statsmodels.miscmodels.count.PoissonGMLE.nloglikeobs PoissonGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.sandbox.regression.gmm.IVGMM.gmmobjective IVGMM.gmmobjective(params, weights) objective function for GMM minimization Parameters: params : array parameter values at which objective is evaluated weights : array weighting matrix Returns: jval : float value of objective function

statsmodels.tsa.arima_model.ARMAResults.bic static ARMAResults.bic() [source]

statsmodels.discrete.discrete_model.MultinomialModel.predict MultinomialModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous

statsmodels.regression.quantile_regression.QuantRegResults.llf static QuantRegResults.llf() [source]

Statsmodels Examples This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. If you are not comfortable with git, we also encourage users to submit their own examples, tutorials or cool statsmodels tricks to the Examples wiki page. Topics Linear Regression Models Plotting Discrete Choice Models Nonparamet

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.rvs ExpTransf_gen.rvs(*args, **kwds) Random variates of given type. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1). size : int or tuple of ints, optional Defining number of random variates (default=1). Retu

statsmodels.discrete.discrete_model.BinaryResults.cov_params BinaryResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like

statsmodels.tsa.arima_process.arma_acf statsmodels.tsa.arima_process.arma_acf(ar, ma, nobs=10) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, m

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.stats TransfTwo_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo