statsmodels.genmod.families.family.NegativeBinomial.loglike NegativeBinomial.loglike(endog, lin_pred=None) [source] The loglikelihood function for the negative binomial family. Parameters: endog : array-like Endogenous response variable lin_pred : array-like The linear predictor of the model. This is dot(exog,params), plus the offset if present. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes sum(endog*log(alpha*exp

statsmodels.stats.weightstats.DescrStatsW.mean static DescrStatsW.mean() [source] weighted mean of data

statsmodels.discrete.discrete_model.NegativeBinomialResults.load classmethod NegativeBinomialResults.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.sandbox.distributions.transformed.LogTransf_gen.expect LogTransf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit ExpTransf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.sandbox.distributions.transformed.LogTransf_gen.mean LogTransf_gen.mean(*args, **kwds) Mean of the distribution 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) Returns: mean : float the mean of the distribution

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.interval ExpTransf_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parame

statsmodels.tsa.arima_process.ArmaProcess.generate_sample ArmaProcess.generate_sample(nsample=100, scale=1.0, distrvs=None, axis=0, burnin=0) [source] generate ARMA samples Parameters: nsample : int or tuple of ints If nsample is an integer, then this creates a 1d timeseries of length size. If nsample is a tuple, then the timeseries is along axis. All other axis have independent arma samples. scale : float standard deviation of noise distrvs : function, random number generator function

statsmodels.sandbox.distributions.transformed.LogTransf_gen.cdf LogTransf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles 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) Returns: cdf : ndarray Cumulative distributi

statsmodels.discrete.discrete_model.MultinomialResults.resid_misclassified static MultinomialResults.resid_misclassified() [source] Residuals indicating which observations are misclassified. Notes The residuals for the multinomial model are defined as where is the index of the category for the endogenous variable and is the index of the predicted probabilities for each category. That is, the residual is a binary indicator that is 0 if the category with the highest predicted probability