:py:mod:`mosum.bottom_up`
=========================

.. py:module:: mosum.bottom_up


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


Functions
~~~~~~~~~

.. autoapisummary::

   mosum.bottom_up.multiscale_bottomUp



.. py:function:: multiscale_bottomUp(x, G=None, threshold=['critical_value', 'custom'][0], alpha=0.1, threshold_function=None, eta=0.4, do_confint=False, level=0.05, N_reps=1000)

   Multiscale MOSUM algorithm with bottom-up merging

   :param x: input data
   :type x: list
   :param G:
             vector of bandwidths; given as either integers less than `len(x)/2`,
              or numbers between `0` and `0.5` describing the moving sum bandwidths relative to `len(x)`
   :type G: int
   :param threshold: indicates which threshold should be used to determine significance.
                     By default, it is chosen from the asymptotic distribution at the given significance level 'alpha`.
                     Alternatively it is possible to parse a user-defined function with 'threshold_function'.
   :type threshold: Str
   :param alpha: numeric value for the significance level with '0 <= alpha <= 1';
                 use iff 'threshold = "critical_value"'
   :type alpha: float
   :param threshold_function:
   :type threshold_function: function
   :param eta: a positive numeric value for the minimal mutual distance of changes,
               relative to moving sum bandwidth (iff 'criterion = "eta"')
   :type eta: float
   :param do_confint: flag indicating whether to compute the confidence intervals for change points
   :type do_confint: bool
   :param level: use iff 'do_confint = True'; a numeric value ('0 <= level <= 1') with which '100(1-level)%'
                 confidence interval is generated
   :type level: float
   :param N_reps: use iff 'do.confint = True'; number of bootstrap replicates to be generated
   :type N_reps: int

   :returns: * *multiscale_cpts object containing*
             * **x** (*list*) -- input data
             * **G** (*int*) -- bandwidth vector
             * *threshold, alpha, threshold_function, eta* -- input
             * **cpts** (*ndarray*) -- estimated change point
             * **cpts_info** (*DataFrame*) -- information on change points, including detection bandwidths, asymptotic p-values, scaled jump sizes
             * **pooled_cpts** (*ndarray*) -- change point candidates
             * **do_confint** (*bool*) -- input
             * *ci* -- confidence intervals

   .. rubric:: Examples

   >>> import mosum
   >>> xx = mosum.testData("blocks")["x"]
   >>> xx_m  = mosum.multiscale_bottomUp(xx, G = [50,100])
   >>> xx_m.summary()
   >>> xx_m.print()