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.

参数：x：vector or string

Data or column name in <cite>data</cite> for the predictor variable.

y：vector or string

Data or column name in <cite>data</cite> for the response variable.

data：DataFrame, optional

DataFrame to use if <cite>x</cite> and <cite>y</cite> 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 <cite>x</cite>, or column name(s) in <cite>data</cite>. These variables are treated as confounding and are removed from the <cite>x</cite> or <cite>y</cite> 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.

返回值：ax：matplotlib axes

Axes with the regression plot.

See also

Plot a simple linear regression model.marginal distrbutions.