plotnine.stat_smooth

stat_smooth(mapping=None, data=None, **kwargs)

Calculate a smoothed conditional mean

{usage}

Parameters

method : str | callable = "auto"

The available methods are:

"auto"       # Use loess if (n<1000), glm otherwise
"lm", "ols"  # Linear Model
"wls"        # Weighted Linear Model
"rlm"        # Robust Linear Model
"glm"        # Generalized linear Model
"gls"        # Generalized Least Squares
"lowess"     # Locally Weighted Regression (simple)
"loess"      # Locally Weighted Regression
"mavg"       # Moving Average
"gpr"        # Gaussian Process Regressor

If a callable is passed, it must have the signature:

def my_smoother(data, xseq, params):
    # * data - has the x and y values for the model
    # * xseq - x values to be predicted
    # * params - stat parameters
    #
    # It must return a new dataframe. Below is the
    # template used internally by Plotnine

    # Input data into the model
    x, y = data["x"], data["y"]

    # Create and fit a model
    model = Model(x, y)
    results = Model.fit()

    # Create output data by getting predictions on
    # the xseq values
    data = pd.DataFrame({
        "x": xseq,
        "y": results.predict(xseq)})

    # Compute confidence intervals, this depends on
    # the model. However, given standard errors and the
    # degrees of freedom we can compute the confidence
    # intervals using the t-distribution.
    #
    # For an alternative, implement confidence intervals by
    # the bootstrap method
    if params["se"]:
        from plotnine.utils.smoothers import tdist_ci
        y = data["y"]            # The predicted value
        df = 123                 # Degrees of freedom
        stderr = results.stderr  # Standard error
        level = params["level"]  # The parameter value
        low, high = tdist_ci(y, df, stderr, level)
        data["se"] = stderr
        data["ymin"] = low
        data["ymax"] = high

    return data

For loess smoothing you must install the scikit-misc package. You can install it using with pip install scikit-misc or pip install plotnine[all].

formula : formula_like = None

An object that can be used to construct a patsy design matrix. This is usually a string. You can only use a formula if method is one of lm, ols, wls, glm, rlm or gls, and in the formula you may refer to the x and y aesthetic variables.

se : bool = True

If True draw confidence interval around the smooth line.

n : int = 80

Number of points to evaluate the smoother at. Some smoothers like mavg do not support this.

fullrange : bool = False

If True the fit will span the full range of the plot.

level : float = 0.95

Level of confidence to use if se=True.

span : float = 2/3.

Controls the amount of smoothing for the loess smoother. Larger number means more smoothing. It should be in the (0, 1) range.

method_args : dict = {}

Additional arguments passed on to the modelling method.

Notes

geom_smooth and stat_smooth are effectively aliases, they both use the same arguments. Use geom_smooth unless you want to display the results with a non-standard geom.

Parameters

See Also

Notes