limix.stats.Chi2Mixture¶
-
class
limix.stats.Chi2Mixture(scale_min=0.1, scale_max=5.0, dof_min=0.1, dof_max=5.0, n_intervals=100, qmax=0.1, tol=0)[source]¶ A class for continuous random variable following a chi2 mixture.
Class for evaluation of P values for a test statistic that follows a two-component mixture of chi2
\[(1-\pi)\chi^2(0) + \pi a \chi^2(d).\]Here \(\pi\) is the probability being in the first component and \(a\) and \(d\) are the scale parameter and the number of degrees of freedom of the second component.
Parameters: - scale_min (float) – Minimum value used for fitting the scale parameter.
- scale_max (float) – Maximum value used for fitting the scale parameter.
- dofmin (float) – Minimum value used for fitting the dof parameter.
- dofmax (float) – Maximum value used for fitting the dof parameter.
- qmax (float) – Only the top qmax quantile is used for the fit.
- n_interval (int) – Number of intervals when performing gridsearch.
- tol (float) – Tolerance of being zero.
Examples
>>> from numpy.random import RandomState >>> import scipy as sp >>> from limix.stats import Chi2Mixture >>> >>> scale = 0.3 >>> dof = 2 >>> mixture = 0.2 >>> n = 100 >>> >>> random = RandomState(1) >>> x = random.chisquare(dof, n) >>> n0 = int( (1-mixture) * n) >>> idxs = random.choice(n, n0, replace=False) >>> x[idxs] = 0 >>> >>> chi2mix = Chi2Mixture(scale_min=0.1, scale_max=5.0, ... dof_min=0.1, dof_max=5.0, ... qmax=0.1, tol=4e-3) >>> chi2mix.estimate_chi2mixture(x) >>> pv = chi2mix.sf(x) >>> print(pv[:4]) [0.2 0.2 0.2 0.2] >>> >>> print('%.2f' % chi2mix.scale) 1.98 >>> print('%.2f' % chi2mix.dof) 0.89 >>> print('%.2f' % chi2mix.mixture) 0.20
-
__init__(scale_min=0.1, scale_max=5.0, dof_min=0.1, dof_max=5.0, n_intervals=100, qmax=0.1, tol=0)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([scale_min, scale_max, dof_min, …])Initialize self. estimate_chi2mixture(lrt)Estimates the parameters of a chi2 mixture. sf(lrt)Computes the p-values from test statistics lrt.