BINOM_TEST
The BINOM_TEST node is based on a numpy or scipy function. The description of that function is as follows:
Perform a test that the probability of success is p.
Note: 'binom_test' is deprecated; it is recommended that 'binomtest' be used instead.
This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is 'p'. Params: x : int or array_like The number of successes, or if x has length 2, it is the
number of successes and the number of failures. n : int The number of trials. This is ignored if x gives both the
number of successes and failures. p : float The hypothesized probability of success. 0 <= p <= 1.
The default value is p = 0.5. alternative : {'two-sided', 'greater', 'less'} Indicates the alternative hypothesis.
The default value is 'two-sided'. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np
import scipy.stats
@flojoy
def BINOM_TEST(
default: OrderedPair | Matrix,
n: int = 2,
p: float = 0.5,
alternative: str = "two-sided",
) -> OrderedPair | Matrix | Scalar:
"""The BINOM_TEST node is based on a numpy or scipy function.
The description of that function is as follows:
Perform a test that the probability of success is p.
Note: 'binom_test' is deprecated; it is recommended that 'binomtest' be used instead.
This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is 'p'.
Parameters
----------
x : int or array_like
The number of successes, or if x has length 2, it is the
number of successes and the number of failures.
n : int
The number of trials. This is ignored if x gives both the
number of successes and failures.
p : float, optional
The hypothesized probability of success. 0 <= p <= 1.
The default value is p = 0.5.
alternative : {'two-sided', 'greater', 'less'}, optional
Indicates the alternative hypothesis.
The default value is 'two-sided'.
Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""
result = scipy.stats.binom_test(
x=default.y,
n=n,
p=p,
alternative=alternative,
)
if isinstance(result, np.ndarray):
result = OrderedPair(x=default.x, y=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))
return result