Update README.md

README.md

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 # GLMUtility
-Wrapper around Python statsmodels to build GLMs
+GLMUtility is built around statsmodels GLM framework.  It's purpose is to create a quasi-UI in Jupyter to allow for a point-and-click
+UI experience.  It is quasi as it still requires writing Python code, but it is heavily abstracted away.
+
+
+# GLMUtility Example
+
+
+```python
+import pandas as pd
+import numpy as np
+df = pd.read_pickle('sample.pkl')
+```
+
+
+```python
+from GLMUtility import GLMUtility
+```
+
+    C:\Users\jbogaard\AppData\Local\Continuum\anaconda3\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
+      from pandas.core import datetools
+
+
+
+
+    <div class="bk-root">
+        <a href="https://bokeh.pydata.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
+        <span id="6f01997b-549e-493e-9037-c63c4d566e9f">Loading BokehJS ...</span>
+    </div>
+
+
+
+
+To create a GLM, you need to specify the universe of predictor (or independent) features, a response (or dependent variable), and finally, a weight. 
+
+
+```python
+independent = ['Gender','Age']
+dependent = 'Response'
+weight = 'Weight'
+MyGLM = GLMUtility.GLM(data=df, 
+                       independent=independent, 
+                       dependent=dependent, 
+                       weight=weight, 
+                       scale='X2', 
+                       family='Poisson', 
+                       link='Log')
+MyGLM.fit()
+```
+
+### view() is your UI
+Using the view() method, we can toggle one way views of our observed response, our fitted averages, marginal plots of any fitted features, as wella s confidence intervals.
+
+
+
+```python
+MyGLM.view()
+```
+
+
+    A Jupyter Widget
+
+
+### Fitting features is 'simple'
+
+
+```python
+MyGLM.fit(simple=['Gender'])
+```
+
+### Custom Factors
+A custom factor is an on-the-fly data transformation that we will be using to manage our model fits. Each model feature has various levels and many of these will be insignificant when we fit the raw feature itself as a simple factor.  Rather than recreating our dataset again, we can aggregate levels of a feature together using custom factors.  You can group multiple levels of a feature by using key:value mapping of a python dictionary
+
+- Custom factors can be created by calling the create_custom method on the GLM object:
+```python
+create_custom(name=<name to assign custom>, column=<source column to apply to>,dictionary=<key:value mapping>)
+```
+
+
+```python
+custom_dict = {**{key:1 for key in range(16,25)}, 
+                **{key:2 for key in range(25,42)}, 
+                **{key:3 for key in range(42,51)}}
+
+MyGLM.create_custom(name='Age_c1', column='Age',dictionary=custom_dict)
+
+MyGLM.fit(simple=['Gender'], customs=['Age_c1'])
+MyGLM.view()
+```
+
+
+    A Jupyter Widget
+
+
+### Variates
+For continuous variables, there is a notion that the factor levels have some relational form and should not be treated as discrete independent factors.  To accomodate this, we can employ Variates which will smooth out the factor fits using a polynomial form.  Functionality is identical to custom factors, except there is an additional arguement `degree` to specify the order of the polynomial.
+
+```python
+create_variate(name=???, column=???,dictionary=???, degree=???)
+```
+
+
+```python
+variate_dict = {key:key for key in np.sort(df['Age'].unique())}
+
+MyGLM.create_variate(name='Age_v1', column='Age',dictionary=variate_dict, degree=3)
+
+MyGLM.fit(simple=['Gender'], variates=['Age_v1'])
+MyGLM.view()
+```
+
+
+    A Jupyter Widget
+
+
+### Offsets?  No problem
+There will be times that we do not want the GLM to compute certain factors, but we want them accounted for in our GLM.  Imagine you've performed a separate analysis (e.g. deductible study) which you want to include in your GLM.  This can be accomplished with **offsets**.  By entering 'known' relativities as offsets, the GLM will solve for all other parameters around those 'known' relativities.  
+
+Creating offsets has similar syntax to custom factors and variates:
+```python
+create_offset(name = ???, column = ???, dictionary = ???)
+```
+With offsets, the dictionary values will be the relativities you would like for each factor level.
+
+### But what about interactions?...
+Sure why not.
+
+
+```python
+create_interaction(name=<name of interaction>, interaction=[<var1>, <var2>])
+```
+
+
+
+```python
+MyGLM.create_interaction('Age.Gender', ['Age_v1','Gender'])
+
+MyGLM.fit(simple=['Gender'], variates=['Age_v1'], interactions = ['Age.Gender'])
+MyGLM.view()
+```
+
+
+    A Jupyter Widget
+
+
+### Two way Actual vs Expected 
+To see interactions more easily, look at the two_way actual vs. expected chart.
+
+
+```python
+MyGLM.two_way('Age','Gender')
+```
+
+
+
+<div class="bk-root">
+    <div class="bk-plotdiv" id="9a486365-dce0-488a-bc08-896f4f3a48ee"></div>
+</div>
+
+
+
+