Metadata-Version: 2.1
Name: chiscore
Version: 0.2.1
Summary: Test statistics from linear combination of chi-squared distributions.
Home-page: https://github.com/limix/chiscore
Author: Rachel Moore, Danilo Horta
Author-email: rm18@sanger.ac.uk, horta@ebi.ac.uk
Maintainer: Danilo Horta
Maintainer-email: horta@ebi.ac.uk
License: MIT
Download-URL: https://github.com/limix/chiscore
Description: # chiscore
        
        Estimate the joint significance of test statistics derived from linear combination
        of chi-squared distributions.
        
        ## Install
        
        We recommend installing it via
        [conda](http://conda.pydata.org/docs/index.html):
        
        ```bash
        conda install -c conda-forge chiscore
        ```
        
        Alternatively, chiscore can also be installed using
        [pip](https://pypi.python.org/pypi/pip):
        
        ```bash
        pip install chiscore
        ```
        
        ## Running the tests
        
        After installation, you can test it
        
        ```bash
        python -c "import chiscore; chiscore.test()"
        ```
        
        as long as you have [pytest](https://docs.pytest.org/en/latest/).
        
        ## Usage
        
        ### Davies
        
        ```python
        >>> from chiscore import davies_pvalue
        >>> q = 1.5
        >>> w = [[0.3, 5.0], [5.0, 1.5]]
        >>> davies_pvalue(q, w)
        {'p_value': 0.6151796819770086, 'param': {'liu_pval': 0.6151796819770086, 'Is_Converged': 1.0}, 'p_value_resampling': None, 'pval_zero_msg': None}
        ```
        
        ### Liu
        
        Let us approximate
        
            𝑋 = 0.5⋅χ²(1, 1) + 0.4⋅χ²(2, 0.6) + 0.1⋅χ²(1, 0.8),
        
        and evaluate Pr(𝑋 > 2).
        
        ```python
        >>> from chiscore import liu_sf
        >>>
        >>> w = [0.5, 0.4, 0.1]
        >>> dofs = [1, 2, 1]
        >>> deltas = [1, 0.6, 0.8]
        >>> (q, dof, delta, _) = liu_sf(2, w, dofs, deltas)
        >>> q
        0.4577529852208846
        >>> dof
        3.5556138890755395
        >>> delta
        0.7491921870025307
        ```
        
        Therefore, we have
        
            Pr(𝑋 > 2) ≈ Pr(χ²(3.56, 0.75) > 𝑡⁺𝜎ₓ + 𝜇ₓ) = 0.458.
        
        ### P-value
        
        ```python
        >>> from chiscore import optimal_davies_pvalue
        >>> q = [1.5, 3.0]
        >>> mu = -0.5
        >>> var = 1.0
        >>> kur = 3.0
        >>> w = [10.0, 0.2, 0.1, 0.3]
        >>> remain_var = 0.5
        >>> df = 3.4
        >>> trho = [5.1, 0.2]
        >>> grid = [0., 0.01]
        >>> optimal_davies_pvalue(q, mu, var, kur, w, remain_var, df, trho, grid)
        0.966039962464624
        ```
        
        ## Authors
        
        * [Danilo Horta](https://github.com/horta)
        
        ## References
        
        * Lee, Seunggeun, Michael C. Wu, and Xihong Lin. "Optimal tests for rare variant
          effects in sequencing association studies." Biostatistics 13.4 (2012): 762-775.
        * Liu, H., Tang, Y., & Zhang, H. H. (2009). A new chi-square approximation to the
          distribution of non-negative definite quadratic forms in non-central normal
          variables. Computational Statistics & Data Analysis, 53(4), 853-856.
        
        ## License
        
        This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/chiscore/master/LICENSE.md).
        
Keywords: test statistic,chi-squared distribution,p-value
Platform: Windows
Platform: MacOS
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
