lets_plot.geom_qq_line¶
- lets_plot.geom_qq_line(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, distribution=None, dparams=None, quantiles=None, color_by=None, **other_args)¶
Display quantile-quantile fitting line.
- 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 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_line’
The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘qq_line’ (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 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. 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).
- quantileslist, default=[0.25, 0.75]
Pair of quantiles to use when fitting the Q-Q line.
- color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’
Define the color 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 line plot is used for comparing two probability distributions (sample and theoretical) by plotting line passed through the pair of corresponding quantiles.
Computed variables:
..theoretical.. : theoretical quantiles.
..sample.. : sample quantiles.
geom_qq_line() understands the following aesthetics mappings:
sample : y-axis value.
alpha : transparency level of a layer. Accept values between 0 and 1.
color (colour) : color of the geometry. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.
linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash.
size : line width.
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() + geom_qq_line()
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') + \ 9 geom_qq_line(distribution='exp', quantiles=[0, 1])