statsmodels.genmod.families.family.Poisson.deviance Poisson.deviance(endog, mu, scale=1.0) [source] Poisson deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float The deviance function at (endog,mu) as defined below. Notes If a constant term is included it is defined as

Maximum Likelihood Estimation (Generic models) Link to Notebook GitHub This tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples: Probit model for binary dependent variables Negative binomial model for count data The GenericLikelihoodModel class eases the process by providing tools such as automatic numeric differentiation and a unified interface to scipy optimization functions. Using statsmodels, users can fit new MLE models simply

statsmodels.genmod.families.family.Poisson class statsmodels.genmod.families.family.Poisson(link=) [source] Poisson exponential family. Parameters: link : a link instance, optional The default link for the Poisson family is the log link. Available links are log, identity, and sqrt. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Attributes Poisson.link a link instance The link function of the Poisson instance. Poisso

statsmodels.genmod.families.links.CDFLink class statsmodels.genmod.families.links.CDFLink(dbn=) [source] The use the CDF of a scipy.stats distribution CDFLink is a subclass of logit in order to use its _clean method for the link and its derivative. Parameters: dbn : scipy.stats distribution Default is dbn=scipy.stats.norm Notes The CDF link is untested. Methods deriv(p) Derivative of CDF link deriv2(p) Second derivative of the link function g??(p) inverse(z) The inverse of the CDF lin

statsmodels.sandbox.regression.gmm.IVRegressionResults.load classmethod IVRegressionResults.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.tsa.fftarma.ArmaFft.pad ArmaFft.pad(maxlag) [source] construct AR and MA polynomials that are zero-padded to a common length Parameters: maxlag : int new length of lag-polynomials Returns: ar : ndarray extended AR polynomial coefficients ma : ndarray extended AR polynomial coefficients

statsmodels.genmod.families.links.identity class statsmodels.genmod.families.links.identity [source] The identity transform Notes g(p) = p Alias of statsmodels.family.links.Power(power=1.) Methods deriv(p) Derivative of the power transform deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the power transform link function inverse_deriv(z) Derivative of the inverse of the power transform

statsmodels.tsa.ar_model.ARResults.conf_int ARResults.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. ?Default? : uses

statsmodels.distributions.empirical_distribution.StepFunction class statsmodels.distributions.empirical_distribution.StepFunction(x, y, ival=0.0, sorted=False, side='left') [source] A basic step function. Values at the ends are handled in the simplest way possible: everything to the left of x[0] is set to ival; everything to the right of x[-1] is set to y[-1]. Parameters: x : array-like y : array-like ival : float ival is the value given to the values to the left of x[0]. Default is 0. so

statsmodels.genmod.families.family.Binomial.resid_anscombe Binomial.resid_anscombe(endog, mu) [source] The Anscombe residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals as defined below. Notes sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) where cox_snell is defined as cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) where betainc is th