pycvcqv

Introduction

pycvcqv provides versatile functions to quantify homogeneity with confidence intervals. It offers a variety of well-established methods from the literature (Kelley, McKay, Miller, Vangel, Mahmoudvand-Hassani, Equal-Tailed, Shortest-Length, Normal Approximation, Bonett, and the Abu-Shawiesh-Akyuz-Kibria adjusted-degrees-of-freedom, large-sample, and augmented-large-sample CIs) and bootstrap resampling techniques (Normal, Basic, Percentile, BCa) for constructing confidence intervals on the Coefficient of Variation (cv) and the Coefficient of Quartile Variation (cqv).

Coefficient of Variation

cv is a measure of relative dispersion representing the degree of variability relative to the mean (Albatineh et al., 2014). Since cv is unitless, it is useful for comparing variables that have different units. It is also a measure of homogeneity (Albatineh et al., 2014).

Coefficient of Quartile Variation

cqv is a measure of relative dispersion based on the interquartile range (IQR). Since cqv is unitless, it is also useful for comparing variables that have different units. It is also a measure of homogeneity (Bonett, 2006; Altunkaynak, 2018).

Install

pip install pycvcqv

Usage

import pandas as pd
from pycvcqv import coefficient_of_variation, cqv

coefficient_of_variation(
    data=[
        0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
        4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
    ],
    multiplier=100,
    ndigits=2
)
# {'cv': 57.77, 'lower': 41.43, 'upper': 98.38}
cqv(
    data=[0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4, 4.6, 5.4, 5.4],
    multiplier=100,
)
# 51.7241
data = pd.DataFrame(
    {
        "col-1": pd.Series([0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5]),
        "col-2": pd.Series([5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9]),
    }
)
coefficient_of_variation(data=data, num_threads=3)
#   columns      cv      lower      upper
# 0   col-1  0.6076     0.3770     1.6667
# 1   col-2  0.1359     0.0913     0.2651
cqv(data=data, num_threads=-1)
#   columns      cqv
# 0   col-1  0.3889
# 1   col-2  0.0732

Confidence-interval methods for cv

coefficient_of_variation accepts a method argument that selects the confidence-interval estimator.

from pycvcqv import coefficient_of_variation

x = [
    0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
    4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9,
]

for method in (
    "kelley", "mckay", "miller", "vangel",
    "mahmoudvand_hassani", "equal_tailed",
    "shortest_length", "normal_approximation",
    "aak_adj", "aak_ls", "aak_als",
    "norm", "basic", "perc", "bca",
):
    print(method, coefficient_of_variation(
        data=x,
        method=method,
        multiplier=100,
        ndigits=3,
        num_replicates=10000,
        random_state=42,
    ))

Output (95% CI, multiplier=100, ndigits=3, bootstrap methods use num_replicates=10000, random_state=42):

Confidence-interval methods for cqv

cqv accepts a method argument that selects the confidence-interval estimator. When method is omitted only the point estimate is returned (the legacy behavior).

from pycvcqv import cqv

x = [
    0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
    4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9,
]

for method in ("bonett", "norm", "basic", "perc", "bca"):
    print(method, cqv(
        data=x,
        method=method,
        multiplier=100,
        ndigits=3,
        num_replicates=10000,
        random_state=42,
    ))

Output (95% CI, multiplier=100, ndigits=3, bootstrap methods use num_replicates=10000, random_state=42):

Credits

This project was generated with python-package-template