class_docstr: Conduct a PP test for stationarity.
  
  In statistics, the Phillips–Perron test (named after Peter C. B.
  Phillips and Pierre Perron) is a unit root test. It is used in time series
  analysis to test the null hypothesis that a time series is integrated of
  order 1. It builds on the Dickey–Fuller test of the null hypothesis
  ``p = 0``.
  
  Parameters
  ----------
  alpha : float, optional (default=0.05)
      Level of the test
  
  lshort : bool, optional (default=True)
      Whether or not to truncate the ``l`` value in the C code.
  
  Notes
  -----
  This test is generally used indirectly via the
  :func:`pmdarima.arima.ndiffs` function, which computes the
  differencing term, ``d``.
  
  The R code allows for two types of tests: 'Z(alpha)' and 'Z(t_alpha)'.
  Since sklearn does not allow extraction of std errors from the linear
  model fit, ``t_alpha`` is much more difficult to achieve, so we do not
  allow that variant.
  
  References
  ----------
  .. [1] R's tseries PP test source code: http://bit.ly/2wbzx6V
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code: def __init__(self, alpha=0.05, lshort=True):
      super(PPTest, self).__init__(alpha=alpha)
  
      self.lshort = lshort
  
imports: from sklearn.base import BaseEstimator
  from sklearn.linear_model import LinearRegression
  from statsmodels import api
  from abc import ABCMeta, abstractmethod
  import numpy
  from ..compat.numpy import DTYPE
  from ..decorators import deprecated
  from ..utils.array import c, diff, check_endog
  from .approx import approx
  from ._arima import C_tseries_pp_sum
label_desc: Use image node_id 1 to create a new PPTest object from inherited base classes: _DifferencingStationarityTest with example: obj = PPTest(alpha, lshort)