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.