statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix_solve Autoregressive.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.expect ExpTransf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one

statsmodels.genmod.families.links.NegativeBinomial.inverse_deriv NegativeBinomial.inverse_deriv(z) [source] Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :

statsmodels.sandbox.regression.try_ols_anova.data2proddummy statsmodels.sandbox.regression.try_ols_anova.data2proddummy(x) [source] creates product dummy variables from 2 columns of 2d array drops last dummy variable, but not from each category singular with simple dummy variable but not with constant quickly written, no safeguards

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_split static GEEResults.resid_split() [source] Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.genmod.cov_struct.CovStruct.covariance_matrix CovStruct.covariance_matrix(endog_expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

statsmodels.tsa.varma_process.VarmaPoly class statsmodels.tsa.varma_process.VarmaPoly(ar, ma=None) [source] class to keep track of Varma polynomial format Examples ar23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) ma22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[ 0.4, 0. ], [ 0.2, 0.3]]]) Methods getisinvertible([a]) check whether the auto-regressive lag-polynomial is stationary getisstationary([a]) check whether the auto-regressive l

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.regression.linear_model.OLS.fit OLS.fit(method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters: method : str Can be ?pinv?, ?qr?. ?pinv? uses the Moore-Penrose pseudoinverse to solve the least squares problem. ?qr? uses the QR factorization. Returns: A RegressionResults class instance. : See also regression.RegressionResu

statsmodels.sandbox.regression.gmm.IVGMMResults.remove_data IVGMMResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute