Fitting models using R-style formulas

Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the patsy docs:

Loading modules and functions

In [1]: import statsmodels.formula.api as smf

In [2]: import numpy as np

In [3]: import pandas

Notice that we called statsmodels.formula.api instead of the usual statsmodels.api. The formula.api hosts many of the same functions found in api (e.g. OLS, GLM), but it also holds lower case counterparts for most of these models. In general, lower case models accept formula and df arguments, whereas upper case ones take endog and exog design matrices. formula accepts a string which describes the model in terms of a patsy formula. df takes a pandas data frame.

dir(smf) will print a list of available models.

Formula-compatible models have the following generic call signature: (formula, data, subset=None, *args, **kwargs)

OLS regression using formulas

To begin, we fit the linear model described on the Getting Started page. Download the data, subset columns, and list-wise delete to remove missing observations:

In [4]: df = sm.datasets.get_rdataset("Guerry", "HistData").data

URLErrorTraceback (most recent call last)
<ipython-input-4-8e82bb04cf4f> in <module>()
----> 1 df = sm.datasets.get_rdataset("Guerry", "HistData").data

/usr/src/packages/BUILD/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/datasets/utils.pyc in get_rdataset(dataname, package, cache)
    287                      "master/doc/"+package+"/rst/")
    288     cache = _get_cache(cache)
--> 289     data, from_cache = _get_data(data_base_url, dataname, cache)
    290     data = read_csv(data, index_col=0)
    291     data = _maybe_reset_index(data)

/usr/src/packages/BUILD/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/datasets/utils.pyc in _get_data(base_url, dataname, cache, extension)
    218     url = base_url + (dataname + ".%s") % extension
    219     try:
--> 220         data, from_cache = _urlopen_cached(url, cache)
    221     except HTTPError as err:
    222         if '404' in str(err):

/usr/src/packages/BUILD/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/datasets/utils.pyc in _urlopen_cached(url, cache)
    209     # not using the cache or didn't find it in cache
    210     if not from_cache:
--> 211         data = urlopen(url).read()
    212         if cache is not None:  # then put it in the cache
    213             _cache_it(data, cache_path)

/usr/lib/python2.7/urllib2.pyc in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    152     else:
    153         opener = _opener
--> 154     return opener.open(url, data, timeout)
    155 
    156 def install_opener(opener):

/usr/lib/python2.7/urllib2.pyc in open(self, fullurl, data, timeout)
    427             req = meth(req)
    428 
--> 429         response = self._open(req, data)
    430 
    431         # post-process response

/usr/lib/python2.7/urllib2.pyc in _open(self, req, data)
    445         protocol = req.get_type()
    446         result = self._call_chain(self.handle_open, protocol, protocol +
--> 447                                   '_open', req)
    448         if result:
    449             return result

/usr/lib/python2.7/urllib2.pyc in _call_chain(self, chain, kind, meth_name, *args)
    405             func = getattr(handler, meth_name)
    406 
--> 407             result = func(*args)
    408             if result is not None:
    409                 return result

/usr/lib/python2.7/urllib2.pyc in https_open(self, req)
   1239         def https_open(self, req):
   1240             return self.do_open(httplib.HTTPSConnection, req,
-> 1241                 context=self._context)
   1242 
   1243         https_request = AbstractHTTPHandler.do_request_

/usr/lib/python2.7/urllib2.pyc in do_open(self, http_class, req, **http_conn_args)
   1196         except socket.error, err: # XXX what error?
   1197             h.close()
-> 1198             raise URLError(err)
   1199         else:
   1200             try:

URLError: <urlopen error [Errno -3] Temporary failure in name resolution>

In [5]: df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()

NameErrorTraceback (most recent call last)
<ipython-input-5-c0f7df8f22c7> in <module>()
----> 1 df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()

NameError: name 'df' is not defined

In [6]: df.head()

NameErrorTraceback (most recent call last)
<ipython-input-6-2569c44faf66> in <module>()
----> 1 df.head()

NameError: name 'df' is not defined

Fit the model:

In [7]: mod = smf.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)

NameErrorTraceback (most recent call last)
<ipython-input-7-fc74d7ce0f53> in <module>()
----> 1 mod = smf.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)

NameError: name 'df' is not defined

In [8]: res = mod.fit()

NameErrorTraceback (most recent call last)
<ipython-input-8-deef2687e692> in <module>()
----> 1 res = mod.fit()

NameError: name 'mod' is not defined

In [9]: print(res.summary())

NameErrorTraceback (most recent call last)
<ipython-input-9-a8dc848a1f25> in <module>()
----> 1 print(res.summary())

NameError: name 'res' is not defined

Categorical variables

Looking at the summary printed above, notice that patsy determined that elements of Region were text strings, so it treated Region as a categorical variable. patsy‘s default is also to include an intercept, so we automatically dropped one of the Region categories.

If Region had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the C() operator:

In [10]: res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()

NameErrorTraceback (most recent call last)
<ipython-input-10-512c713f4dd3> in <module>()
----> 1 res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()

NameError: name 'df' is not defined

In [11]: print(res.params)

NameErrorTraceback (most recent call last)
<ipython-input-11-c61a950343b4> in <module>()
----> 1 print(res.params)

NameError: name 'res' is not defined

Examples more advanced features patsy‘s categorical variables function can be found here: Patsy: Contrast Coding Systems for categorical variables

Operators

We have already seen that “~” separates the left-hand side of the model from the right-hand side, and that “+” adds new columns to the design matrix.

Removing variables

The “-” sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by:

In [12]: res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()

NameErrorTraceback (most recent call last)
<ipython-input-12-01cb7f19a578> in <module>()
----> 1 res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()

NameError: name 'df' is not defined

In [13]: print(res.params)

NameErrorTraceback (most recent call last)
<ipython-input-13-c61a950343b4> in <module>()
----> 1 print(res.params)

NameError: name 'res' is not defined

Multiplicative interactions

”:” adds a new column to the design matrix with the product of the other two columns. “*” will also include the individual columns that were multiplied together:

In [14]: res1 = smf.ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()

NameErrorTraceback (most recent call last)
<ipython-input-14-b102884e9732> in <module>()
----> 1 res1 = smf.ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()

NameError: name 'df' is not defined

In [15]: res2 = smf.ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()

NameErrorTraceback (most recent call last)
<ipython-input-15-e7613c271b74> in <module>()
----> 1 res2 = smf.ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()

NameError: name 'df' is not defined

In [16]: print(res1.params)

NameErrorTraceback (most recent call last)
<ipython-input-16-9e7f9958623f> in <module>()
----> 1 print(res1.params)

NameError: name 'res1' is not defined

In [17]: print(res2.params)

NameErrorTraceback (most recent call last)
<ipython-input-17-5ef235c70f25> in <module>()
----> 1 print(res2.params)

NameError: name 'res2' is not defined

Many other things are possible with operators. Please consult the patsy docs to learn more.

Functions

You can apply vectorized functions to the variables in your model:

In [18]: res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()

NameErrorTraceback (most recent call last)
<ipython-input-18-4fe1546acec0> in <module>()
----> 1 res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()

NameError: name 'df' is not defined

In [19]: print(res.params)

NameErrorTraceback (most recent call last)
<ipython-input-19-c61a950343b4> in <module>()
----> 1 print(res.params)

NameError: name 'res' is not defined

Define a custom function:

In [20]: def log_plus_1(x):
   ....:     return np.log(x) + 1.
   ....: 

In [21]: print(res.params)

NameErrorTraceback (most recent call last)
<ipython-input-21-c61a950343b4> in <module>()
----> 1 print(res.params)

NameError: name 'res' is not defined

Namespaces

Notice that all of the above examples use the calling namespace to look for the functions to apply. The namespace used can be controlled via the eval_env keyword. For example, you may want to give a custom namespace using the patsy:patsy.EvalEnvironment or you may want to use a “clean” namespace, which we provide by passing eval_func=-1. The default is to use the caller’s namespace. This can have (un)expected consequences, if, for example, someone has a variable names C in the user namespace or in their data structure passed to patsy, and C is used in the formula to handle a categorical variable. See the Patsy API Reference for more information.

Using formulas with models that do not (yet) support them

Even if a given statsmodels function does not support formulas, you can still use patsy‘s formula language to produce design matrices. Those matrices can then be fed to the fitting function as endog and exog arguments.

To generate numpy arrays:

In [22]: import patsy

In [23]: f = 'Lottery ~ Literacy * Wealth'

In [24]: y, X = patsy.dmatrices(f, df, return_type='dataframe')

NameErrorTraceback (most recent call last)
<ipython-input-24-418e6636605c> in <module>()
----> 1 y, X = patsy.dmatrices(f, df, return_type='dataframe')

NameError: name 'df' is not defined

In [25]: print(y[:5])

NameErrorTraceback (most recent call last)
<ipython-input-25-8b56dfab7799> in <module>()
----> 1 print(y[:5])

NameError: name 'y' is not defined

In [26]: print(X[:5])

NameErrorTraceback (most recent call last)
<ipython-input-26-7dbdd9618cc6> in <module>()
----> 1 print(X[:5])

NameError: name 'X' is not defined

To generate pandas data frames:

In [27]: f = 'Lottery ~ Literacy * Wealth'

In [28]: y, X = patsy.dmatrices(f, df, return_type='dataframe')

NameErrorTraceback (most recent call last)
<ipython-input-28-418e6636605c> in <module>()
----> 1 y, X = patsy.dmatrices(f, df, return_type='dataframe')

NameError: name 'df' is not defined

In [29]: print(y[:5])

NameErrorTraceback (most recent call last)
<ipython-input-29-8b56dfab7799> in <module>()
----> 1 print(y[:5])

NameError: name 'y' is not defined

In [30]: print(X[:5])

NameErrorTraceback (most recent call last)
<ipython-input-30-7dbdd9618cc6> in <module>()
----> 1 print(X[:5])

NameError: name 'X' is not defined
In [31]: print(smf.OLS(y, X).fit().summary())

NameErrorTraceback (most recent call last)
<ipython-input-31-6bf304b23cf5> in <module>()
----> 1 print(smf.OLS(y, X).fit().summary())

NameError: name 'y' is not defined