rank_QS, 0.75)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [-3.988506684733393, -2.852200552237104])
assert_allclose(r["effsizes0"],
[[-0.4955288599792398], [0.36297469139979893]])
assert_allclose(r["effsizes1"], [0.5929013274071214, 0.36216887594630626])
assert_allclose(r["scale"], [0.18324637118292808, 0.10382205995195082],
atol=1e-6)
def test_lmm_scan_difficult_settings_multicovariates():
y = array([-1.0449132, 1.15229426, 0.79595129])
low_rank_K = array([[5.0, 14.0, 23.0], [14.0, 50.0, 86.0], [23.0, 86.0, 149.0]])
full_rank_K = array([[6.0, 14.0, 23.0], [14.0, 51.0, 86.0], [23.0, 86.0, 150.0]])
low_rank_QS = economic_qs(low_rank_K)
full_rank_QS = economic_qs(full_rank_K)
X = array(
[
[-0.40592765, 1.04348945],
[0.92275415, -0.32394197],
[-0.98197991, 1.22912219],
]
)
M = array(
[[0.88766985, -1.80940339], [0.00822629, -0.4488265], [0.55807272, -2.00868376]]
)
scanner = FastScanner(y, X, low_rank_QS, 0)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [28.896466, 28.896466], atol=1e-6, rtol=1e-6)
assert_allclose(
r["effsizes0"],
[
[-2.247703714834354, 0.8190282920748556],
[-3.1760541039273127, 0.40265319454969883],
],
)
assert_allclose(r["effsizes1"], [-1.195493, 0.318417], atol=1e-6, rtol=1e-6)
assert_allclose(
r["scale"], [1.850371707708594e-17, 1.3877787807814457e-17], atol=1e-6
)
X = ones((3, 1))
M = array(
[[0.88766985, -1.80940339], [0.00822629, -0.4488265], [0.55807272, -2.00868376]]
)
scanner = FastScanner(y, X, low_rank_QS, 0.75)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [0.823505300549, -0.607251148058])
assert_allclose(r["effsizes0"], [[0.3495610486128161], [0.21723441738440089]])
assert_allclose(r["effsizes1"], [-1.79486991632, 0.872439001906])
assert_allclose(r["scale"], [0.00476137752581085, 0.012358877799161524], atol=1e-6)
M = array([[0.0, -1.80940339], [0.0, -0.4488265], [0.0, -2.00868376]])
scanner = FastScanner(y, X, low_rank_QS, 0)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [8.704135, 28.896466])
assert_allclose(r["effsizes0"], [[-0.9261322100000001], [-12.837513658497771]])
assert_allclose(r["effsizes1"][1], -9.23756057859)
assert_allclose(r["scale"], [0.010459203921428906, 0.0], atol=1e-6)
M = array([[0.0, -1.80940339], [0.0, -0.4488265], [0.0, -2.00868376]])
scanner = FastScanner(y, X, low_rank_QS, 0.75)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [-6.137441, -0.607251], atol=1e-6, rtol=1e-6)
assert_allclose(r["effsizes0"], [[-0.9093206621461191], [0.21723441738440089]])
assert_allclose(r["effsizes1"][1], 0.872439001906)
assert_allclose(r["scale"], [0.49332480112228455, 0.012358877799161524], atol=1e-6)
M = array(
[[0.88766985, -1.80940339], [0.00822629, -0.4488265], [0.55807272, -2.00868376]]
)
scanner = FastScanner(y, X, full_rank_QS, 0)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [0.636421389625, -0.795311239268])
assert_allclose(r["effsizes1"], [-1.79757295968, 0.871806031702])
assert_allclose(r["scale"], [0.004736354793149378, 0.012301930830306581], atol=1e-6)
M = array(
[[0.88766985, -1.80940339], [0.00822629, -0.4488265], [0.55807272, -2.00868376]]
)
scanner = FastScanner(y, X, full_rank_QS, 0.75)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [0.251547678451, -1.18305656511])
assert_allclose(r["effsizes0"], [[0.3717198167493795], [0.2377661184233066]])
assert_allclose(r["effsizes1"], [-1.80551427016, 0.86994164292])
assert_allclose(r["scale"], [0.004662840142176661, 0.012134195778553977], atol=1e-6)
M = array([[0.0, -1.80940339], [0.0, -0.4488265], [0.0, -2.00868376]])
scanner = FastScanner(y, X, full_rank_QS, 0)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [-5.911136, -0.795311], atol=1e-6, rtol=1e-6)
assert_allclose(r["effsizes0"], [[-0.9038187010303032], [0.2224382967889082]])
assert_allclose(r["effsizes1"][1], 0.871806031702)
assert_allclose(r["scale"], [0.3725256920525814, 0.012301930830306581], atol=1e-6)
M = array([[0.0, -1.80940339], [0.0, -0.4488265], [0.0, -2.00868376]])
scanner = FastScanner(y, X, full_rank_QS, 0.75)
r = scanner.fast_scan(M, verbose=False)
assert_allclose(r["lml"], [-5.509792, -1.183057], atol=1e-6, rtol=1e-6)
assert_allclose(r["effsizes0"], [[-0.8876088873393126], [0.2377661184233066]])
assert_allclose(r["effsizes1"], [0.0, 0.8699416429204303])
assert_allclose(r["scale"], [0.21713469123194049, 0.012134195778553977], atol=1e-6)
def _test_fast_scanner_effsizes_se(K0, QS, v):
X = ones((3, 1))
y = array([-1.0449132, 1.15229426, 0.79595129])
M = array([[0.887669, -1.809403], [0.008226, -0.44882], [0.558072, -2.008683]])
scanner = FastScanner(y, X, QS, v)
r = scanner.fast_scan(M, verbose=False)
for i in range(2):
K = r["scale"][i] * (K0 + v * eye(3))
XM = concatenate((X, M[:, i : (i + 1)]), axis=1)
effsizes_se = sqrt(abs(pinv(XM.T @ solve(K, XM)).diagonal()))
se = concatenate((r["effsizes0_se"][i], r["effsizes1_se"][i : (i + 1)]))
assert_allclose(se, effsizes_se, atol=1e-5)
# Redundant covariates
M = array([[0, 1], [0, 1], [0, 1]])
scanner = FastScanner(y, X, QS, v)
r = scanner.fast_scan(M, verbose=False)
for i in range(2):
K = r["scale"][i] * (K0 + v * eye(3))
XM = concatenate((X, M[:, i : (i + 1)]), axis=1)
effsizes_se = sqrt(abs(pinv(XM.T @ solve(K, XM)).diagonal()))
se = concatenate((r["effsizes0_se"][i], r["effsizes1_se"][i : (i + 1)]))
assert_allclose(se, effsizes_se, atol=1e-5)
M = array([[0.887669, -1.809403], [0.008226, -0.44882], [0.558072, -2.008683]])
scanner = FastScanner(y, X, QS, v)
r = scanner.scan(M)
K = r["scale"] * (K0 + v * eye(3))
XM = concatenate((X, M), axis=1)
effsizes_se = sqrt(abs(pinv(XM.T @ solve(K, XM)).diagonal()))
se = concatenate((r["effsizes0_se"], r["effsizes1_se"]))
assert_allclose(se, effsizes_se, atol=1e-5)
# Redundant covariates
M = array([[0, 1], [0, 1], [0, 1]])
scanner = FastScanner(y, X, QS, v)
r = scanner.scan(M)
K = r["scale"] * (K0 + v * eye(3))
XM = concatenate((X, M), axis=1)
effsizes_se = sqrt(abs(pinv(XM.T @ solve(K, XM)).diagonal()))
se = concatenate((r["effsizes0_se"], r["effsizes1_se"]))
assert_allclose(se, effsizes_se, atol=1e-5)