statsmodels.discrete.discrete_model.MultinomialModel class statsmodels.discrete.discrete_model.MultinomialModel(endog, exog, **kwargs) [source] Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized([start_p

statsmodels.graphics.regressionplots.influence_plot statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source] Plot of influence in regression. Plots studentized resids vs. leverage. Parameters: results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha va

statsmodels.discrete.discrete_model.Probit.loglike Probit.loglike(params) [source] Log-likelihood of probit model (i.e., the normal distribution). Parameters: params : array-like The parameters of the model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.regression.linear_model.GLSAR.loglike GLSAR.loglike(params) Returns the value of the Gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable endog. Parameters: params : array-like The parameter estimates Returns: loglike : float The value of the log-likelihood function for a GLS Model. Notes The log-likelihood function for the normal distribution is Y and Y-h

statsmodels.robust.robust_linear_model.RLMResults.sresid static RLMResults.sresid() [source]

statsmodels.stats.proportion.proportion_effectsize statsmodels.stats.proportion.proportion_effectsize(prop1, prop2, method='normal') [source] effect size for a test comparing two proportions for use in power function Parameters: prop1, prop2: float or array_like : Returns: es : float or ndarray effect size for (transformed) prop1 - prop2 Notes only method=?normal? is implemented to match pwr.p2.test see http://www.statmethods.net/stats/power.html Effect size for normal is defined as 2

statsmodels.robust.robust_linear_model.RLMResults.save RLMResults.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_

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects static MixedLMResults.random_effects() [source] Returns the conditional means of all random effects given the data. Returns: random_effects : DataFrame A DataFrame with the distinct group values as the index and the conditional means of the random effects in the columns.

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.rvs TransfTwo_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.sandbox.distributions.extras.ACSkewT_gen.pdf ACSkewT_gen.pdf(x, *args, **kwds) Probability density function at x 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: pdf : ndarray Probability density function