def pv_adj_and_ind(self, nperm, pv_adj, nullfit, lrt, lrtperm,
alteqnull, alteqnullperm, qmax, nullfitfile, nlocalperm):
if nlocalperm>0: #don't do the fitting
ind = pv_adj.argsort()
return pv_adj, ind
from fastlmm.association.tests import Cv
return Cv.pv_adj_and_ind(nperm, pv_adj, nullfit, lrt, lrtperm,
alteqnull, alteqnullperm, qmax, nullfitfile, nlocalperm) # call the shared version of this method
def pv_adj_and_ind(self, nperm, pv_adj, nullfit, lrt, lrtperm,
alteqnull, alteqnullperm, qmax, nullfitfile, nlocalperm):
if nlocalperm>0: #don't do the fitting
ind = pv_adj.argsort()
return pv_adj, ind
from fastlmm.association.tests import Cv
return Cv.pv_adj_and_ind(nperm, pv_adj, nullfit, lrt, lrtperm,
alteqnull, alteqnullperm, qmax, nullfitfile, nlocalperm) # call the shared version of this method
def recalibrate(dirin,filepattern='*.txt', lrtpermfile=None, pnames=["P-value(50/50)"], rownames=rownames(), nullfit="qq",qmax=0.1, postfix="RECALIBRATED"):
'''
Read in each results file, use the null stats in lrtpermfile to re-calibrate the null distribution,
and then add a column to each file
'''
assert lrtpermfile is not None, "must provide lrtpermfile (output by FastLmmSet.py with lrt)"
myfiles = getfiles(dirin, filepattern)
ii=0
for f in myfiles:
ii=ii+1
print str(ii) + ") " + f
pv,rowids,llnull,llalt = extractpvals(f,pnames,rownames)
lrt = -2*(llnull-llalt)
alteqnull = (lrt==0)
pv_adj = Cv.pv_adj_and_ind(nperm=0, pv_adj=None, nullfit=nullfit, lrt=lrt, lrtperm=None,
alteqnull=alteqnull, alteqnullperm=None, qmax=qmax,
nullfitfile=lrtpermfile, nlocalperm=0, sort=False)[0]
outfile = ut.appendtofilename(f,postfix)
np.savetxt(outfile, pv_adj)