lets_plot.geom_smooth

lets_plot.geom_smooth(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, orientation=None, method=None, n=None, se=None, level=None, span=None, deg=None, seed=None, max_n=None, color_by=None, fill_by=None, **other_args)

Add a smoothed conditional mean.

Parameters:

mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’smooth’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec, default=’identity’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content.

orientationstr, default=’x’

Specify the axis that the layer’s stat and geom should run along. Possible values: ‘x’, ‘y’.

methodstr, default=’lm’

Smoothing method: ‘lm’ (Linear Model) or ‘loess’ (Locally Estimated Scatterplot Smoothing). If value of deg parameter is greater than 1 then linear model becomes polynomial of the given degree.

nint

Number of points to evaluate smoother at.

sebool, default=True

Display confidence interval around smooth.

levelfloat, default=0.95

Level of confidence interval to use.

spanfloat, default=0.5

Only for ‘loess’ method. The fraction of source points closest to the current point is taken into account for computing a least-squares regression. A sensible value is usually 0.25 to 0.5.

degint, default=1

Degree of polynomial for linear regression model.

seedint

Random seed for ‘loess’ sampling.

max_nint, default=1000

Maximum number of data-points for ‘loess’ method. If this quantity exceeded random sampling is applied to data.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.

fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’

Define the fill aesthetic for the geometry.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns:

LayerSpec

Geom object specification.

Notes

geom_smooth() aids the eye in seeing patterns in the presence of overplotting.

Computed variables:

• ..y.. : predicted (smoothed) value.

• ..ymin.. : lower pointwise confidence interval around the mean.

• ..ymax.. : upper pointwise confidence interval around the mean.

• ..se.. : standard error.

geom_smooth() understands the following aesthetics mappings:

• x : x-axis value.

• y : y-axis value.

• alpha : transparency level of a layer. Accept values between 0 and 1.

• color (colour) : color of the geometry. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

• fill : fill color for the confidence interval around the line. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

• linetype : type of the line of conditional mean line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

• size : line width. Define line width for conditional mean and confidence bounds lines.

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
n = 50
x = np.arange(n)
y = x + np.random.normal(scale=10, size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_point() + geom_smooth()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
n = 100
x = np.linspace(-2, 2, n)
y = x ** 2 + np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_point() + geom_smooth(color='red', deg=2, se=False)

import numpy as np
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
t = np.linspace(0, 1, 100)
mean = 1 + np.zeros(2)
cov = np.eye(2)
x, y = np.random.multivariate_normal(mean, cov, t.size).T
df = pd.DataFrame({'t': t, 'x': x, 'y': y})
df = df.melt(id_vars=['t'], value_vars=['x', 'y'])
ggplot(df, aes(x='t', y='value', group='variable')) + \
    geom_point(aes(color='variable'), size=3, alpha=.5) + \
    geom_smooth(aes(color='variable'), size=1, \
                method='loess', span=.3, level=.7, seed=42)