statsmodels.graphics.tsaplots.plot_pacf
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statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, **kwargs)
[source] -
Plot the partial autocorrelation function
Plots lags on the horizontal and the correlations on vertical axis.
Parameters: x : array_like
Array of time-series values
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being created.
lags : array_like, optional
Array of lag values, used on horizontal axis. If not given,
lags=np.arange(len(corr))
is used.alpha : scalar, optional
If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x))
method : ?ywunbiased? (default) or ?ywmle? or ?ols?
specifies which method for the calculations to use:
- yw or ywunbiased : yule walker with bias correction in denominator for acovf
- ywm or ywmle : yule walker without bias correction
- ols - regression of time series on lags of it and on constant
- ld or ldunbiased : Levinson-Durbin recursion with bias correction
- ldb or ldbiased : Levinson-Durbin recursion without bias correction
use_vlines : bool, optional
If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is ?o?; it can be overridden with a
marker
kwarg.**kwargs : kwargs, optional
Optional keyword arguments that are directly passed on to the Matplotlib
plot
andaxhline
functions.Returns: fig : Matplotlib figure instance
If
ax
is None, the created figure. Otherwise the figure to whichax
is connected.See also
matplotlib.pyplot.xcorr
,matplotlib.pyplot.acorr
,mpl_examples
Notes
Adapted from matplotlib?s
xcorr
.Data are plotted as
plot(lags, corr, **kwargs)
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