lets_plot.geom_smooth¶
- lets_plot.geom_smooth(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, method=None, n=None, se=None, level=None, span=None, deg=None, seed=None, max_n=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 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
Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
- show_legendbool, default=True
False - do not show legend for this layer.
- samplingFeatureSpec
Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
- tooltipslayer_tooltips
Result of the call to the layer_tooltips() function. Specifies appearance, style and content.
- methodstr, default=’lm’
Smoothing method: ‘lm’ (Linear Model) or ‘loess’ (Locally Estimated Scatterplot Smoothing).
- 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.
- 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. Understands numbers between 0 and 1.
color (colour) : color of a geometry. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.
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 : lines width. Defines line width for conditional mean and confidence bounds lines.
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4np.random.seed(42) 5n = 50 6x = np.arange(n) 7y = x + np.random.normal(scale=10, size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ 9 geom_point() + geom_smooth()