statsmodels.graphics.gofplots.qqplot_2samples
-
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 toProbPlot
instances using only the default values - so useProbPlot
instances if finer-grained control of the quantile computations is required.Parameters: data1, data2 : array-like (1d) or
ProbPlot
instancesxlabel, 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 whichax
is connected.See also
scipy.stats.probplot
Notes
- Depends on matplotlib.
- If
data1
anddata2
are notProbPlot
instances, instances will be created using the default parameters. Therefore, it is recommended to useProbPlot
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):
Please login to continue.