geom_qq_line#

geom_qq_line(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=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 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_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 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.

inherit_aesbool, default=True

False - do not combine the layer aesthetic mappings with the plot shared mappings.

manual_keystr or layer_key

The key to show in the manual legend. Specify text for the legend label or advanced settings using the layer_key() function.

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. Set tooltips=’none’ to hide tooltips from the layer.

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. For more info see Color and Fill.

  • linetype : type of the line. Accept codes or names (0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’), a hex string (up to 8 digits for dash-gap lengths), or a list pattern [offset, [dash, gap, …]] / [dash, gap, …]. For more info see Line Types.

  • 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])
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