def setColCovars(self, rank, Cn): """ set column covariances """ self.rank = rank # col covars self.Cr = covariance.lowrank(self.P, self.rank) self.Cr.setParams(1e-3 * SP.randn(self.P * self.rank)) self.Cn = Cn
def setColCovars(self,rank,Cn): """ set column covariances """ self.rank=rank # col covars self.Cr = covariance.lowrank(self.P,self.rank) self.Cr.setParams(1e-3*SP.randn(self.P*self.rank)) self.Cn = Cn
def fitSingleTraitModel(Y,XX=None,S_XX=None,U_XX=None,verbose=False): """ fit single trait model """ N,P = Y.shape RV = {} Cg = covariance.lowrank(1) Cn = covariance.lowrank(1) gp = gp2kronSum(mean(Y[:,0:1]),Cg,Cn,XX=XX,S_XX=S_XX,U_XX=U_XX) params0 = {'Cg':SP.sqrt(0.5)*SP.ones(1),'Cn':SP.sqrt(0.5)*SP.ones(1)} var = SP.zeros((P,2)) conv1 = SP.zeros(P,dtype=bool) for p in range(P): if verbose: print '.. fitting variance trait %d'%p gp.setY(Y[:,p:p+1]) conv1[p],info = OPT.opt_hyper(gp,params0,factr=1e3) var[p,0] = Cg.K()[0,0] var[p,1] = Cn.K()[0,0] RV['conv1'] = conv1 RV['varST'] = var return RV
def fitSingleTraitModel(Y, XX=None, S_XX=None, U_XX=None, verbose=False): """ fit single trait model """ N, P = Y.shape RV = {} Cg = covariance.lowrank(1) Cn = covariance.lowrank(1) gp = gp2kronSum(mean(Y[:, 0:1]), Cg, Cn, XX=XX, S_XX=S_XX, U_XX=U_XX) params0 = { 'Cg': SP.sqrt(0.5) * SP.ones(1), 'Cn': SP.sqrt(0.5) * SP.ones(1) } var = SP.zeros((P, 2)) conv1 = SP.zeros(P, dtype=bool) for p in range(P): if verbose: print '.. fitting variance trait %d' % p gp.setY(Y[:, p:p + 1]) conv1[p], info = OPT.opt_hyper(gp, params0, factr=1e3) var[p, 0] = Cg.K()[0, 0] var[p, 1] = Cn.K()[0, 0] RV['conv1'] = conv1 RV['varST'] = var return RV