def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False):
Rajd = Nadjust_after = None
if self.params.Radjust == 'after':
Nadjust_after = Nref
Radj = R
elif self.params.Radjust == 'before':
Nadjust_after = None
Radj = R.adjusted_before_inversion(Nref)
else:
Nadjust_after = None
Radj = R
if self.params.RAreg:
print('regularizing RA')
RA = RA.add_ridge(self.params.Lambda, renormalize=True)
gs = GenomicSubset(self.params.region)
A = SnpSubset(self.refpanel, bedtool=gs.bedtool)
RA.zero_outside_irs(A.irs)
if not memoize or not hasattr(self, 'bias'):
print('adding lambda')
Radjreg = Radj.add_ridge(self.params.Lambda, renormalize=True)
print('computing inverse')
self.Radjreginv = Radjreg.inv(Nadjust_after=Nadjust_after)
print('done.computing bias...')
A = SnpSubset(self.refpanel,
bedtool=GenomicSubset(self.params.region).bedtool)
W = self.window(A)
if not self.params.avgunbiased:
tr = self.Radjreginv.dot(RA).trace()
self.scaling = 1
else:
tr = RA.dot(self.Radjreginv).dot(R).dot(
self.Radjreginv).trace()
Q = R.dot(self.Radjreginv).dot(RA).dot(self.Radjreginv).dot(R)
Q.zero_outside_irs(A.irs)
self.scaling = A.num_snps() / Q.trace()
# self.bias = tr / N + \
# float(self.refpanel.M-len(W.irs))/self.refpanel.M * \
# self.params.sigma2g * tr / self.params.pop_size
self.bias = tr / N + \
self.params.sigma2g * tr / self.params.pop_size
print('\nbias =', self.bias)
print('scaling =', self.scaling)
betahat = self.Radjreginv.dot(alphahat)
return self.scaling * (betahat.dot(RA.dot(betahat)) - self.bias)
def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False):
Rajd = Nadjust_after = None
if self.params.Radjust == "after":
Nadjust_after = Nref
Radj = R
elif self.params.Radjust == "before":
Nadjust_after = None
Radj = R.adjusted_before_inversion(Nref)
else:
Nadjust_after = None
Radj = R
if self.params.RAreg:
print("regularizing RA")
RA = RA.add_ridge(self.params.Lambda, renormalize=True)
gs = GenomicSubset(self.params.region)
A = SnpSubset(self.refpanel, bedtool=gs.bedtool)
RA.zero_outside_irs(A.irs)
if not memoize or not hasattr(self, "bias"):
print("adding lambda")
Radjreg = Radj.add_ridge(self.params.Lambda, renormalize=True)
print("computing inverse")
self.Radjreginv = Radjreg.inv(Nadjust_after=Nadjust_after)
print("done.computing bias...")
A = SnpSubset(self.refpanel, bedtool=GenomicSubset(self.params.region).bedtool)
W = self.window(A)
if not self.params.avgunbiased:
tr = self.Radjreginv.dot(RA).trace()
self.scaling = 1
else:
tr = RA.dot(self.Radjreginv).dot(R).dot(self.Radjreginv).trace()
Q = R.dot(self.Radjreginv).dot(RA).dot(self.Radjreginv).dot(R)
Q.zero_outside_irs(A.irs)
self.scaling = A.num_snps() / Q.trace()
# self.bias = tr / N + \
# float(self.refpanel.M-len(W.irs))/self.refpanel.M * \
# self.params.sigma2g * tr / self.params.pop_size
self.bias = tr / N + self.params.sigma2g * tr / self.params.pop_size
print("\nbias =", self.bias)
print("scaling =", self.scaling)
betahat = self.Radjreginv.dot(alphahat)
return self.scaling * (betahat.dot(RA.dot(betahat)) - self.bias)
