Skip to content

Commit

Permalink
Updated documentation for multiple methods
Browse files Browse the repository at this point in the history
  • Loading branch information
@Corey-Bryant
Corey-Bryant committed Mar 10, 2022
1 parent b605202 commit fde503e
Show file tree
Hide file tree
Showing 5 changed files with 79 additions and 120 deletions.
6 changes: 6 additions & 0 deletions source/anova_documentation.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -188,3 +188,9 @@ If it's of interest, one can also access the underlying regression table.
.. raw:: html

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>systolic</th> <th>Coef.</th> <th>Std. Err.</th> <th>t</th> <th>p-value</th> <th>95% Conf. Interval</th> </tr> </thead> <tbody> <tr> <td>Intercept</td> <td>29.3333</td> <td>4.2905</td> <td>6.8367</td> <td>0.0000</td> <td>[20.6969, 37.9697]</td> </tr> <tr> <td>drug</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>1</td> <td>(reference)</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>2</td> <td>-1.3333</td> <td>6.3639</td> <td>-0.2095</td> <td>0.8350</td> <td>[-14.1432, 11.4765]</td> </tr> <tr> <td>3</td> <td>-13.0000</td> <td>7.4314</td> <td>-1.7493</td> <td>0.0869</td> <td>[-27.9587, 1.9587]</td> </tr> <tr> <td>4</td> <td>-15.7333</td> <td>6.3639</td> <td>-2.4723</td> <td>0.0172</td> <td>[-28.5432, -2.9235]</td> </tr> <tr> <td>disease</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>1</td> <td>(reference)</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>2</td> <td>-1.0833</td> <td>6.7839</td> <td>-0.1597</td> <td>0.8738</td> <td>[-14.7387, 12.572]</td> </tr> <tr> <td>3</td> <td>-8.9333</td> <td>6.3639</td> <td>-1.4038</td> <td>0.1671</td> <td>[-21.7432, 3.8765]</td> </tr> <tr> <td>drug:disease</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>2:2</td> <td>6.5833</td> <td>9.7839</td> <td>0.6729</td> <td>0.5044</td> <td>[-13.1107, 26.2774]</td> </tr> <tr> <td>2:3</td> <td>-0.9000</td> <td>8.9999</td> <td>-0.1000</td> <td>0.9208</td> <td>[-19.0159, 17.2159]</td> </tr> <tr> <td>3:2</td> <td>-10.8500</td> <td>10.2435</td> <td>-1.0592</td> <td>0.2950</td> <td>[-31.4692, 9.7692]</td> </tr> <tr> <td>3:3</td> <td>1.1000</td> <td>10.2435</td> <td>0.1074</td> <td>0.9150</td> <td>[-19.5192, 21.7192]</td> </tr> <tr> <td>4:2</td> <td>0.3167</td> <td>9.3017</td> <td>0.0340</td> <td>0.9730</td> <td>[-18.4066, 19.04]</td> </tr> <tr> <td>4:3</td> <td>9.5333</td> <td>9.2022</td> <td>1.0360</td> <td>0.3056</td> <td>[-8.9897, 28.0564]</td> </tr> </tbody></table>


References
==========
.. bibliography:: refs.bib
:list: bullet
85 changes: 39 additions & 46 deletions source/difference_test_documentation.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -72,16 +72,16 @@ Arguments
.. note::
This can be a one step, or two step process.

**One step**
.. code:: python
**One step**
.. code:: python
difference_test("DV ~ IV", data).conduct()
difference_test("DV ~ IV", data).conduct()
**Two step**
.. code:: python
**Two step**
.. code:: python
model = difference_test("DV ~ IV", data)
model.conduct()
model = difference_test("DV ~ IV", data)
model.conduct()
Expand Down Expand Up @@ -212,7 +212,10 @@ correlation coefficient r :cite:`Kerby2012` for the Wilcoxon ranked-sign test.
Examples
--------
========

Loading Packages and Data
-------------------------
First let's create an example data set to work through the examples. This will be done using
numpy (to create fake data) and pandas (to hold the data in a data frame).

Expand Down Expand Up @@ -267,16 +270,19 @@ will be shown below.
Now the data is in the correct structure.


Independent Samples t-test
--------------------------

.. code:: python
# Independent t-test
# If you don't store the 2 returned DataFrames, it outputs as a tuple and
# is displayed
rp.difference_test("StressReactivity ~ C(Exercise)",
data = df2,
equal_variances = True,
independent_samples = True).conduct(effect_size = "all")
data = df2,
equal_variances = True,
independent_samples = True).conduct(effect_size = "all")
.. parsed-literal::
Expand All @@ -302,9 +308,9 @@ Now the data is in the correct structure.
# Otherwise you can store them as objects
summary, results = rp.difference_test("StressReactivity ~ C(Exercise)",
data = df2,
equal_variances = True,
independent_samples = True).conduct(effect_size = "all")
data = df2,
equal_variances = True,
independent_samples = True).conduct(effect_size = "all")
summary
Expand Down Expand Up @@ -339,13 +345,16 @@ Now the data is in the correct structure.
Paired Samples t-test
---------------------

.. code:: python
# Paired samples t-test
summary, results = rp.difference_test("StressReactivity ~ C(Exercise)",
data = df2,
equal_variances = True,
independent_samples = False).conduct(effect_size = "all")
data = df2,
equal_variances = True,
independent_samples = False).conduct(effect_size = "all")
summary
Expand Down Expand Up @@ -378,6 +387,10 @@ Now the data is in the correct structure.
10 Point-Biserial r 0.105763
Welch's t-test
--------------
One can request either the Satterthwaite (default) or Welch degrees of freedom; to
calculate degrees of freedom using Welch's formula set welch_dof = "welch"

.. code:: python
Expand Down Expand Up @@ -420,47 +433,27 @@ Now the data is in the correct structure.
Wilcoxon signed-rank Test
--------------------------
.. code:: python
# Wilcoxon signed-rank test
summary, results = rp.difference_test("StressReactivity ~ C(Exercise)",
data = df2,
equal_variances = False,
independent_samples = False).conduct(effect_size = "r")
desc, var_adj, res = difference_test("StressReactivity ~ C(Exercise)", df2, independent_samples=False, equal_variances = False).conduct()
summary
print(desc, var_adj, res, sep = "\n"*2)
.. parsed-literal::
Name N Mean Variance SD SE 95% Conf. Interval
0 No 100 4.59 7.55747 2.74909 0.274909 4.044522 5.135478
1 Yes 100 4.16 9.81253 3.1325 0.313250 3.538445 4.781555
.. code:: python
results
.. raw:: html

.. parsed-literal::
Wilcoxon signed-rank test Results
0 (No = Yes)
1 W = 1849.5
2 Two sided p-value = 0.333755
3 Point-Biserial r 0.366238
.. code:: python
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>sign</th> <th>obs</th> <th>sum ranks</th> <th>expected</th> </tr> </thead> <tbody> <tr> <td>positive</td> <td>40</td> <td>2,298.0000</td> <td>2,502.5000</td> </tr> <tr> <td>negative</td> <td>51</td> <td>2,707.0000</td> <td>2,502.5000</td> </tr> <tr> <td>zero</td> <td>9</td> <td>45.0000</td> <td>45.0000</td> </tr> <tr> <td>all</td> <td>100</td> <td>5,050.0000</td> <td>5,050.0000</td> </tr> </tbody></table>

# Exporting descriptive table (summary) and result table (results) to same
# csv file
summary.to_csv("C:\\Users\\...\\test.csv", index= False)
results.to_csv("C:\\Users\\...\\test.csv", index= False, mode= 'a')
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>unadjusted variance</th> <th>adjustment for ties</th> <th>adjustment for zeros</th> <th>adjusted variance</th> </tr> </thead> <tbody> <tr> <td>84,587.5000</td> <td>-375.1250</td> <td>-71.2500</td> <td>84,141.1250</td> </tr> </tbody></table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>z</th> <th>w</th> <th>pval</th> </tr> </thead> <tbody> <tr> <td>-0.7050</td> <td>2,298.0000</td> <td>0.4808</td> </tr> </tbody></table>




References
----------
==========
.. bibliography:: refs.bib
:list: bullet
34 changes: 21 additions & 13 deletions source/signrank_documentation.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -21,15 +21,15 @@ Input
-----
**signrank(formula_like = None, data = {}, group1 = None, group2 = None, zero_method = "pratt", correction = False, mode = "auto")**

* **formula_like** : A valid formula which will parse the data into a design matrix.
* **formula_like** : A valid `formula <https://patsy.readthedocs.io/en/latest/formulas.html>`_ which will parse the data into a design matrix.
* **data** : The dataframe which contains the data to be analyzed; required if using *formula_like*.
* **group1** : The array like object which contains data for the paired-sample.
* **group2** : The array like object which contains data for the paired-sample.
* **zero_method** : How to handle the zero-differences in the ranking process. Available options are (from :cite:`scipy_wilcoxon`):
* **zero_method** : How to handle the zero-differences in the ranking process. Available options are (see `scipy.stats.wilcoxon <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon>`_):
* *"pratt"* : Includes zero-differences in the ranking process, but drops the ranks of the zeros (default).
* *"wilcox"* : Discards all zero-differences.
* **correction** : Boolean value indicating if the continuity correction should be applied; see :cite:`scipy_wilcoxon` for more information.
* **mode** : Method to calculate the p-value, see :cite:`scipy_wilcoxon` for more information. Options are:
* **correction** : Boolean value indicating if the continuity correction should be applied; see `scipy.stats.wilcoxon <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon>`_ for more information.
* **mode** : Method to calculate the p-value, see `scipy.stats.wilcoxon <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon>`_ for more information. Options are:
* *"auto"* : Use the exact distribution if there are no more than 25 observations and no ties, otherwise a normal approximation will be used (default).
* *"exact"* : Use the exact distribution, can be used if there are no more than 25 observations and no ties.
* *"approx"* : Use a normal approximation.
Expand Down Expand Up @@ -87,13 +87,13 @@ called 'fuel'.
.. code:: python
import researchpy as rp
import pandas as pd
# Used to load example data #
import statsmodels.datasets
import pandas as pd
# Used to load example data #
import statsmodels.datasets
fuel = statsmodels.datasets.webuse('fuel')
fuel["id"] = range(1, fuel.shape[0] + 1)
fuel.info()
fuel = statsmodels.datasets.webuse('fuel')
fuel["id"] = range(1, fuel.shape[0] + 1)
fuel.info()
.. raw:: html

Expand All @@ -110,7 +110,7 @@ the data ready for this demonstration section the transformation will be conduct
value_vars = ["mpg1", "mpg2"],
var_name = "mpg")
fuel2.head()
fuel2.head()
.. raw:: html

Expand All @@ -127,7 +127,7 @@ it makes the output look cleaner.
desc, var_adj, res = signrank(group1 = fuel.mpg1, group2 = fuel.mpg2).conduct()
print(desc, var_adj, res, sep = "\n"*2)
print(desc, var_adj, res, sep = "\n"*2)
.. raw:: html

Expand Down Expand Up @@ -165,7 +165,7 @@ Signrank using Long Structured Datasets
desc, var_adj, res = signrank("value ~ C(mpg)", fuel2).conduct()
print(desc, var_adj, res, sep = "\n"*2)
print(desc, var_adj, res, sep = "\n"*2)
.. raw:: html

Expand All @@ -174,3 +174,11 @@ Signrank using Long Structured Datasets
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>unadjusted variance</th> <th>adjustment for ties</th> <th>adjustment for zeros</th> <th>adjusted variance</th> </tr> </thead> <tbody> <tr> <td>162.5000</td> <td>-1.6250</td> <td>-0.2500</td> <td>160.6250</td> </tr> </tbody></table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>z</th> <th>w</th> <th>pval</th> </tr> </thead> <tbody> <tr> <td>-1.9726</td> <td>13.5000</td> <td>0.0485</td> </tr> </tbody></table>




References
==========
.. bibliography:: refs.bib
:list: bullet
10 changes: 5 additions & 5 deletions source/summarize_documentation.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -56,12 +56,12 @@ called 'auto'.
.. code:: python
import researchpy as rp
import pandas as pd
# Used to load example data #
import statsmodels.datasets
import pandas as pd
# Used to load example data #
import statsmodels.datasets
auto = statsmodels.datasets.webuse('auto')
auto.info()
auto = statsmodels.datasets.webuse('auto')
auto.info()
.. parsed-literal::
Expand Down
64 changes: 8 additions & 56 deletions source/ttest_documentation.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -193,6 +193,9 @@ calculated as:
Examples
========

Loading Packages and Data
-------------------------
.. code:: python
import numpy, pandas, researchpy
Expand Down Expand Up @@ -636,72 +639,21 @@ Wilcoxon Signed-Rank Test
.. code:: python
# Wilcoxon signed-rank test
researchpy.ttest(df['healthy'], df['non-healthy'],
equal_variances= False, paired= True)
desc, res = researchpy.ttest(df['healthy'], df['non-healthy'],
equal_variances= False, paired= True)
.. raw:: html

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Wilcoxon signed-rank test</th>
<th>results</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Mean for healthy =</td>
<td>4.5900</td>
</tr>
<tr>
<th>1</th>
<td>Mean for non-healthy =</td>
<td>4.1600</td>
</tr>
<tr>
<th>2</th>
<td>T value =</td>
<td>1849.5000</td>
</tr>
<tr>
<th>3</th>
<td>Z value =</td>
<td>-0.9638</td>
</tr>
<tr>
<th>4</th>
<td>Two sided p value =</td>
<td>0.3347</td>
</tr>
<tr>
<th>5</th>
<td>r =</td>
<td>-0.0681</td>
</tr>
</tbody>
</table>
</div>


<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>sign</th> <th>obs</th> <th>sum ranks</th> <th>expected</th> </tr> </thead> <tbody> <tr> <td>positive</td> <td>52</td> <td>2,804.5000</td> <td>2,502.5000</td> </tr> <tr> <td>negative</td> <td>39</td> <td>2,200.5000</td> <td>2,502.5000</td> </tr> <tr> <td>zero</td> <td>9</td> <td>45.0000</td> <td>45.0000</td> </tr> <tr> <td>all</td> <td>100</td> <td>5,050.0000</td> <td>5,050.0000</td> </tr> </tbody></table>

.. code:: python
# Exporting descriptive table (des) and result table (res) to same
# csv file
des, res = researchpy.ttest(df['healthy'], df['non-healthy'])
des.to_csv("C:\\Users\\...\\test.csv", index= False)
res.to_csv("C:\\Users\\...\\test.csv", index= False, mode= 'a')
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>Wilcoxon signed-rank test</th> <th>results</th> </tr> </thead> <tbody> <tr> <td>Mean for healthy =</td> <td>4.5900</td> </tr> <tr> <td>Mean for non-healthy =</td> <td>4.1600</td> </tr> <tr> <td>W value =</td> <td>2,200.5000</td> </tr> <tr> <td>Z value =</td> <td>1.0411</td> </tr> <tr> <td>p value =</td> <td>0.2978</td> </tr> <tr> <td>Rank-Biserial r =</td> <td>0.1196</td> </tr> <tr> <td>Pearson r =</td> <td>0.1041</td> </tr> </tbody></table>





References
----------
==========
.. bibliography:: refs.bib
:cited:
:list: bullet

0 comments on commit fde503e

Please sign in to comment.