statsmodels.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit_loc_scale NormExpan_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.regression.mixed_linear_model.MixedLM class statsmodels.regression.mixed_linear_model.MixedLM(endog, exog, groups, exog_re=None, use_sqrt=True, missing='none', **kwargs) [source] An object specifying a linear mixed effects model. Use the fit method to fit the model and obtain a results object. Parameters: endog : 1d array-like The dependent variable exog : 2d array-like A matrix of covariates used to determine the mean structure (the ?fixed effects? covariates). groups : 1d

statsmodels.regression.linear_model.OLSResults.cov_HC3 static OLSResults.cov_HC3() See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.MultinomialResults.bic static MultinomialResults.bic() [source]

statsmodels.tsa.arima_process.ArmaProcess.periodogram ArmaProcess.periodogram(nobs=None) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that

statsmodels.sandbox.distributions.transformed.loggammaexpg statsmodels.sandbox.distributions.transformed.loggammaexpg = univariate distribution of a non-linear monotonic transformation of a random variable

statsmodels.tsa.filters.bk_filter.bkfilter statsmodels.tsa.filters.bk_filter.bkfilter(X, low=6, high=32, K=12) [source] Baxter-King bandpass filter Parameters: X : array-like A 1 or 2d ndarray. If 2d, variables are assumed to be in columns. low : float Minimum period for oscillations, ie., Baxter and King suggest that the Burns-Mitchell U.S. business cycle has 6 for quarterly data and 1.5 for annual data. high : float Maximum period for oscillations BK suggest that the U.S. business cy

Import Paths and Structure We offer two ways of importing functions and classes from statsmodels: API import for interactive useAllows tab completion Direct import for programsAvoids importing unnecessary modules and commands API Import for interactive use For interactive use the recommended import is: import statsmodels.api as sm Importing statsmodels.api will load most of the public parts of statsmodels. This makes most functions and classes conveniently available within one or two lev

statsmodels.tsa.vector_ar.var_model.VARResults.sigma_u_mle static VARResults.sigma_u_mle() [source] (Biased) maximum likelihood estimate of noise process covariance