def setY(self, Y, standardize=False):
"""
Set phenotype matrix
Args:
Y: phenotype matrix [N, P]
standardize: if True, phenotype is standardized (zero mean, unit variance)
"""
assert Y.shape[
0] == self.N, 'VarianceDecomposition:: Incompatible shape'
assert Y.shape[
1] == self.P, 'VarianceDecomposition:: Incompatible shape'
if standardize:
Y = preprocess.standardize(Y)
#check that missing values match the current structure
assert (~(sp.isnan(Y).any(axis=1)) == self.Iok).all(
), 'VarianceDecomposition:: pattern of missing values needs to match Y given at initialization'
self.Y = Y
self.vd.setPheno(Y)
self.init = False
self.optimum = None
self.cache['Sigma'] = None
self.cache['Hessian'] = None
def setY(self,Y,standardize=False):
"""
Set phenotype matrix
Args:
Y: phenotype matrix [N, P]
standardize: if True, phenotype is standardized (zero mean, unit variance)
"""
assert Y.shape[0]==self.N, 'VarianceDecomposition:: Incompatible shape'
assert Y.shape[1]==self.P, 'VarianceDecomposition:: Incompatible shape'
if standardize:
Y=preprocess.standardize(Y)
#check that missing values match the current structure
assert (~(sp.isnan(Y).any(axis=1))==self.Iok).all(), 'VarianceDecomposition:: pattern of missing values needs to match Y given at initialization'
self.Y = Y
self.vd.setPheno(Y)
self.init = False
self.optimum = None
self.cache['Sigma'] = None
self.cache['Hessian'] = None
def __init__(self, Y, standardize=False):
"""
Args:
Y: phenotype matrix [N, P]
standardize: if True, impute missing phenotype values by mean value,
zero-mean and unit-variance phenotype (Boolean, default False)
"""
#check whether Y is a vector, if yes reshape
if (len(Y.shape) == 1):
Y = Y[:, sp.newaxis]
#create column of 1 for fixed if nothing providede
self.N = Y.shape[0]
self.P = Y.shape[1]
self.Nt = self.N * self.P
self.Iok = ~(sp.isnan(Y).any(axis=1))
#outsourced to handle missing values:
if standardize:
Y = preprocess.standardize(Y)
self.Y = Y
self.vd = limix.CVarianceDecomposition(Y)
self.n_randEffs = 0
self.n_fixedEffs = 0
self.gp = None
self.init = False
self.fast = False
self.noisPos = None
self.optimum = None
# for predictions
self.Fstar = []
self.Kstar = []
self.Ntest = None
# for multi trait models
self.trait_covar_type = []
self.rank = []
self.fixed_tc = []
self.diag = []
self.jitter = []
self.cache = {}
self.cache['Sigma'] = None
self.cache['Hessian'] = None
pass
def __init__(self,Y,standardize=False):
"""
Args:
Y: phenotype matrix [N, P]
standardize: if True, impute missing phenotype values by mean value,
zero-mean and unit-variance phenotype (Boolean, default False)
"""
#check whether Y is a vector, if yes reshape
if (len(Y.shape)==1):
Y = Y[:,sp.newaxis]
#create column of 1 for fixed if nothing providede
self.N = Y.shape[0]
self.P = Y.shape[1]
self.Nt = self.N*self.P
self.Iok = ~(sp.isnan(Y).any(axis=1))
#outsourced to handle missing values:
if standardize:
Y=preprocess.standardize(Y)
self.Y = Y
self.vd = limix.CVarianceDecomposition(Y)
self.n_randEffs = 0
self.n_fixedEffs = 0
self.gp = None
self.init = False
self.fast = False
self.noisPos = None
self.optimum = None
# for predictions
self.Fstar = []
self.Kstar = []
self.Ntest = None
# for multi trait models
self.trait_covar_type = []
self.rank = []
self.fixed_tc = []
self.diag = []
self.jitter = []
self.cache = {}
self.cache['Sigma'] = None
self.cache['Hessian'] = None
pass
def __init__(self, Y, standardize=False):
"""
Args:
Y: phenotype matrix [N, P]
standardize: if True, impute missing phenotype values by mean value,
zero-mean and unit-variance phenotype (Boolean, default False)
"""
#create column of 1 for fixed if nothing providede
self.N = Y.shape[0]
self.P = Y.shape[1]
self.Nt = self.N * self.P
self.Iok = ~(SP.isnan(Y).any(axis=1))
#outsourced to handle missing values:
if standardize:
Y = preprocess.standardize(Y)
self.Y = Y
self.vd = limix.CVarianceDecomposition(Y)
self.n_terms = 0
self.gp = None
self.init = False
self.fast = False
self.noisPos = None
self.optimum = None
# for multi trait models
self.covar_type = []
self.diag = []
self.offset = []
self.cache = {}
self.cache['Sigma'] = None
self.cache['Hessian'] = None
self.cache['Lparams'] = None
self.cache['paramsST'] = None
pass
def __init__(self,Y,standardize=False):
"""
Args:
Y: phenotype matrix [N, P]
standardize: if True, impute missing phenotype values by mean value,
zero-mean and unit-variance phenotype (Boolean, default False)
"""
#create column of 1 for fixed if nothing providede
self.N = Y.shape[0]
self.P = Y.shape[1]
self.Nt = self.N*self.P
self.Iok = ~(SP.isnan(Y).any(axis=1))
#outsourced to handle missing values:
if standardize:
Y=preprocess.standardize(Y)
self.Y = Y
self.vd = limix.CVarianceDecomposition(Y)
self.n_terms = 0
self.gp = None
self.init = False
self.fast = False
self.noisPos = None
self.optimum = None
# for multi trait models
self.covar_type = []
self.diag = []
self.offset = []
self.cache = {}
self.cache['Sigma'] = None
self.cache['Hessian'] = None
self.cache['Lparams'] = None
self.cache['paramsST']= None
pass
