hapsburg.PackagesSupport.fit_ne
===============================

.. py:module:: hapsburg.PackagesSupport.fit_ne

.. autoapi-nested-parse::

   Class the fits Ne with a MLE framework.
   Standard error either from bootstrap over individuals,
   or from likelihood curve (kind of assumping ROH are independent,
   which is not fully correct)
   @Author: Harald Ringbauer, 2020



Classes
-------

.. autoapisummary::

   hapsburg.PackagesSupport.fit_ne.MLE_ROH_Ne


Functions
---------

.. autoapisummary::

   hapsburg.PackagesSupport.fit_ne.ind_roh_from_pdf
   hapsburg.PackagesSupport.fit_ne.inds_roh_from_pdf
   hapsburg.PackagesSupport.fit_ne.load_roh_vec
   hapsburg.PackagesSupport.fit_ne.get_default_res


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

.. py:class:: MLE_ROH_Ne(endog=[], exog=[], start_params=[1000], min_len=4, max_len=20.0, error_model=False, output=False, chr_lgts=[], **kwds)

   Bases: :py:obj:`statsmodels.base.model.GenericLikelihoodModel`


   Class for MLE estimation of block sharing
   between populations with modeled error
   Bins into length blocks and models block-sharing
   between populations as indep. Poisson
   Operates in cM unit space (not Morgan!)


   .. py:attribute:: bin_width
      :value: 0.1



   .. py:attribute:: mid_bins
      :value: []



   .. py:attribute:: chr_lgts
      :value: []



   .. py:attribute:: fp_rate
      :value: []



   .. py:attribute:: theoretical_shr
      :value: []



   .. py:attribute:: trans_mat


   .. py:attribute:: full_shr_pr
      :value: []



   .. py:attribute:: density_fun
      :value: 0



   .. py:attribute:: start_params
      :value: []



   .. py:attribute:: error_model
      :value: True



   .. py:attribute:: estimates
      :value: []



   .. py:attribute:: summary
      :value: 0



   .. py:attribute:: output
      :value: True



   .. py:method:: initialize_ll_model(**kwds)

      Function to initialize LL model



   .. py:method:: set_params(recalculate_bins=False, **kwargs)

      Set the Parameters.
      Takes keyworded arguments



   .. py:method:: fit(start_params=None, maxiter=10000, maxfun=5000, **kwds)

      Fit Parameters via fit method of statsmodels



   .. py:method:: fit_ll_profile(ns=[], level=1.92, num=1000)

      Fit Parameters via likelihood profile.
      Only works for 1D search.
      Return ML, lower, upper CI values
      ns: Array of 2Ne values to test.
      level: loglikelihood difference to use for CI values.



   .. py:method:: loglikeobs(params)

      Return vector of log likelihoods for every observation. (here pairs of pops)



   .. py:method:: ll_individual(roh, params)

      Log likelihood function for every raw of data (sharing between countries).
      Return log likelihood.



   .. py:method:: create_bins()

      Creates the bins according to parameters in Class.
      Also set the false positive rate



   .. py:method:: calculate_thr_shr(params)

      Calculates the expected Bessel-Decay per bin



   .. py:method:: calculate_trans_mat()

      Calculate the transition matrix from true estimated to
      observed values for block sharing.



   .. py:method:: calculate_full_bin_prob()

      Calculate the full probablities per bin



   .. py:method:: get_bl_shr_interval(interval, r, params=[])

      Return the estimated block-sharing under the model in interval given distance r.
      For this use all the bins intersecting the interval and average.
      Assumes r is array and return array



   .. py:method:: print_block_nr()

      Calculate and print the Nr of analyzed Blocks of right Length.



   .. py:method:: ci_li(ns=[], level=1.92, update=True)

      Get confidence Intervall based on likelihood ration test.
      Return lower, upper Ne supported by
      ns: N values to test.
      level: loglikelihood difference to use.
      update: Whether to update Confidence Intervals in the fit



   .. py:method:: block_shr_density(l, params)

      Calculate and print the Nr of analyzed Blocks of right Length.



   .. py:method:: get_summ_as_df(summary)

      Transforms statsmodels summary to pandas dataframe.
      If no summary given use the last saved one
      Return that dataframe



.. py:function:: ind_roh_from_pdf(ne=500, bin_range=[0.04, 0.5], nbins=100000, output=False)

   Create ROH for single individual. Everything measured in Morgan.
   Return array of ROH lengths, in Morgan


.. py:function:: inds_roh_from_pdf(n_ind=5, ne=500, bin_range=[0.04, 0.5], nbins=100000, output=False, cm=True)

   Create ROH for single individual. Everything measured in Morgan.
   Return array of ROH lengths, in Morgan.
   cm: If true, output in centimorgan


.. py:function:: load_roh_vec(iids=[], base_path='./output/roh/', suffix='_roh_full.csv')

   Load and return ROH length vector


.. py:function:: get_default_res()

   REturn default line of results dataframe.
   Used for unfittable scenarios