def test_vardec_multitrait():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y0 = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
y1 = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
Y = stack((y0, y1), axis=1)
vardec = VarDec(Y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(
vardec.covariance[0].scale,
[
[0.7051133960584494, 0.18484625482083472],
[0.18484625482083472, 0.048457649666404354],
],
rtol=1e-4,
)
assert_allclose(
vardec.covariance[1].scale,
[
[1.4901383238452581e-05, 1.78191074575448e-05],
[1.78191074575448e-05, 1.4592715995489043],
],
atol=1e-6,
rtol=1e-4,
)
assert_allclose(vardec.lml(), -17.39609607331073)
assert_allclose(
vardec.effsizes,
[
[-0.6356567160957514, 0.06692298344869516],
[-0.3888579153976617, -1.5350512471600464],
[-0.13499034585739983, 0.08961009904531193],
],
rtol=1e-4,
)
def test_qtl_scan_three_hypotheses_mt():
random = RandomState(0)
n = 30
ntraits = 2
ncovariates = 3
A = random.randn(ntraits, ntraits)
A = A @ A.T
M = random.randn(n, ncovariates)
C0 = random.randn(ntraits, ntraits)
C0 = C0 @ C0.T
C1 = random.randn(ntraits, ntraits)
C1 = C1 @ C1.T
G = random.randn(n, 4)
A0 = random.randn(ntraits, 1)
A1 = random.randn(ntraits, 2)
A01 = concatenate((A0, A1), axis=1)
K = random.randn(n, n + 1)
K = normalise_covariance(K @ K.T)
beta = vec(random.randn(ntraits, ncovariates))
alpha = vec(random.randn(A01.shape[1], G.shape[1]))
m = kron(A, M) @ beta + kron(A01, G) @ alpha
Y = unvec(mvn(random, m, kron(C0, K) + kron(C1, eye(n))), (n, -1))
idx = [[0, 1], 2, [3]]
r = scan(G, Y, idx=idx, K=K, M=M, A=A, A0=A0, A1=A1, verbose=False)
str(r)
def test_vardec_2_matrices():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples),
K) + random.randn(nsamples)
vardec = VarDec(y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.32199728815536727, rtol=1e-5)
assert_allclose(vardec.covariance[1].scale, 1.4182987383374532, rtol=1e-5)
assert_allclose(vardec.lml(), -33.63946372828994, rtol=1e-5)
def test_vardec_poisson_2_matrices():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "poisson"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
y = exp((y - y.mean()) / y.std())
vardec = VarDec(y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.10726852397002325, atol=1e-5)
assert_allclose(vardec.covariance[1].scale,
4.168569936272955e-11,
atol=1e-5)
assert_allclose(vardec.lml(), -26.36419072811823)
def test_vardec():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K0 = random.randn(nsamples, 10)
K0 = K0 @ K0.T
K0 /= K0.diagonal().mean()
K0 += eye(nsamples) * 1e-4
K1 = random.randn(nsamples, 10)
K1 = K1 @ K1.T
K1 /= K1.diagonal().mean()
K1 += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K0)
y += mvn(random, zeros(nsamples), K1)
vardec = VarDec(y, lik, X)
vardec.append(K0)
vardec.append(K1)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.38473522809891697)
assert_allclose(vardec.covariance[1].scale, 1.1839796169221422)
assert_allclose(vardec.covariance[2].scale,
2.061153622438558e-09,
atol=1e-5)
assert_allclose(vardec.lml(), -21.91827344966165)
assert_allclose(
vardec.effsizes,
[-0.5008873352111712, -1.193536406235688, -0.28254298530554534])
def test_vardec_poisson():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "poisson"
K0 = random.randn(nsamples, 10)
K0 = K0 @ K0.T
K0 /= K0.diagonal().mean()
K0 += eye(nsamples) * 1e-4
K1 = random.randn(nsamples, 10)
K1 = K1 @ K1.T
K1 /= K1.diagonal().mean()
K1 += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K0)
y += mvn(random, zeros(nsamples), K1)
y = exp((y - y.mean()) / y.std())
vardec = VarDec(y, lik, X)
vardec.append(K0)
vardec.append(K1)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale,
2.808478303826397e-09,
atol=1e-5)
assert_allclose(vardec.covariance[1].scale, 0.3503800007985209)
assert_allclose(vardec.covariance[2].scale,
2.061153622438558e-09,
atol=1e-5)
assert_allclose(vardec.lml(), -28.887285796984564)
def _test_qtl_scan_st(lik):
random = RandomState(0)
n = 30
ncovariates = 3
M = random.randn(n, ncovariates)
v0 = random.rand()
v1 = random.rand()
G = random.randn(n, 4)
K = random.randn(n, n + 1)
K = normalise_covariance(K @ K.T)
beta = random.randn(ncovariates)
alpha = random.randn(G.shape[1])
m = M @ beta + G @ alpha
y = mvn(random, m, v0 * K + v1 * eye(n))
idx = [[0, 1], 2, [3]]
if lik == "poisson":
y = random.poisson(exp(y))
elif lik == "bernoulli":
y = random.binomial(1, 1 / (1 + exp(-y)))
elif lik == "probit":
y = random.binomial(1, st.norm.cdf(y))
elif lik == "binomial":
ntrials = random.randint(0, 30, len(y))
y = random.binomial(ntrials, 1 / (1 + exp(-y)))
lik = (lik, ntrials)
r = scan(G, y, lik=lik, idx=idx, K=K, M=M, verbose=False)
str(r)
str(r.stats.head())
str(r.effsizes["h2"].head())
str(r.h0.trait)
str(r.h0.likelihood)
str(r.h0.lml)
str(r.h0.effsizes)
str(r.h0.variances)
def test_qtl_scan_two_hypotheses_mt_A0A1_none():
random = RandomState(0)
n = 30
ntraits = 2
ncovariates = 3
A = random.randn(ntraits, ntraits)
A = A @ A.T
M = random.randn(n, ncovariates)
C0 = random.randn(ntraits, ntraits)
C0 = C0 @ C0.T
C1 = random.randn(ntraits, ntraits)
C1 = C1 @ C1.T
G = random.randn(n, 4)
A1 = eye(ntraits)
K = random.randn(n, n + 1)
K = normalise_covariance(K @ K.T)
beta = vec(random.randn(ntraits, ncovariates))
alpha = vec(random.randn(A1.shape[1], G.shape[1]))
m = kron(A, M) @ beta + kron(A1, G) @ alpha
Y = unvec(mvn(random, m, kron(C0, K) + kron(C1, eye(n))), (n, -1))
Y = DataArray(Y, dims=["sample", "trait"], coords={"trait": ["WA", "Cx"]})
idx = [[0, 1], 2, [3]]
r = scan(G, Y, idx=idx, K=K, M=M, A=A, verbose=False)
df = r.effsizes["h2"]
df = df[df["test"] == 0]
assert_array_equal(df["trait"], ["WA"] * 3 + ["Cx"] * 3 + [None] * 4)
assert_array_equal(
df["env"], [None] * 6 + ["env1_WA", "env1_WA", "env1_Cx", "env1_Cx"]
)
str(r)