statsmodels.tsa.ar_model.ARResults.aic static ARResults.aic() [source]

statsmodels.stats.power.tt_solve_power statsmodels.stats.power.tt_solve_power = > solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of pairs. Parameters: effect_size : float standardized effect size, mean d

statsmodels.miscmodels.count.PoissonOffsetGMLE.from_formula classmethod PoissonOffsetGMLE.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.Data

statsmodels.robust.norms.TukeyBiweight class statsmodels.robust.norms.TukeyBiweight(c=4.685) [source] Tukey?s biweight function for M-estimation. Parameters: c : float, optional The tuning constant for Tukey?s Biweight. The default value is c = 4.685. Notes Tukey?s biweight is sometime?s called bisquare. Methods psi(z) The psi function for Tukey?s biweight estimator psi_deriv(z) The derivative of Tukey?s biweight psi function rho(z) The robust criterion function for Tukey?s biweight e

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

statsmodels.tsa.tsatools.lagmat2ds statsmodels.tsa.tsatools.lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward') [source] generate lagmatrix for 2d array, columns arranged by variables Parameters: x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude

statsmodels.regression.mixed_linear_model.MixedLMResults class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params) [source] Class to contain results of fitting a linear mixed effects model. MixedLMResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance Pointer to PHreg model instance that called fit. normalized_cov_params : array The sampling covariance

statsmodels.stats.outliers_influence.OLSInfluence.dffits_internal static OLSInfluence.dffits_internal() [source] (cached attribute) dffits measure for influence of an observation based on resid_studentized_internal uses original results, no nobs loop

statsmodels.graphics.gofplots.ProbPlot.sample_quantiles static ProbPlot.sample_quantiles() [source]

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_cov VARProcess.forecast_cov(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes