statsmodels.graphics.tsaplots.plot_pacf

statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, title='Partial Autocorrelation', zero=True, vlines_kwargs=None, **kwargs)[source]

Plot the partial autocorrelation function

Parameters:
x : array_like

Array of time-series values

ax : AxesSubplot, optional

If given, this subplot is used to plot in instead of a new figure being created.

lags : {int, array_like}, optional

An int or array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int. If not provided, lags=np.arange(len(corr)) is used.

alpha : float, 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 : str

Specifies which method for the calculations to use:

  • ”ywm” or “ywmle” : Yule-Walker without adjustment. Default.

  • ”yw” or “ywadjusted” : Yule-Walker with sample-size adjustment in denominator for acovf. Default.

  • ”ols” : regression of time series on lags of it and on constant.

  • ”ols-inefficient” : regression of time series on lags using a single common sample to estimate all pacf coefficients.

  • ”ols-adjusted” : regression of time series on lags with a bias adjustment.

  • ”ld” or “ldadjusted” : 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.

title : str, optional

Title to place on plot. Default is ‘Partial Autocorrelation’

zero : bool, optional

Flag indicating whether to include the 0-lag autocorrelation. Default is True.

vlines_kwargs : dict, optional

Optional dictionary of keyword arguments that are passed to vlines.

**kwargs : kwargs, optional

Optional keyword arguments that are directly passed on to the Matplotlib plot and axhline functions.

Returns:

If ax is None, the created figure. Otherwise the figure to which ax is connected.

Return type:

Figure

See also

matplotlib.pyplot.xcorr, matplotlib.pyplot.acorr

Notes

Plots lags on the horizontal and the correlations on vertical axis. Adapted from matplotlib’s xcorr.

Data are plotted as plot(lags, corr, **kwargs)

kwargs is used to pass matplotlib optional arguments to both the line tracing the autocorrelations and for the horizontal line at 0. These options must be valid for a Line2D object.

vlines_kwargs is used to pass additional optional arguments to the vertical lines connecting each autocorrelation to the axis. These options must be valid for a LineCollection object.

Examples

>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm

>>> dta = sm.datasets.sunspots.load_pandas().data
>>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
>>> del dta["YEAR"]
>>> sm.graphics.tsa.plot_pacf(dta.values.squeeze(), lags=40, method="ywm")
>>> plt.show()

(Source code, png, hires.png, pdf)

../_images/graphics_tsa_plot_pacf.png