statsmodels.regression.linear_model.RegressionResults class statsmodels.regression.linear_model.RegressionResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Returns: **Attributes** : aic : Aikake?s information criteria. For a model with a constant . For a model without a constant . bic : Bayes? i

statsmodels.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.sandbox.distributions.transformed.Transf_gen.stats Transf_gen.stats(*args, **kwds) Some statistics of the given RV 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments

statsmodels.regression.quantile_regression.QuantReg.whiten QuantReg.whiten(data) [source] QuantReg model whitener does nothing: returns data.

statsmodels.genmod.generalized_linear_model.GLM.fit GLM.fit(start_params=None, maxiter=100, method='IRLS', tol=1e-08, scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source] Fits a generalized linear model for a given family. Parameters: maxiter : int, optional Default is 100. method : string Default is ?IRLS? for iteratively reweighted least squares. This is currently the only method available for GLM fit. scale : string or float, optional scale can be ?X2?, ?d

statsmodels.tsa.ar_model.ARResults.predict ARResults.predict(start=None, end=None, dynamic=False) [source] Returns in-sample and out-of-sample prediction. Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date s

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()

statsmodels.tsa.arima_model.ARMA.score ARMA.score(params) [source] Compute the score function at params. Notes This is a numerical approximation.

statsmodels.stats.moment_helpers.corr2cov statsmodels.stats.moment_helpers.corr2cov(corr, std) [source] convert correlation matrix to covariance matrix given standard deviation Parameters: corr : array_like, 2d correlation matrix, see Notes std : array_like, 1d standard deviation Returns: cov : ndarray (subclass) covariance matrix Notes This function does not convert subclasses of ndarrays. This requires that multiplication is defined elementwise. np.ma.array are allowed, but not m

statsmodels.duration.hazard_regression.PHRegResults.cov_params PHRegResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-lik