hamsta.core.HAMSTA.fit#

HAMSTA.fit(S, intercept_design, N, Z=None, rotated_Z=None, U=None, M=None, constraints={}, residual_var=1.0, jackknife=False, num_blocks=10, complete=True, est_thres=False)#

Fit to compute likelihood and MLE

Parameters

  • Z (Optional[ndarray]) – signed test statistics of shape (M, )

  • rotated_Z (Optional[ndarray]) – test statistics after rotation of shape S.shape

  • U (Optional[ndarray]) – the matrix U from SVD results of A = USV’

  • S (ndarray) – the matrix S from SVD results of A = USV’

  • M (Optional[int]) – Number of markers

  • constraints (dict) – constraints applied in the optimization

  • residual_var (float) – variance of the residual in admixture mapping (default: 1)

  • jackknife (bool) – If true, compute the jackknife standard error

  • num_blocks (int) – Number of jackknife blocks

  • complete (bool) – if False, only skip all tests and compute only estimates

  • est_thres (Union[bool, float]) – If float, estimate significant threshold at family-wise error rate equal the float value. If true, assume FWER=0.05. If false, skip significant threshold estimation.