:py:mod:`spatialtis_core.spatial_de.somde.util`
===============================================

.. py:module:: spatialtis_core.spatial_de.somde.util


Module Contents
---------------

.. py:function:: LL(delta, UTy, UT1, S, n, Yvar=None)

   Log-likelihood of GP model as a function of delta.

   The parameter delta is the ratio s2_e / s2_t, where s2_e is the
   observation noise and s2_t is the noise explained by covariance
   in time or space.


.. py:function:: SE_kernel(X, l)


.. py:function:: brent_max_LL(UTy, UT1, S, n)


.. py:function:: const_fits(exp_tab)

   Get maximum LL for const model



.. py:function:: cosine_kernel(X, p)

   Periodic kernel as l -> oo in [Lloyd et al 2014]

   Easier interpretable composability with SE?


.. py:function:: dyn_de(X, exp_tab, kernel_space=None)


.. py:function:: factor(K)


.. py:function:: get_UT1(U)


.. py:function:: get_UTy(U, y)


.. py:function:: get_l_limits(X)


.. py:function:: get_mll_results(results, null_model='const')


.. py:function:: gower_scaling_factor(K)

   Gower normalization factor for covariance matric K

   Based on https://github.com/PMBio/limix/blob/master/limix/utils/preprocess.py


.. py:function:: lbfgsb_max_LL(UTy, UT1, S, n, Yvar=None)


.. py:function:: lengthscale_fits(exp_tab, U, UT1, S, Gower, num=64)

   Fit GPs after pre-processing for particular lengthscale



.. py:function:: linear_kernel(X)


.. py:function:: logdelta_prior_lpdf(log_delta)


.. py:function:: make_FSV(UTy, S, n, Gower)


.. py:function:: make_objective(UTy, UT1, S, n, Yvar=None)


.. py:function:: mu_hat(delta, UTy, UT1, S, n, Yvar=None)

   ML Estimate of bias mu, function of delta.



.. py:function:: null_fits(exp_tab)

   Get maximum LL for null model



.. py:function:: qvalue(pv, pi0=None)


.. py:function:: regress_out(sample_info, expression_matrix, covariate_formula, design_formula='1', rcond=-1)


.. py:function:: s2_t_hat(delta, UTy, S, n, Yvar=None)

   ML Estimate of structured noise, function of delta



.. py:function:: search_max_LL(UTy, UT1, S, n, num=32)

   Search for delta which maximizes log likelihood.



.. py:function:: simulate_const_model(MLL_params, N)


.. py:function:: stabilize(expression_matrix)