statsmodels.regression.quantile_regression.QuantRegResults.fvalue static QuantRegResults.fvalue()

statsmodels.tsa.arima_model.ARMA.fit ARMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to trans

statsmodels.graphics.regressionplots.plot_fit statsmodels.graphics.regressionplots.plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs) [source] Plot fit against one regressor. This creates one graph with the scatterplot of observed values compared to fitted values. Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes x_var : int or str Name or index of regressor in exog matrix. y_true : array_like (optional) If this is not N

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.jac_func NonlinearIVGMM.jac_func(params, weights, args=None, centered=True, epsilon=None) [source]

Plot Interaction of Categorical Factors Link to Notebook GitHub In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers. In [1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd from statsmodels.graphics.factorplots import interaction_plot In [2]: np.random.

statsmodels.sandbox.regression.gmm.GMMResults.pvalues static GMMResults.pvalues()

statsmodels.tsa.arima_model.ARMA.loglike ARMA.loglike(params, set_sigma2=True) [source] Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.discrete.discrete_model.Poisson.from_formula classmethod Poisson.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.tsa.ar_model.ARResults.load classmethod ARResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tsa.ar_model.ARResults.save ARResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data is true and