statsmodels.graphics.regressionplots.plot_partregress statsmodels.graphics.regressionplots.plot_partregress(endog, exog_i, exog_others, data=None, title_kwargs={}, obs_labels=True, label_kwargs={}, ax=None, ret_coords=False, **kwargs) [source] Plot partial regression for a single regressor. Parameters: endog : ndarray or string endogenous or response variable. If string is given, you can use a arbitrary translations as with a formula. exog_i : ndarray or string exogenous, explanatory var

Dates in timeseries models Link to Notebook GitHub In [1]: from __future__ import print_function import statsmodels.api as sm import numpy as np import pandas as pd Getting started In [2]: data = sm.datasets.sunspots.load() Right now an annual date series must be datetimes at the end of the year. In [3]: from datetime import datetime dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) Using Pandas Make a pandas TimeSeries

statsmodels.regression.linear_model.RegressionResults.f_test RegressionResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses

statsmodels.regression.linear_model.OLSResults.get_robustcov_results OLSResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for robust covariance c

statsmodels.graphics.functional.fboxplot statsmodels.graphics.functional.fboxplot(data, xdata=None, labels=None, depth=None, method='MBD', wfactor=1.5, ax=None, plot_opts={}) [source] Plot functional boxplot. A functional boxplot is the analog of a boxplot for functional data. Functional data is any type of data that varies over a continuum, i.e. curves, probabillity distributions, seasonal data, etc. The data is first ordered, the order statistic used here is banddepth. Plotted are then the

statsmodels.tsa.ar_model.ARResults.f_test ARResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a s

statsmodels.iolib.table.SimpleTable.pop SimpleTable.pop([index]) ? item -- remove and return item at index (default last). Raises IndexError if list is empty or index is out of range.

statsmodels.tsa.vector_ar.irf.IRAnalysis.H static IRAnalysis.H() [source]

statsmodels.discrete.discrete_model.BinaryResults.t_test BinaryResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple o

statsmodels.discrete.discrete_model.NegativeBinomialResults.wald_test NegativeBinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the