WebSource code for statsmodels.stats.diagnostic. # -*- coding: utf-8 -*- """ Various Statistical Tests Author: josef-pktd License: BSD-3 Notes ----- Almost fully verified against R or Gretl, not all options are the same. In many cases of Lagrange multiplier tests both the LM test and the F test is returned. WebRegressionResults.predict(exog=None, transform=True, *args, **kwargs) ¶. Call self.model.predict with self.params as the first argument. Parameters: exog array_like, optional. The values for which you want to predict. see Notes below. transform bool, optional. If the model was fit via a formula, do you want to pass exog through the formula.
statsmodels.regression.linear_model.RegressionResults
WebAug 7, 2024 · Each table in this attribute (which is a list of tables) is a SimpleTable, which has methods for outputting different formats. We can then read any of those formats back as a pd.DataFrame: import statsmodels.api as sm model = sm.OLS (y,x) results = model.fit () results_summary = results.summary () # Note that tables is a list. Webstatsmodels.base.model.Results.predict. Call self.model.predict with self.params as the first argument. The values for which you want to predict. see Notes below. If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log (x1) + log (x2), and transform is True, then you ... havilah ravula
Linear Regression in Python using Statsmodels - GeeksforGeeks
WebMay 19, 2013 · The models and results instances all have a save and load method, so you don't need to use the pickle module directly. Edit to add an example:. import statsmodels.api as sm data = sm.datasets.longley.load_pandas() data.exog['constant'] = 1 results = sm.OLS(data.endog, data.exog).fit() results.save("longley_results.pickle") # we should … Webstatsmodels 0.14.0 (+770) statsmodels.tsa.vector_ar.var_model.VARResults Type to start searching statsmodels User Guide ... Simulates impulse response function, returning an array of simulations. is_stable ([verbose]) Determine stability based on model coefficients. long_run_effects Compute long-run effect of unit impulse. WebReturn an information criterion for the model. initialize (model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. llf_scaled ([scale]) Return the log-likelihood at the given scale, using the estimated scale if the provided scale is None. load (fname) Load a pickled results instance. normalized_cov_params () havilah seguros