statsmodels.nonparametric.kernel_regression.KernelCensoredReg.censored KernelCensoredReg.censored(censor_val) [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ar ArmaFft.arma2ar(nobs=None)

statsmodels.sandbox.regression.gmm.IVGMMResults.remove_data IVGMMResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute

statsmodels.sandbox.distributions.extras.SkewNorm_gen.mean SkewNorm_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.discrete.discrete_model.CountResults class statsmodels.discrete.discrete_model.CountResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for count data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.emplike.descriptive.DescStatMV.mv_mean_contour DescStatMV.mv_mean_contour(mu1_low, mu1_upp, mu2_low, mu2_upp, step1, step2, levs=[0.2, 0.1, 0.05, 0.01, 0.001], var1_name=None, var2_name=None, plot_dta=False) [source] Creates a confidence region plot for the mean of bivariate data Parameters: m1_low : float Minimum value of the mean for variable 1 m1_upp : float Maximum value of the mean for variable 1 mu2_low : float Minimum value of the mean for variable 2 mu2_upp : float

statsmodels.tsa.ar_model.AR.from_formula classmethod AR.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 : extra arguments The

statsmodels.tools.numdiff.approx_hess1 statsmodels.tools.numdiff.approx_hess1(x, f, epsilon=None, args=(), kwargs={}, return_grad=False) [source] Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chosen according to EPS**(1/3)*x. args : tuple Ar

statsmodels.genmod.cov_struct.Exchangeable.initialize Exchangeable.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.robust.robust_linear_model.RLMResults.conf_int RLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval.