plotnine.stats.stat_qq

class plotnine.stats.stat_qq(*args, **kwargs)[source]

Calculation for quantile-quantile plot

Usage

stat_qq(mapping=None, data=None, geom='qq', position='identity', na_rm=False,
        quantiles=None, distribution='norm', alpha_beta=(0.375, 0.375),
        dparams=(), **kwargs)

Only the mapping and data can be positional, the rest must be keyword arguments. **kwargs can be aesthetics (or parameters) used by the geom.

Parameters:
mapping : aes, optional

Aesthetic mappings created with aes(). If specified and inherit.aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic Default value
sample  
x 'stat(theoretical)'
y 'stat(sample)'

The bold aesthetics are required.

Options for computed aesthetics

'theoretical'  # theoretical quantiles
'sample'       # sample quantiles
data : dataframe, optional

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

geom : str or stat, optional (default: qq)

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str or position, optional (default: identity)

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool, optional (default: False)

If False, removes missing values with a warning. If True silently removes missing values.

distribution : str (default: norm)

Distribution or distribution function name. The default is norm for a normal probability plot. Objects that look enough like a stats.distributions instance (i.e. they have a ppf method) are also accepted. See scipy stats for available distributions.

dparams : dict

Distribution-specific shape parameters (shape parameters plus location and scale).

quantiles : array_like, optional

Probability points at which to calculate the theoretical quantile values. If provided, must be the same number as as the sample data points. The default is to use calculated theoretical points, use to alpha_beta control how these points are generated.

alpha_beta : tuple

Parameter values to use when calculating the quantiles. Default is (3/8, 3/8).

See also

scipy.stats.mstats.plotting_positions
to calculate the quantiles.