# scipy.special.nbdtrik

scipy.special.nbdtrik(y, n, p, out=None) = <ufunc 'nbdtrik'>

  Negative binomial percentile function.

  Returns the inverse with respect to the parameter k of y = nbdtr(k, n, p), the negative binomial cumulative distribution function.

  Parameters:
    y array_like

      The probability of k or fewer failures before n successes (float).

    n array_like

      The target number of successes (positive int).

    p array_like

      Probability of success in a single event (float).

    out ndarray, optional

      Optional output array for the function results

  Returns:
    k scalar or ndarray

      The maximum number of allowed failures such that nbdtr(k, n, p) = y.

  See also

  `nbdtr`

    Cumulative distribution function of the negative binomial.

  `nbdtrc`

    Survival function of the negative binomial.

  `nbdtri`

    Inverse with respect to p of nbdtr(k, n, p).

  `nbdtrin`

    Inverse with respect to n of nbdtr(k, n, p).

  `scipy.stats.nbinom`

    Negative binomial distribution

  Notes

  Wrapper for the CDFLIB [1] Fortran routine cdfnbn.

  Formula 26.5.26 of [2],

  \[\sum_{j=k + 1}^\infty {{n + j - 1}\choose{j}} p^n (1 - p)^j = I_{1 - p}(k + 1, n),\]

  is used to reduce calculation of the cumulative distribution function to that of a regularized incomplete beta \(I\).

  Computation of k involves a search for a value that produces the desired value of y. The search relies on the monotonicity of y with k.

  References

  [1]

  Barry Brown, James Lovato, and Kathy Russell, CDFLIB: Library of Fortran Routines for Cumulative Distribution Functions, Inverses, and Other Parameters.

  [2]

  Milton Abramowitz and Irene A. Stegun, eds. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. New York: Dover, 1972.

  Examples

  Compute the negative binomial cumulative distribution function for an exemplary parameter set.

      >>> import numpy as np
      >>> from scipy.special import nbdtr, nbdtrik
      >>> k, n, p = 5, 2, 0.5
      >>> cdf_value = nbdtr(k, n, p)
      >>> cdf_value
      0.9375

  Verify that `nbdtrik` recovers the original value for k.

      >>> nbdtrik(cdf_value, n, p)
      5.0

  Plot the function for different parameter sets.

      >>> import matplotlib.pyplot as plt
      >>> p_parameters = [0.2, 0.5, 0.7, 0.5]
      >>> n_parameters = [30, 30, 30, 80]
      >>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']
      >>> parameters_list = list(zip(p_parameters, n_parameters, linestyles))
      >>> cdf_vals = np.linspace(0, 1, 1000)
      >>> fig, ax = plt.subplots(figsize=(8, 8))
      >>> for parameter_set in parameters_list:
      ...     p, n, style = parameter_set
      ...     nbdtrik_vals = nbdtrik(cdf_vals, n, p)
      ...     ax.plot(cdf_vals, nbdtrik_vals, label=rf"$n={n},\ p={p}$",
      ...             ls=style)
      >>> ax.legend()
      >>> ax.set_ylabel("$k$")
      >>> ax.set_xlabel("$CDF$")
      >>> ax.set_title("Negative binomial percentile function")
      >>> plt.show()

  The negative binomial distribution is also available as `scipy.stats.nbinom`. The percentile function method `ppf` returns the result of `nbdtrik` rounded up to integers:

      >>> from scipy.stats import nbinom
      >>> q, n, p = 0.6, 5, 0.5
      >>> nbinom.ppf(q, n, p), nbdtrik(q, n, p)
      (5.0, 4.800428460273882)