statsmodels.graphics.tsaplots.plot_pacf¶
-
statsmodels.graphics.tsaplots.
plot_pacf
(x, ax=None, lags=None, alpha=0.05, method='ywunbiased', use_vlines=True, title='Partial Autocorrelation', zero=True, vlines_kwargs=None, **kwargs)[source]¶ Plot the partial autocorrelation function
- Parameters
- xarray_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
int
or array_like,optional
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{‘ywunbiased’, ‘ywmle’, ‘ols’}
Specifies which method for the calculations to use:
yw or ywunbiased : yule walker with bias correction in denominator for acovf. Default.
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_vlinesbool,
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’
- zerobool,
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
andaxhline
functions.
- Returns
- fig
Matplotlib
figure
instance
If ax is None, the created figure. Otherwise the figure to which ax is connected.
- fig
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_acf(dta.values.squeeze(), lags=40) >>> plt.show()
(Source code, png, hires.png, pdf)