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Update anova_documentation.rst
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@Corey-Bryant
Corey-Bryant committed Dec 27, 2021
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Expand Up @@ -30,11 +30,11 @@ anova methods
^^^^^^^^^^^^^

* **results(return_type = "Dataframe", decimals = 4, pretty_format = True)**
* **return_type** : The type of data structure the results should be returned as. Supported options
are 'Dataframe' which will return a Pandas DataFrame or 'Dictionary' which will return a dictionary.
* **decimals** : The number of decimal places the data should be rounded too.
* **pretty_format ** : If pretty formatting should be applied. This adds extra empty spaces in the returned data
structure for visualization of the results.
* **return_type** : The type of data structure the results should be returned as. Supported options
are 'Dataframe' which will return a Pandas DataFrame or 'Dictionary' which will return a dictionary.
* **decimals** : The number of decimal places the data should be rounded too.
* **pretty_format ** : If pretty formatting should be applied. This adds extra empty spaces in the returned data
structure for visualization of the results.


Expand All @@ -47,11 +47,11 @@ called 'systolic'.
.. code:: python
import researchpy as rp
# Used to load example data #
import statsmodels.datasets
# Used to load example data #
import statsmodels.datasets
systolic = statsmodels.datasets.webuse('systolic')
systolic = statsmodels.datasets.webuse('systolic')
Now let's get some quick information regarding the data set.
Expand Down Expand Up @@ -85,24 +85,25 @@ treated as categorical while the dependent variable will be treated as continuou
rp.summarize(systolic["systolic"])
.. parsed-literal::
.. raw:: html

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Name</th> <th>N</th> <th>Mean</th> <th>Median</th> <th>Variance</th> <th>SD</th> <th>SE</th> <th>95% Conf. Interval</th> </tr> </thead>
<tbody> <tr> <th>0</th> <td>systolic</td> <td>58</td> <td>18.8793</td> <td>21</td> <td>163.862</td> <td>12.8009</td> <td>1.6808</td> <td>[15.5135, 22.2451]</td> </tr> </tbody>
</table>





Name N Mean Median Variance SD SE 95% Conf. Interval
0 systolic 58 18.8793 21 163.862 12.8009 1.6808 [15.5135, 22.2451]


.. code:: python
rp.crosstab(systolic["disease"], systolic["drug"])
.. parsed-literal::
.. raw:: html

drug 1 2 3 4 All
disease
1 6 5 3 5 19
2 4 4 5 6 19
3 5 6 4 5 20
All 15 15 12 16 58
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Variable</th> <th>Outcome</th> <th>Count</th> <th>Percent</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>drug</td> <td>4</td> <td>16</td> <td>27.59</td> </tr> <tr> <th>1</th> <td></td> <td>2</td> <td>15</td> <td>25.86</td> </tr> <tr> <th>2</th> <td></td> <td>1</td> <td>15</td> <td>25.86</td> </tr> <tr> <th>3</th> <td></td> <td>3</td> <td>12</td> <td>20.69</td> </tr> <tr> <th>4</th> <td>disease</td> <td>3</td> <td>20</td> <td>34.48</td> </tr> <tr> <th>5</th> <td></td> <td>2</td> <td>19</td> <td>32.76</td> </tr> <tr> <th>6</th> <td></td> <td>1</td> <td>19</td> <td>32.76</td> </tr> </tbody></table>


Now to conduct the ANOVA; by default Type 3 sum of squares are used.
Expand All @@ -113,14 +114,6 @@ Now to conduct the ANOVA; by default Type 3 sum of squares are used.
mod = anova("systolic ~ C(drug) + C(disease) + C(drug):C(disease)", data = systolic, sum_of_squares = 3)
mod.results()
.. parsed-literal::
.. raw:: html

Source Sum of Squares Degrees of Freedom Mean Squares F value p-value
0 Model 4259.34 11 387.213 3.5057 0.0013
1
2 drug 2997.47 3 999.157 9.046 0.0001
3 disease 415.873 2 207.936 1.8826 0.1637
4 drug:disease 707.266 6 117.878 1.0672 0.3958
5
6 Residual 5080.82 46 110.453
7 Total 9340.16 57 163.862
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Source</th> <th>Sum of Squares</th> <th>Degrees of Freedom</th> <th>Mean Squares</th> <th>F value</th> <th>p-value</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>Model</td> <td>4259.34</td> <td>11</td> <td>387.213</td> <td>3.5057</td> <td>0.0013</td> </tr> <tr> <th>1</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>2</th> <td>drug</td> <td>2997.47</td> <td>3</td> <td>999.157</td> <td>9.046</td> <td>0.0001</td> </tr> <tr> <th>3</th> <td>disease</td> <td>415.873</td> <td>2</td> <td>207.936</td> <td>1.8826</td> <td>0.1637</td> </tr> <tr> <th>4</th> <td>drug:disease</td> <td>707.266</td> <td>6</td> <td>117.878</td> <td>1.0672</td> <td>0.3958</td> </tr> <tr> <th>5</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>6</th> <td>Residual</td> <td>5080.82</td> <td>46</td> <td>110.453</td> <td></td> <td></td> </tr> <tr> <th>7</th> <td>Total</td> <td>9340.16</td> <td>57</td> <td>163.862</td> <td></td> <td></td> </tr> </tbody></table>

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