R abline() equivalent in Python
Question:
I am trying to plot a Linear Regression onto a scatterplot in Python.
In R I would simply do the following:
Run OLS Linear Regresion
fit_1 <- lm(medv ~ lstat)
plot(medv ~ lstat)
abline(fit_1, col = "red")
I have been looking at different solutions in Python, but I can’t seem to be able to actually get it to work.
My script is:
Plot Data
Boston.plot(kind='scatter', x='medv', y='lstat', color = "black")
plt.show()
Run Linear Regression
fit_1 = sm.ols(formula='medv ~ lstat', data= Boston).fit()
Show Summary
fit_1.summary()
Plot Regression Line
Insert code here
Answers:
Try this:
plt.plot(Boston.lstat, fit_1.fittedvalues, 'r')
It can be done quite simply. In the below code, I use sklearn to fit the model and predict the values.
import pandas as pd
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
model = LinearRegression()
model.fit(X,y)
predictions = model.predict(X)
plt.plot(X,y,'o')
# change here
plt.plot(X, predictions, '-')
plt.show()
Saw this on Statology that helped me a lot:
def abline(slope, intercept):
axes = plt.gca()
x_vals = np.array(axes.get_xlim())
y_vals = intercept + slope * x_vals
plt.plot(x_vals, y_vals, '--')
I am trying to plot a Linear Regression onto a scatterplot in Python.
In R I would simply do the following:
Run OLS Linear Regresion
fit_1 <- lm(medv ~ lstat)
plot(medv ~ lstat)
abline(fit_1, col = "red")
I have been looking at different solutions in Python, but I can’t seem to be able to actually get it to work.
My script is:
Plot Data
Boston.plot(kind='scatter', x='medv', y='lstat', color = "black")
plt.show()
Run Linear Regression
fit_1 = sm.ols(formula='medv ~ lstat', data= Boston).fit()
Show Summary
fit_1.summary()
Plot Regression Line
Insert code here
Try this:
plt.plot(Boston.lstat, fit_1.fittedvalues, 'r')
It can be done quite simply. In the below code, I use sklearn to fit the model and predict the values.
import pandas as pd
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
model = LinearRegression()
model.fit(X,y)
predictions = model.predict(X)
plt.plot(X,y,'o')
# change here
plt.plot(X, predictions, '-')
plt.show()
Saw this on Statology that helped me a lot:
def abline(slope, intercept):
axes = plt.gca()
x_vals = np.array(axes.get_xlim())
y_vals = intercept + slope * x_vals
plt.plot(x_vals, y_vals, '--')