statsmodels.regression.mixed_linear_model.MixedLM.hessian MixedLM.hessian(params) [source] Returns the Hessian matrix of the profile log-likelihood. Notes The Hessian matrix that is returned is computed with respect to the parameterization defined by this model?s use_sqrt attribute. The input value params can be with respect to any parameterization.

statsmodels.sandbox.distributions.extras.ACSkewT_gen.pdf ACSkewT_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles 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 scale parameter (default=1) Returns: pdf : ndarray Probability density function

statsmodels.tsa.arima_model.ARMA.loglike_css ARMA.loglike_css(params, set_sigma2=True) [source] Conditional Sum of Squares likelihood function.

statsmodels.tsa.arima_model.ARIMA.predict ARIMA.predict(params, start=None, end=None, exog=None, typ='linear', dynamic=False) [source] ARIMA model in-sample and out-of-sample prediction Parameters: params : array-like The fitted parameters of the model. 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 n

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary_frame GEEMargins.summary_frame(alpha=0.05) [source] Returns a DataFrame summarizing the marginal effects. Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: frame : DataFrames A DataFrame summarizing the marginal effects.

statsmodels.emplike.descriptive.DescStatUV.test_mean DescStatUV.test_mean(mu0, return_weights=False) [source] Returns - 2 x log-likelihood ratio, p-value and weights for a hypothesis test of the mean. Parameters: mu0 : float Mean value to be tested return_weights : bool If return_weights is True the funtion returns the weights of the observations under the null hypothesis. Default is False Returns: test_results : tuple The log-likelihood ratio and p-value of mu0

statsmodels.sandbox.regression.gmm.IV2SLS.from_formula classmethod IV2SLS.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.DataFrame args : ex

statsmodels.regression.linear_model.RegressionResults.resid static RegressionResults.resid() [source]

statsmodels.genmod.families.links.NegativeBinomial.deriv NegativeBinomial.deriv(p) [source] Derivative of the negative binomial transform Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the negative binomial transform link function Notes g?(x) = 1/(x+alpha*x^2)

statsmodels.tsa.arima_process.ArmaProcess.impulse_response ArmaProcess.impulse_response(nobs=None) [source] get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By