sgpv 1.0.0

Tools to calculate SGPVs

sgpv module

This module allows to calculate Second Generation P-Values and their diagnostics in Python. This package is a translation of the original sgpv R-library into Python. The same library has already been translated into Stata by the author of this Python translation.

This module contains the following functions:

        value    - calculate the SGPVs
        power    - power functions for the SGPVs
        risk     - false confirmation/discovery risks for the SGPVs
        plot     - plot the SGPVs
        data     - load the example dataset into memory

Dependencies

This module depends on:

        pandas>=1.0.4

        matplotlib>=3.2.1

        numpy>=1.18.0

        scipy>=1.3.2

These dependencies document only under which version I tested my functions. Older version might work as well.

Installation

Binaries and source distributions are available from PyPi https://pypi.org/projects/sgpv

The same installation files are also located in the folder dist. Just download the tarball and unzip it. Then run

python setup.py install

Examples

Below are some examples taken from the documentation of each function:

Calculate second generation p-values (sgpv.value):

>>> import numpy as np
>>> import sgpv
>>> lb = (np.log(1.05), np.log(1.3), np.log(0.97))
>>> ub = (np.log(1.8), np.log(1.8), np.log(1.02))
>>> sgpv.value(est_lo = lb, est_hi = ub,
             null_lo = np.log(1/1.1), null_hi = np.log(1.1))
    sgpv(pdelta=array([0.1220227, 0.        , 1.        ]),
     deltagap=array([None, 1.7527413, None], dtype=object))

Power function(sgpv.power)

>>> import sgpv       
>>> sgpv.power(true=2, null_lo=-1, null_hi=1, std_err = 1,
...        interval_type='confidence', interval_level=0.05)
poweralt = 0.168537 powerinc = 0.831463 powernull =  0
type I error summaries:
at 0 = 0.0030768 min = 0.0030768 max = 0.0250375 mean = 0.0094374
>>> sgpv.power(true=0, null_lo=-1, null_hi=1, std_err = 1,
...         interval_type='confidence', interval_level=0.05)
poweralt = 0.0030768 powerinc = 0.9969232 powernull =  0
type I error summaries:
at 0 = 0.0030768 min = 0.0030768 max = 0.0250375 mean = 0.0094374 

False discory risk(sgpv.risk)

>>> import sgpv
>>> import numpy as np
>>> from scipy.stats import norm
>>> sgpv.risk(sgpval = 0, null_lo = np.log(1/1.1), null_hi = np.log(1.1),
           std_err = 0.8, null_weights = 'Uniform',
           null_space = (np.log(1/1.1), np.log(1.1)), alt_weights = 'Uniform',
           alt_space = (2 + 1*norm.ppf(1-0.05/2)*0.8, 2 - 1*norm.ppf(1-0.05/2)*0.8),
           interval_type = 'confidence', interval_level = 0.05)
0.0594986

Plotting of SGPVs with example dataset:

>>> import pandas as pd
>>> import sgpv
>>> import data
>>> import matplotlib.pyplot as plt
>>> df = data.load_dataset()  # Load the example dataset as a dataframe
>>> est_lo=df['ci.lo']
>>> est_hi=df['ci.hi']
>>> pvalue=df['p.value']
>>> null_lo=-0.3
>>> null_hi=0.3
>>> title_lab="Leukemia Example"
>>> y_lab="Fold Change (base 10)"
>>> x_lab="Classical p-value ranking"
>>> sgpv.plot(est_lo=est_lo, est_hi=est_hi, null_lo=null_lo, null_hi=null_hi,
...            set_order=pvalue, null_pt=0, x_show=7000, outline_zone=True,
...            title_lab=title_lab, y_lab=y_lab, x_lab=x_lab )
>>> plt.yticks(ticks=np.round(np.log10(np.asarray(
...        (1/1000,1/100,1/10,1/2,1,2,10,100,1000))),2), labels=(
...                           '1/1000','1/100','1/10','1/2',1,2,10,100,1000))
>>> plt.show()