specification-curve 0.2.5

Specification Curve is a Python package that performs specification curve analysis.

Specification Curve

Specification Curve is a Python (3.6+) package that performs specification curve analysis.

• Free software: MIT license

• Documentation: https://specification-curve.readthedocs.io.

Quickstart

You can run this package right now in Google Colab

Running

from specification_curve import specification_curve as specy
from specification_curve import example as scdata
df = scdata.load_example_data1()
y_endog = 'y1'
x_exog = 'x1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                              cat_expand=['group2'])
sc.fit()
sc.plot()

produces

Grey squares (black lines when there are many specifications) show whether a variable is included in a specification or not. Blue markers and error bars show whether the coefficient is significant (0.05).

Here’s another example:

from specification_curve import specification_curve as specy
import numpy as np
import pandas as pd
n_samples = 300
np.random.seed(1332)
x_1 = np.random.random(size=n_samples)
x_2 = np.random.random(size=n_samples)
x_3 = np.random.random(size=n_samples)
x_4 = np.random.randint(2, size=n_samples)
y = (0.8*x_1 + 0.1*x_2 + 0.5*x_3 + x_4*0.6 +
     + 2*np.random.randn(n_samples))
df = pd.DataFrame([x_1, x_2, x_3, x_4, y],
                  ['x_1', 'x_2', 'x_3', 'x_4', 'y']).T
# Set x_4 as a categorical variable
df['x_4'] = df['x_4'].astype('category')
sc = specy.SpecificationCurve(df, 'y', 'x_1', ['x_2', 'x_3', 'x_4'],
                              cat_expand=['x_4'])
sc.fit()
sc.plot()

Features

These examples use the first set of example data:

from specification_curve import specification_curve as specy
from specification_curve import example as scdata
df = scdata.load_example_data1()

• Expand fixed effects into mutually exclusive groups using cat_expand

y_endog = 'y1'
x_exog = 'x1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                              cat_expand=['group1', 'group2'])
sc.fit()
sc.plot()

• Mutually exclude two variables using exclu_grp

y_endog = 'y1'
x_exog = 'x1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                                  exclu_grps=[['c1', 'c2']])
sc.fit()
sc.plot()

• Use multiple independent or dependent variables

x_exog = ['x1', 'x2']
y_endog = 'y1'
controls = ['c1', 'c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls)
sc.fit()
sc.plot()

• Save plots to file (format is inferred from file extension)

sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                                  cat_expand=['group1'])
sc.fit()
sc.plot(save_path='test_fig.pdf')

• Specification results stored in output DataFrame df_r

sc = specy.SpecificationCurve(df, y_endog, x_exog, controls)
sc.fit()
print(sc.df_r)

• Other statsmodels estimators (OLS is the default) can be used

import numpy as np
import pandas as pd
import statsmodels.api as sm
n_samples = 1000
x_2 = np.random.randint(2, size=n_samples)
x_1 = np.random.random(size=n_samples)
x_3 = np.random.randint(3, size=n_samples)
x_4 = np.random.random(size=n_samples)
x_5 = x_1 + 0.05*np.random.randn(n_samples)
x_beta = -1 + 3.5*x_1 + 0.2*x_2 + 0.3*x_3
prob = 1/(1 + np.exp(-x_beta))
y = np.random.binomial(n=1, p=prob, size=n_samples)
y2 = np.random.binomial(n=1, p=prob*0.98, size=n_samples)
df = pd.DataFrame([x_1, x_2, x_3, x_4, x_5, y, y2],
                  ['x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'y', 'y2']).T
y_endog = ['y', 'y2']
x_exog = ['x_1', 'x_5']
controls = ['x_3', 'x_2', 'x_4']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                              cat_expand='x_3')
sc.fit(estimator=sm.Logit)  # sm.Probit also works
sc.plot()

• The style of specification plot flexes for very large numbers of specifications

n_samples = 400
# Number of dimensions of continuous
# random variables
n_dim = 8
c_rnd_vars = np.random.random(size=(n_dim, n_samples))
c_rnd_vars_names = [f'c_{i}' for i in range(np.shape(c_rnd_vars)[0])]
y_1 = (0.3*c_rnd_vars[0, :] +
       0.5*c_rnd_vars[1, :])
y_2 = y_1 + 0.05*np.random.randn(n_samples)
df = pd.DataFrame([y_1, y_2], ['y1', 'y2']).T
for i, col_name in enumerate(c_rnd_vars_names):
    df[col_name] = c_rnd_vars[i, :]
controls = c_rnd_vars_names[1:]
sc = specy.SpecificationCurve(df, ['y1', 'y2'], c_rnd_vars_names[0],
                              controls)
sc.fit()
sc.plot()

• Always include regressors using the always_include keyword argument

df = scdata.load_example_data1()
x_exog = 'x1'
y_endog = 'y1'
controls = ['c2', 'group1', 'group2']
sc = specy.SpecificationCurve(df, y_endog, x_exog, controls,
                                always_include='c1')
sc.fit()
sc.plot()

Similar Packages

In RStats, there is specr (which inspired many design choices in this package) and spec_chart. Some of the example data in this package is the same as in specr.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.2.5 (2020-11-22)

• Added option to always include some regressors

• Added option to label preferred specification in plots

0.2.4 (2020-09-15)

• Further fix to pick up example data

0.2.3 (2020-09-14)

• Fixed examples

• Bug fix for including csv data in pypi distribution

0.2.2 (2020-09-02)

• More badges in readme

• Dropped support for python 3.5

0.2.1 (2020-09-02)

• Switched to object oriented design

• Now supports range of statsmodels estimators!

• Example showing how to save plots to file in docs

• Example showing where estimation results are stored in docs

• Docs example of very large number of specifications

0.1.1 (2020-08-01)

• Multiple independent, dependent, and control variables implemented as lists. Mutually exclusive control variables implemented. Expansions of categorical variables into mutually exclusive fixed effects implemented.

0.1.0 (2020-07-27)

• First release on PyPI.