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Does anyone know how to display the regression equation in seaborn using sns.regplot or sns.jointplot? regplot doesn't seem to have any parameter that you can be pass to display regression diagnostics, and jointplot only displays the pearson R^2, and p-value. I'm looking for a way to see the slope coefficient, standard error, and intercept as well.
Thanks
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regplot() : This method is used to plot data and a linear regression model fit. There are a number of mutually exclusive options for estimating the regression model.
Regression plots in seaborn can be easily implemented with the help of the lmplot() function. lmplot() can be understood as a function that basically creates a linear model plot. lmplot() makes a very simple linear regression plot.It creates a scatter plot with a linear fit on top of it.
While regplot() always shows a single relationship, lmplot() combines regplot() with FacetGrid to provide an easy interface to show a linear regression on “faceted” plots that allow you to explore interactions with up to three additional categorical variables.
By default seaborn fits the length of regression line according to the length of x axis. Another option is to use argument truncate=True - that would limit the regression line only to the extent of data. Other options? In my example I want the lower regression line to be extended down till x=0.
Seaborn | Regression Plots Last Updated : 17 Sep, 2019 The regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses. Regression plots as the name suggests creates a regression line between 2 parameters and helps to visualize their linear relationships.
Two main functions in seaborn are used to visualize a linear relationship as determined through regression. These functions, regplot () and lmplot () are closely related, and share much of their core functionality.
In contrast, the size and shape of the lmplot () figure is controlled through the FacetGrid interface using the height and aspect parameters, which apply to each facet in the plot, not to the overall figure itself: A few other seaborn functions use regplot () in the context of a larger, more complex plot.
This is because regplot () is an “axes-level” function draws onto a specific axes. This means that you can make multi-panel figures yourself and control exactly where the regression plot goes.
In 2015, the lead developer for seaborn replied to a feature request asking for access to the statistical values used to generate plots by saying, "It is not available, and it will not be made available."
So, unfortunately, this feature does not exist in seaborn, and seems unlikely to exist in the future.
Update: in March 2018, seaborn's lead developer reiterated his opposition to this feature. He seems... uninterested in further discussion.
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A late and partial answer. I had the problem of just wanting to get the data of the regression line and I found this:
When you have this plot:
f = mp.figure() ax = f.add_subplot(1,1,1) p = sns.regplot(x=dat.x,y=ydat,data=dat,ax=ax) Then p has a method get_lines() which gives back a list of line2D objects. And a line2D object has methods to get the desired data:
So to get the linear regression data in this example, you just need to do this:
p.get_lines()[0].get_xdata() p.get_lines()[0].get_ydata() Those calls return each a numpy array of the regression line data points which you can use freely.
Using p.get_children() you get a list of the individual elements of the plot.
The path information of the confidence interval plot can be found with:
p.get_children()[1].get_paths() It's in the form of tuples of data points.
Generally a lot can be found by using the dir() command on any Python object, it just shows everything that's in there.
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