statsmodels.tsa.arima_process.ArmaProcess.pacf ArmaProcess.pacf(nobs=None) [source] partial 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 pacf Returns: pacf : array partial autocorrelation of ARMA process given by ar, ma Notes solv

statsmodels.genmod.families.family.Poisson.loglike Poisson.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Poisson exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes llf = scale * sum(-mu + endog*log(mu) - gammaln(endog+1

statsmodels.stats.power.GofChisquarePower.solve_power GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2) [source] solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters: effect_size : float standardized effect size, according to

statsmodels.tsa.vector_ar.var_model.VARResults.stderr static VARResults.stderr() [source] Standard errors of coefficients, reshaped to match in size

statsmodels.tsa.vector_ar.var_model.VARProcess.plotsim VARProcess.plotsim(steps=1000) [source] Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.genmod.families.links.nbinom class statsmodels.genmod.families.links.nbinom(alpha=1.0) [source] The negative binomial link function. Notes g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative binomial transform inverse_deriv(z) Derivative of the inverse of the negative binomial transform

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdroots ArmaFft.spdroots_(arroots, maroots, w) [source] spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies) Parameters: arroots : ndarray roots of ar (denominator) lag-polynomial maroots : ndarray roots of ma (numerator) lag-polynomial w : array_like frequencies for which spd is calculated Notes this should go into a function

statsmodels.genmod.families.family.NegativeBinomial.starting_mu NegativeBinomial.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.stats.proportion.binom_test statsmodels.stats.proportion.binom_test(count, nobs, prop=0.5, alternative='two-sided') [source] Perform a test that the probability of success is p. This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is p. Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The probability of suc

statsmodels.iolib.summary2.Summary.add_title Summary.add_title(title=None, results=None) [source] Insert a title on top of the summary table. If a string is provided in the title argument, that string is printed. If no title string is provided but a results instance is provided, statsmodels attempts to construct a useful title automatically.