seaborn.residplot¶

seaborn.residplot(x, y, data=None, lowess=False, x_partial=None, y_partial=None, order=1, robust=False, dropna=True, label=None, color=None, scatter_kws=None, line_kws=None, ax=None)¶

Plot the residuals of a linear regression.

This function will regress y on x (possibly as a robust or polynomial regression) and then draw a scatterplot of the residuals. You can optionally fit a lowess smoother to the residual plot, which can help in determining if there is structure to the residuals.

Parameters:

x : vector or string Data or column name in data for the predictor variable. y : vector or string Data or column name in data for the response variable. data : DataFrame, optional DataFrame to use if x and y are column names. lowess : boolean, optional Fit a lowess smoother to the residual scatterplot. {x, y}_partial : matrix or string(s) , optional Matrix with same first dimension as x, or column name(s) in data. These variables are treated as confounding and are removed from the x or y variables before plotting. order : int, optional Order of the polynomial to fit when calculating the residuals. robust : boolean, optional Fit a robust linear regression when calculating the residuals. dropna : boolean, optional If True, ignore observations with missing data when fitting and plotting. label : string, optional Label that will be used in any plot legends. color : matplotlib color, optional Color to use for all elements of the plot. {scatter, line}_kws : dictionaries, optional Additional keyword arguments passed to scatter() and plot() for drawing the components of the plot. ax : matplotlib axis, optional Plot into this axis, otherwise grab the current axis or make a new one if not existing.

Returns:

ax: matplotlib axes Axes with the regression plot.

See also

regplot

Plot a simple linear regression model.

jointplot

marginal distrbutions.