lets_plot.geom_qq#
- lets_plot.geom_qq(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, distribution=None, dparams=None, color_by=None, fill_by=None, **other_args)#
Display quantile-quantile plot.
- 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=’qq’
The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘qq’ (compare two probability distributions), ‘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.
- distribution{‘norm’, ‘uniform’, ‘t’, ‘gamma’, ‘exp’, ‘chi2’}, default=’norm’
Distribution function to use.
- dparamslist
Additional parameters (of float type) passed on to distribution function. If distribution is ‘norm’ then dparams is a pair [mean, std] (=[0.0, 1.0] by default). If distribution is ‘uniform’ then dparams is a pair [a, b] (=[0.0, 1.0] by default). If distribution is ‘t’ then dparams is an integer number [d] (=[1] by default). If distribution is ‘gamma’ then dparams is a pair [alpha, beta] (=[1.0, 1.0] by default). If distribution is ‘exp’ then dparams is a float number [lambda] (=[1.0] by default). If distribution is ‘chi2’ then dparams is an integer number [k] (=[1] by default).
- 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
The Q-Q plot is used for comparing two probability distributions (sample and theoretical) by plotting their quantiles against each other.
If the two distributions being compared are similar, the points in the Q-Q plot will approximately lie on the straight line.
Computed variables:
..theoretical.. : theoretical quantiles.
..sample.. : sample quantiles.
geom_qq() understands the following aesthetics mappings:
sample : y-axis value.
alpha : transparency level of a point. 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. Is applied only to the points of shapes having inner area. 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”).
shape : shape of the point, an integer from 0 to 25.
size : size of the point.
stroke : width of the shape border. Applied only to the shapes having border.
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6sample = np.random.normal(0, 1, n) 7ggplot({'sample': sample}, aes(sample='sample')) + geom_qq()
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6sample = np.random.exponential(1, n) 7ggplot({'sample': sample}, aes(sample='sample')) + \ 8 geom_qq(distribution='exp')