def setUp(self):
sp.random.seed(1)
# generate data
n = 10
f = 2
X = 1. * (sp.rand(n, f) < 0.2)
X -= X.mean(0)
X /= X.std(0)
kinship = sp.dot(X, X.T)
kinship /= kinship.diagonal().mean()
design = sp.zeros((n, n))
for i in range(n // 2):
design[2 * i, 2 * i + 1] = 1
design[2 * i + 1, 2 * i] = 1
# define covariance
self.C = DirIndirCov(kinship, design)
self.C.setRandomParams()
def VD(self, DGE, IGE, IEE, cageEffect):
""" defines covariance for variance decomposition."""
#defines mean
mean = lin_mean(self.pheno,self.covs)
#define cagemate assignment - required for SGE, SEE, and cage effects. Z is N focal x N_cm and has 0s in cells Z_i,i (i.e. an animal is not its own cage mate)
same_cage = 1. * (self.cage==self.cage_cm)
diff_inds = 1. * (self.sampleID[:,sp.newaxis]!=self.sampleID_cm)
Z = same_cage * diff_inds
#define the overall genetic covariance matrix
if DGE or IGE:
#scales kinship (DGE component) to sample variance 1
sf_K = covar_rescaling_factor(self.kinship)
self.kinship *= sf_K
#now create and scale SGE and DGE/SGE covariance components
if IGE:
#first SGE component: ZKcmZ' in this code (ZKZ' in paper)
_ZKcmZ = sp.dot(Z,sp.dot(self.kinship_cm,Z.T))
sf_ZKcmZ = covar_rescaling_factor(_ZKcmZ)
self.kinship_cm *= sf_ZKcmZ
#second DGE/SGE covariance:
self.kinship_cross *= sp.sqrt(sf_K * sf_ZKcmZ)
if DGE and not IGE:
self._genoCov = FixedCov(self.kinship)
elif IGE and not DGE:
self._genoCov = FixedCov(_ZKcmZ)
elif DGE and IGE:
self._genoCov = DirIndirCov(self.kinship,Z,kinship_cm=self.kinship_cm,kinship_cross=self.kinship_cross)
else:
self._genoCov = None
#define the overall environmental covariance matrix
#there is always DEE
#env naturally has sample variance 1 so no need to scale it
if IEE:
#_ZZ = ZIcmZ'
_ZZ = sp.dot(Z,Z.T)
sf_ZZ = covar_rescaling_factor(_ZZ)
self.env_cm *= sf_ZZ
self.env_cross *= sp.sqrt(1 * sf_ZZ)
self._envCov = DirIndirCov(self.env,Z,kinship_cm=self.env_cm,kinship_cross=self.env_cross)
else:
self._envCov = FixedCov(self.env)
##define cage effect covariance matrix
if cageEffect:
N = self.pheno.shape[0]
uCage = sp.unique(self.cage)
#W, the cage design matrix, is N x n_cages (where N is number of focal animals)
W = sp.zeros((N,uCage.shape[0]))
for cv_i, cv in enumerate(uCage):
W[:,cv_i] = 1.*(self.cage[:,0]==cv)
#WW, the cage effect covariance matrix, is N x N and has 1s in cells WW_i,i
WW = sp.dot(W,W.T)
#this is equivalent to getting covar_rescaling_factor first and then multiplying, as done for other matrices above
WW = covar_rescale(WW)
self._cageCov = FixedCov(WW)
else:
self._cageCov = None
# define overall covariance matrix as sum of genetic, environmental and cage covariance matrices
if self._genoCov is None:
if self._cageCov is None:
self.covar = SumCov(self._envCov)
else:
self.covar = SumCov(self._envCov,self._cageCov)
else:
if self._cageCov is None:
self.covar = SumCov(self._genoCov,self._envCov)
else:
self.covar = SumCov(self._genoCov,self._envCov,self._cageCov)
## define gp
self._gp = GP(covar=self.covar,mean=mean)
