def construct(self,
Y,
X=None,
forcefullrank=False,
SNPs0=None,
i_exclude=None,
nullModel=None,
altModel=None,
scoring=None,
greater_is_better=None):
'''
The same code gets executed for both linear and logistic, because the logistic is an approximation.
'''
assert nullModel['effect'] == 'mixed' and altModel['effect'] == 'mixed',\
'You have not used mixed effects for the two kernel case.'
assert nullModel['link'] == 'linear'
assert altModel['link'] == 'linear'
if self.score == 'mom':
assert nullModel['link'] == 'linear' and altModel['link'] == 'linear', 'You are allowed to use '\
'only the linear link for sc mom test.'
G0, K0 = tu.set_snps0(SNPs0=SNPs0,
sample_size=Y.shape[0],
i_exclude=i_exclude,
forcefullrank=forcefullrank)
return score.scoretest2K(Y=Y[:, SP.newaxis], X=X, K=K0, G0=G0)
def construct(self, Y, X=None, forcefullrank = False, SNPs0 = None, i_exclude = None, nullModel = None, altModel = None,
scoring = None, greater_is_better = None):
'''
The same code gets executed for both linear and logistic, because the logistic is an approximation.
'''
assert nullModel['effect'] == 'mixed' and altModel['effect'] == 'mixed',\
'You have not used mixed effects for the two kernel case.'
assert nullModel['link'] == 'linear'
assert altModel['link'] == 'linear'
if self.score == 'mom':
assert nullModel['link'] == 'linear' and altModel['link'] == 'linear', 'You are allowed to use '\
'only the linear link for sc mom test.'
G0,K0=tu.set_snps0(SNPs0=SNPs0,sample_size=Y.shape[0],i_exclude=i_exclude,forcefullrank=forcefullrank)
return score.scoretest2K(Y=Y[:,SP.newaxis],X=X,K=K0,G0=G0)
def construct(self,
Y,
X=None,
forcefullrank=False,
SNPs0=None,
i_exclude=None,
nullModel=None,
altModel=None,
scoring=None,
greater_is_better=None):
G0, K0 = tu.set_snps0(SNPs0=SNPs0,
sample_size=Y.shape[0],
i_exclude=i_exclude)
print "constructing LMM - this should only happen once."
return lrt(Y,
X=X,
forcefullrank=forcefullrank,
G0=G0,
K0=K0,
nullModel=nullModel,
altModel=altModel)
def construct(self,
Y,
X=None,
forcefullrank=False,
SNPs0=None,
i_exclude=None,
nullModel=None,
altModel=None,
scoring=None,
greater_is_better=None):
G0, K0 = tu.set_snps0(SNPs0=SNPs0,
sample_size=Y.shape[0],
i_exclude=i_exclude)
return lr.lrt(Y=Y,
X=X,
model0=None,
appendbias=False,
forcefullrank=forcefullrank,
G0=G0,
K0=K0,
nullModel=nullModel,
altModel=altModel)
def construct(self, Y, X=None, forcefullrank = False, SNPs0 = None, i_exclude = None, nullModel = None, altModel = None,
scoring = None, greater_is_better = None):
G0,K0=tu.set_snps0(SNPs0=SNPs0,sample_size=Y.shape[0],i_exclude=i_exclude)
return lr.lrt(Y=Y, X=X, model0=None, appendbias=False, forcefullrank=forcefullrank, G0=G0,K0=K0, nullModel=nullModel, altModel=altModel)
def construct(self, Y, X=None, forcefullrank = False, SNPs0 = None, i_exclude=None, nullModel = None, altModel = None,
scoring = None, greater_is_better = None):
G0,K0=tu.set_snps0(SNPs0=SNPs0,sample_size=Y.shape[0],i_exclude=i_exclude)
print "constructing LMM - this should only happen once."
return lrt(Y, X=X, forcefullrank=forcefullrank, G0=G0, K0=K0, nullModel=nullModel,altModel=altModel)
