# generate data
h2 = 0.3
N = 500; P = 3; S = 1000
X = 1.*(SP.rand(N,S)<0.2)
beta = SP.randn(S,P)
Yg = SP.dot(X,beta); Yg*=SP.sqrt(h2/Yg.var(0).mean())
Yn = SP.randn(N,P); Yn*=SP.sqrt((1-h2)/Yn.var(0).mean())
Y = Yg+Yn; Y-=Y.mean(0); Y/=Y.std(0)
XX = SP.dot(X,X.T)
XX/= XX.diagonal().mean()
Xr = 1.*(SP.rand(N,10)<0.2)
Xr*= SP.sqrt(N/(Xr**2).sum())
# define mean term
mu = mean(Y)
# add first fixed effect
F = 1.*(SP.rand(N,2)<0.2); A = SP.eye(P)
# add second fixed effect
F2 = 1.*(SP.rand(N,3)<0.2); A2 = SP.ones((1,P))
mu.addFixedEffect(F=F,A=A)
mu.addFixedEffect(F=F2,A=A2)
if mtSet_present:
mu1 = MEAN.mean(Y)
mu1.addFixedEffect(F=F,A=A)
mu1.addFixedEffect(F=F2,A=A2)
# generate data
h2 = 0.3
N = 1000; P = 4; S = 1000
X = 1.*(SP.rand(N,S)<0.2)
beta = SP.randn(S,P)
Yg = SP.dot(X,beta); Yg*=SP.sqrt(h2/Yg.var(0).mean())
Yn = SP.randn(N,P); Yn*=SP.sqrt((1-h2)/Yn.var(0).mean())
Y = Yg+Yn; Y-=Y.mean(0); Y/=Y.std(0)
XX = SP.dot(X,X.T)
XX/= XX.diagonal().mean()
Xr = 1.*(SP.rand(N,10)<0.2)
Xr*= SP.sqrt(N/(Xr**2).sum())
# define mean term
mean = mean(Y)
print mean.Y
# add first fixed effect
F = 1.*(SP.rand(N,2)<0.2); A = SP.eye(P)
mean.addFixedEffect(F=F,A=A)
# add first fixed effect
F = 1.*(SP.rand(N,3)<0.2); A = SP.ones((1,P))
mean.addFixedEffect(F=F,A=A)
# rotate stuff by row and cols
C = SP.cov(Y.T)
Sc,Uc = LA.eigh(C)
Sr,Ur = LA.eigh(XX)
d = SP.kron(Sc,Sr)
mean.d = d
P = 2
S = 1000
R = 1000
maf = 0.2
h2_S = 0.3
h2_R = 0.2
snps, X, Y = generate_data(N=N,
S=S,
R=R,
P=P,
h2_S=h2_S,
h2_R=h2_R,
maf=maf)
# define mean term
mu = mean(Y)
# add any fixed effect including bias term
F = 1. * (SP.rand(N, 3) < 0.2)
A = SP.eye(P)
F[:, 0] = 1.0
mu.addFixedEffect(F=F, A=A)
# add common fixed effect
F2 = 1. * (SP.rand(N, 4) < 0.2)
A2 = SP.ones((1, P))
mu.addFixedEffect(F=F2, A=A2)
# define covariance matrices
Cg = freeform(P)
Cn = freeform(P)
X = 1. * (SP.rand(N, S) < 0.2)
beta = SP.randn(S, P)
Yg = SP.dot(X, beta)
Yg *= SP.sqrt(h2 / Yg.var(0).mean())
Yn = SP.randn(N, P)
Yn *= SP.sqrt((1 - h2) / Yn.var(0).mean())
Y = Yg + Yn
Y -= Y.mean(0)
Y /= Y.std(0)
XX = SP.dot(X, X.T)
XX /= XX.diagonal().mean()
Xr = 1. * (SP.rand(N, 10) < 0.2)
Xr *= SP.sqrt(N / (Xr**2).sum())
# define mean term
mean = mean(Y)
print((mean.Y))
# add first fixed effect
F = 1. * (SP.rand(N, 2) < 0.2)
A = SP.eye(P)
mean.addFixedEffect(F=F, A=A)
# add first fixed effect
F = 1. * (SP.rand(N, 3) < 0.2)
A = SP.ones((1, P))
mean.addFixedEffect(F=F, A=A)
# rotate stuff by row and cols
C = SP.cov(Y.T)
Sc, Uc = LA.eigh(C)
Sr, Ur = LA.eigh(XX)
Yg = SP.dot(X, beta)
Yg += SP.dot(snps, beta_snp)
Yg *= SP.sqrt(h2 / Yg.var(0).mean())
Yn = SP.randn(N, P)
Yn *= SP.sqrt((1 - h2))
Y = Yg + Yn
Y -= Y.mean(0)
Y /= Y.std(0)
XX = SP.dot(X, X.T)
XX /= XX.diagonal().mean()
Xr = 1. * (SP.rand(N, 10) < 0.2)
Xr *= SP.sqrt(N / (Xr**2).sum())
# define mean term
mu = mean(Y)
# add first fixed effect
F = 1. * (SP.rand(N, 3) < 0.2)
A = SP.eye(P)
F[:, 0] = 1.0
# add second fixed effect
F2 = 1. * (SP.rand(N, 4) < 0.2)
A2 = SP.ones((1, P))
mu.addFixedEffect(F=F, A=A)
mu.addFixedEffect(F=F2, A=A2)
# define covariance matrices
Cg = freeform(P)
beta_snp = SP.randn(S,P)*10000
#beta_snp[(SP.rand(beta_snp.shape[0],beta_snp.shape[1])<0.1)]=0.0
Yg = SP.dot(X,beta);
Yg += SP.dot(snps,beta_snp);
Yg*=SP.sqrt(h2/Yg.var(0).mean())
Yn = SP.randn(N,P); Yn*=SP.sqrt((1-h2))
Y = Yg+Yn; Y-=Y.mean(0); Y/=Y.std(0)
XX = SP.dot(X,X.T)
XX/= XX.diagonal().mean()
Xr = 1.*(SP.rand(N,10)<0.2)
Xr*= SP.sqrt(N/(Xr**2).sum())
# define mean term
mu = mean(Y)
# add first fixed effect
F = 1.*(SP.rand(N,3)<0.2); A = SP.eye(P)
F[:,0]=1.0
# add second fixed effect
F2 = 1.*(SP.rand(N,4)<0.2); A2 = SP.ones((1,P))
mu.addFixedEffect(F=F,A=A)
mu.addFixedEffect(F=F2,A=A2)
# define covariance matrices
Cg = freeform(P)
Cn = freeform(P)
