statsmodels.graphics.gofplots.qqplot_2samples¶
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statsmodels.graphics.gofplots.
qqplot_2samples
(data1, data2, xlabel=None, ylabel=None, line=None, ax=None)[source]¶ Q-Q Plot of two samples’ quantiles.
Can take either two ProbPlot instances or two array-like objects. In the case of the latter, both inputs will be converted to ProbPlot instances using only the default values - so use ProbPlot instances if finer-grained control of the quantile computations is required.
Parameters: data1, data2 : array-like (1d) or ProbPlot instances
xlabel, ylabel : str or None
User-provided labels for the x-axis and y-axis. If None (default), other values are used.
line : str {‘45’, ‘s’, ‘r’, q’} or None
Options for the reference line to which the data is compared:
- ‘45’ - 45-degree line
- ‘s’ - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them
- ‘r’ - A regression line is fit
- ‘q’ - A line is fit through the quartiles.
- None - by default no reference line is added to the plot.
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being created.
Returns: fig : Matplotlib figure instance
If ax is None, the created figure. Otherwise the figure to which ax is connected.
See also
scipy.stats.probplot
Notes
- Depends on matplotlib.
- If data1 and data2 are not ProbPlot instances, instances will be created using the default parameters. Therefore, it is recommended to use ProbPlot instance if fine-grained control is needed in the computation of the quantiles.
Examples
>>> x = np.random.normal(loc=8.5, scale=2.5, size=37) >>> y = np.random.normal(loc=8.0, scale=3.0, size=37) >>> pp_x = sm.ProbPlot(x) >>> pp_y = sm.ProbPlot(y) >>> qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None):