def make_incidence_matrix(individuals, effect_name):
if effect_name.lower() == 'residual':
incidence_matrix = sparseeye(len(individuals))
elif is_genetic_effect(effect_name):
incidence_matrix = sparseeye(len(individuals))
else:
levels = sorted({ind.phenotypes[effect_name] for ind in individuals})
# Missing values are not a valid level
levels = [x for x in levels if x is not None]
nlevels = len(levels)
# Calculate which individual has which level
gen = (ind.phenotypes[effect_name] == level for ind, level in
product(individuals, levels))
Z = np.fromiter(gen, dtype=np.uint8)
# Shout out to scipy for both not documenting reshape on any of their
# sparse matrix objects and also not making them take the same number
# of arguments
Z = Z.reshape(-1, nlevels)
# Check for missing values and complain about them!
# Kind of hard to read but heres how it goes:
# Check if any of the rows are all zero.
if (Z == 0).all(axis=1).any():
raise LinAlgError('Missing values in random effect')
incidence_matrix = csc_matrix(Z)
return incidence_matrix
def make_incidence_matrix(individuals, effect_name):
if effect_name.lower() == 'residual':
incidence_matrix = sparseeye(len(individuals))
elif is_genetic_effect(effect_name):
incidence_matrix = sparseeye(len(individuals))
else:
levels = sorted({ind.phenotypes[effect_name] for ind in individuals})
# Missing values are not a valid level
levels = [x for x in levels if x is not None]
nlevels = len(levels)
# Calculate which individual has which level
gen = (ind.phenotypes[effect_name] == level
for ind, level in product(individuals, levels))
Z = np.fromiter(gen, dtype=np.uint8)
# Shout out to scipy for both not documenting reshape on any of their
# sparse matrix objects and also not making them take the same number
# of arguments
Z = Z.reshape(-1, nlevels)
# Check for missing values and complain about them!
# Kind of hard to read but heres how it goes:
# Check if any of the rows are all zero.
if (Z == 0).all(axis=1).any():
raise LinAlgError('Missing values in random effect')
incidence_matrix = csc_matrix(Z)
return incidence_matrix
def __init__(self,
individuals,
label,
variance=None,
incidence_matrix=None,
covariance_matrix=None,
levels=None):
"""
Create the random effect.
:param individuals: Individuals included
:param label: name of the effect
:param variance: variance associated with the effect
:param incidence_matrix: incidence matrix for random effect
:param covariance_matrix: covariance matrix for random effect
:param levels: levels of random effect
:type individuals: iterable
:type label: string
:type variance: float
:type incidence_matrix: matrix
:type covariance_matrix: matrix
"""
nobs = len(individuals)
self.label = label
self.variance_component = variance
if isinstance(incidence_matrix, str) and incidence_matrix == 'eye':
self.incidence_matrix = sparseeye(nobs, nobs)
elif incidence_matrix is None:
self.incidence_matrix = make_incidence_matrix(
individuals, self.label)
else:
self.incidence_matrix = incidence_matrix
if covariance_matrix is None:
# Number of levels of random effects is the number of
# columns in the incidence matrix
nlevel = self.incidence_matrix.shape[1]
self.covariance_matrix = sparseeye(nlevel, nlevel)
else:
# Covariance matrices are square
if covariance_matrix.shape[0] != covariance_matrix.shape[1]:
raise LinAlgError('Covariance matrix not square')
if covariance_matrix.shape[0] != self.incidence_matrix.shape[1]:
raise LinAlgError('Incidence and covariance matrix '
'not conformable')
self.covariance_matrix = covariance_matrix
if not levels:
self.levels = [
'L{}'.format(i) for i in range(self.incidence_matrix.shape[1])
]
else:
if len(levels) != incidence_matrix.shape[1]:
raise ValueError('Number of levels not correct')
self.levels = levels
self.V_i = self.Z * self.G * self.Z.T
def __init__(self, individuals, label, variance=None,
incidence_matrix=None, covariance_matrix=None, levels=None):
"""
Create the random effect.
:param individuals: Individuals included
:param label: name of the effect
:param variance: variance associated with the effect
:param incidence_matrix: incidence matrix for random effect
:param covariance_matrix: covariance matrix for random effect
:param levels: levels of random effect
:type individuals: iterable
:type label: string
:type variance: float
:type incidence_matrix: matrix
:type covariance_matrix: matrix
"""
nobs = len(individuals)
self.label = label
self.variance_component = variance
if isinstance(incidence_matrix, str) and incidence_matrix == 'eye':
self.incidence_matrix = sparseeye(nobs, nobs)
elif incidence_matrix is None:
self.incidence_matrix = make_incidence_matrix(individuals,
self.label)
else:
self.incidence_matrix = incidence_matrix
if covariance_matrix is None:
# Number of levels of random effects is the number of
# columns in the incidence matrix
nlevel = self.incidence_matrix.shape[1]
self.covariance_matrix = sparseeye(nlevel, nlevel)
else:
# Covariance matrices are square
if covariance_matrix.shape[0] != covariance_matrix.shape[1]:
raise LinAlgError('Covariance matrix not square')
if covariance_matrix.shape[0] != self.incidence_matrix.shape[1]:
raise LinAlgError('Incidence and covariance matrix '
'not conformable')
self.covariance_matrix = covariance_matrix
if not levels:
self.levels = ['L{}'.format(i) for i in
range(self.incidence_matrix.shape[1])]
else:
if len(levels) != incidence_matrix.shape[1]:
raise ValueError('Number of levels not correct')
self.levels = levels
self.V_i = self.Z * self.G * self.Z.T
def __init__(self, individuals, label, variance=None,
incidence_matrix=None, covariance_matrix=None, levels=None):
nobs = len(individuals)
self.label = label
self.variance_component = variance
if isinstance(incidence_matrix, str) and incidence_matrix == 'eye':
self.incidence_matrix = sparseeye(nobs, nobs)
elif incidence_matrix is None:
m = make_incidence_matrix(individuals,
self.label)
self.incidence_matrix = m
else:
self.incidence_matrix = incidence_matrix
if covariance_matrix is None:
# Number of levels of random effects is the number of
# columns in the incidence matrix
nlevel = self.incidence_matrix.shape[1]
self.covariance_matrix = sparseeye(nlevel, nlevel)
else:
# Covariance matrices are square
if covariance_matrix.shape[0] != covariance_matrix.shape[1]:
raise LinAlgError('Covariance matrix not square')
if covariance_matrix.shape[0] != self.incidence_matrix.shape[1]:
raise LinAlgError('Incidence and covariance matrix '
'not conformable')
self.covariance_matrix = covariance_matrix
if not levels:
self.levels = ['L{}'.format(i) for i in range(self.incidence_matrix.shape[1])]
else:
if len(levels) != incidence_matrix.shape[1]:
raise ValueError('Number of levels not correct')
self.levels = levels
self.V_i = self.Z * self.G * self.Z.T
