Regression in Python using R-style formula – it’s easy!

Datetime:2016-08-22 22:26:40          Topic: Python  R Program           Share

I remember experimenting with doing regressions in Python using R-style formulae a long time ago, and I remember it being a bit complicated. Luckily it’s become really easy now – and I’ll show you just how easy.

Before running this you will need to install the pandas , statsmodels and patsy packages. If you’re using conda you should be able to do this by running the following from the terminal:

conda install statsmodels patsy

(and then say yes when it asks you to confirm it)

import pandas as pd
from statsmodels.formula.api import ols

Before we can do any regression, we need some data – so lets read some data on cars:

df = pd.read_csv("http://web.pdx.edu/~gerbing/data/cars.csv")

You may have noticed from the code above that you can just give a URL to the read_csv function and it will download it and open it – handy!

Anyway, here is the data:

df.head()
Model MPG Cylinders Engine Disp Horsepower Weight Accelerate Year Origin
0 amc ambassador dpl 15.0 8 390.0 190 3850 8.5 70 American
1 amc gremlin 21.0 6 199.0 90 2648 15.0 70 American
2 amc hornet 18.0 6 199.0 97 2774 15.5 70 American
3 amc rebel sst 16.0 8 304.0 150 3433 12.0 70 American
4 buick estate wagon (sw) 14.0 8 455.0 225 3086 10.0 70 American

Before we do our regression it might be a good idea to look at simple correlations between columns. We can get the correlations between each pair of columns using the corr() method:

df.corr()
MPG Cylinders Engine Disp Horsepower Weight Accelerate Year
MPG 1.000000 -0.777618 -0.805127 -0.778427 -0.832244 0.423329 0.580541
Cylinders -0.777618 1.000000 0.950823 0.842983 0.897527 -0.504683 -0.345647
Engine Disp -0.805127 0.950823 1.000000 0.897257 0.932994 -0.543800 -0.369855
Horsepower -0.778427 0.842983 0.897257 1.000000 0.864538 -0.689196 -0.416361
Weight -0.832244 0.897527 0.932994 0.864538 1.000000 -0.416839 -0.309120
Accelerate 0.423329 -0.504683 -0.543800 -0.689196 -0.416839 1.000000 0.290316
Year 0.580541 -0.345647 -0.369855 -0.416361 -0.309120 0.290316 1.000000

Now we can do some regression using R-style formulae. In this case we’re trying to predict MPG based on the year that the car was released:

model = ols("MPG ~ Year", data=df)
results = model.fit()

The ‘formula’ that we used above is the same as R uses: on the left is the dependent variable, on the right is the independent variable. The ols method is nice and easy, we just give it the formula, and then the DataFrame to use to get the data from (in this case, it’s called df ). We then call fit() to actually do the regression.

We can easily get a summary of the results here – including all sorts of crazy statistical measures!

results.summary()
OLS Regression Results
Dep. Variable: MPG R-squared: 0.337
Model: OLS Adj. R-squared: 0.335
Method: Least Squares F-statistic: 198.3
Date: Sat, 20 Aug 2016 Prob (F-statistic): 1.08e-36
Time: 10:42:17 Log-Likelihood: -1280.6
No. Observations: 392 AIC: 2565.
Df Residuals: 390 BIC: 2573.
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.0117 6.645 -10.536 0.000 -83.076 -56.947
Year 1.2300 0.087 14.080 0.000 1.058 1.402
Omnibus: 21.407 Durbin-Watson: 1.121
Prob(Omnibus): 0.000 Jarque-Bera (JB): 15.843
Skew: 0.387 Prob(JB): 0.000363
Kurtosis: 2.391 Cond. No. 1.57e+03

We can do a more complex model easily too. First lets list the columns of the data to remind us what variables we have:

df.columns
Index(['Model', 'MPG', 'Cylinders', 'Engine Disp', 'Horsepower', 'Weight',
       'Accelerate', 'Year', 'Origin'],
      dtype='object')

We can now add in more variables – doing multiple regression:

model = ols("MPG ~ Year + Weight + Horsepower", data=df)
results = model.fit()
results.summary()
OLS Regression Results
Dep. Variable: MPG R-squared: 0.808
Model: OLS Adj. R-squared: 0.807
Method: Least Squares F-statistic: 545.4
Date: Sat, 20 Aug 2016 Prob (F-statistic): 9.37e-139
Time: 10:42:17 Log-Likelihood: -1037.4
No. Observations: 392 AIC: 2083.
Df Residuals: 388 BIC: 2099.
Df Model: 3
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -13.7194 4.182 -3.281 0.001 -21.941 -5.498
Year 0.7487 0.052 14.365 0.000 0.646 0.851
Weight -0.0064 0.000 -15.768 0.000 -0.007 -0.006
Horsepower -0.0050 0.009 -0.530 0.597 -0.024 0.014
Omnibus: 41.952 Durbin-Watson: 1.423
Prob(Omnibus): 0.000 Jarque-Bera (JB): 69.490
Skew: 0.671 Prob(JB): 8.14e-16
Kurtosis: 4.566 Cond. No. 7.48e+04

We can see that bringing in some extra variables has increased the $R^2$ value from ~0.3 to ~0.8 – although we can see that the P value for the Horsepower is very high. If we remove Horsepower from the regression then it barely changes the results:

model = ols("MPG ~ Year + Weight", data=df)
results = model.fit()
results.summary()
OLS Regression Results
Dep. Variable: MPG R-squared: 0.808
Model: OLS Adj. R-squared: 0.807
Method: Least Squares F-statistic: 819.5
Date: Sat, 20 Aug 2016 Prob (F-statistic): 3.33e-140
Time: 10:42:17 Log-Likelihood: -1037.6
No. Observations: 392 AIC: 2081.
Df Residuals: 389 BIC: 2093.
Df Model: 2
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -14.3473 4.007 -3.581 0.000 -22.224 -6.470
Year 0.7573 0.049 15.308 0.000 0.660 0.855
Weight -0.0066 0.000 -30.911 0.000 -0.007 -0.006
Omnibus: 42.504 Durbin-Watson: 1.425
Prob(Omnibus): 0.000 Jarque-Bera (JB): 71.997
Skew: 0.670 Prob(JB): 2.32e-16
Kurtosis: 4.616 Cond. No. 7.17e+04

We can also see if introducing categorical variables helps with the regression. In this case, we only have one categorical variable, called Origin . Patsy automatically treats strings as categorical variables, so we don’t have to do anything special – but if needed we could wrap the variable name in C() to force it to be a categorical variable.

model = ols("MPG ~ Year + Origin", data=df)
results = model.fit()
results.summary()
OLS Regression Results
Dep. Variable: MPG R-squared: 0.579
Model: OLS Adj. R-squared: 0.576
Method: Least Squares F-statistic: 178.0
Date: Sat, 20 Aug 2016 Prob (F-statistic): 1.42e-72
Time: 10:42:17 Log-Likelihood: -1191.5
No. Observations: 392 AIC: 2391.
Df Residuals: 388 BIC: 2407.
Df Model: 3
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -61.2643 5.393 -11.360 0.000 -71.868 -50.661
Origin[T.European] 7.4784 0.697 10.734 0.000 6.109 8.848
Origin[T.Japanese] 8.4262 0.671 12.564 0.000 7.108 9.745
Year 1.0755 0.071 15.102 0.000 0.935 1.216
Omnibus: 10.231 Durbin-Watson: 1.656
Prob(Omnibus): 0.006 Jarque-Bera (JB): 10.589
Skew: 0.402 Prob(JB): 0.00502
Kurtosis: 2.980 Cond. No. 1.60e+03

You can see here that Patsy has automatically created extra variables for Origin : in this case, European and Japanese, with the ‘default’ being American. You can configure how this is done very easily – see here .

Just for reference, you can easily get any of the statistical outputs as attributes on the results object:

results.rsquared
0.57919459237581172
results.params
Intercept            -61.264305
Origin[T.European]     7.478449
Origin[T.Japanese]     8.426227
Year                   1.075484
dtype: float64

You can also really easily use the model to predict based on values you’ve got:

results.predict({'Year':1990, 'Origin':'European'})
array([ 2086.42634156])

Categorised as: Programming , Python





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