sex = sex.astype(np.bool)
individuals, parents = rel.nonzero()
fathers = np.zeros((rel.shape[0]), dtype=np.int32)
mothers = np.zeros((rel.shape[0]), dtype=np.int32)
interest = np.zeros((rel.shape[0]), dtype=np.int32)
# need to add one so 0 is not an entry
fathers[individuals[sex[parents]]] = parents[sex[parents]] + 1
mothers[individuals[~sex[parents]]] = parents[~sex[parents]] + 1
if indices is not None:
interest[indices] = True
content = np.vstack((np.zeros((rel.shape[0]), dtype=np.int32),
np.arange(rel.shape[0]) + 1,
fathers, mothers,
['2' if x else '1' for x in sex],
interest))
np.savetxt(fam_file_path, content.T, delimiter=' ', fmt='%s')
if __name__ == "__main__":
from Simulation.Pedigree import simulate_tree
rel, sex, _ = simulate_tree(10000, 0.001, 1.4, 0.9)
indices = np.array([1, 2, 3])
write_fam('temp.fam', rel, sex, indices)
rel_after, sex_after, interest_after, entries_list = read_fam('temp.fam')
assert (rel_after - rel).nnz == 0
assert np.count_nonzero(sex - sex_after) == 0
assert np.count_nonzero(interest_after - indices) == 0
factor = cholesky(V)
_, L_CT_invV_C, _, fixed_effects = compute_fixed_effects(factor, y, covariates)
fixed_effects_p_values = compute_fixed_effects_p_value(y, covariates, fixed_effects, L_CT_invV_C)
sigmas_sigmas = compute_sig_of_sig(mats, covariates, factor, y, sim_num)
return {"covariance coefficients": mats_coefficients,
"covariates coefficients": fixed_effects,
"covariance std": sigmas_sigmas,
"covariates p-values": fixed_effects_p_values}
if __name__ == "__main__":
from Simulation.Pedigree import simulate_tree
from Simulation.Phenotype import simulate_phenotype
from Matrices.Numerator import simple_numerator
from Matrices.Epistasis import pairwise_epistasis
from Matrices.Dominance import dominance
rel, _, _ = simulate_tree(50000, 0.001, 1.4, 0.9)
ibd, _, _ = simple_numerator(rel)
epis = pairwise_epistasis(ibd)
dom = dominance(rel, ibd)
cov = np.random.randn(50000, 2)
y = simulate_phenotype([ibd, epis, dom],
cov,
np.array([0.3, 0.2, 0.1, 0.4]),
np.array([0.01, 0.02, 0.03]),
True)
print(LMM(SparseCholesky(), [ibd, epis, dom], cov, y))