statsmodels.sandbox.distributions.transformed.LogTransf_gen.ppf LogTransf_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_l

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz3 IRAnalysis.err_band_sz3(orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default N

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit NormExpan_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters

statsmodels.graphics.gofplots.ProbPlot.probplot ProbPlot.probplot(xlabel=None, ylabel=None, line=None, exceed=False, ax=None, **plotkwargs) [source] Probability plot of the unscaled quantiles of x versus the probabilities of a distibution (not to be confused with a P-P plot). The x-axis is scaled linearly with the quantiles, but the probabilities are used to label the axis. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default),

statsmodels.discrete.discrete_model.DiscreteResults.remove_data DiscreteResults.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 att

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

statsmodels.stats.proportion.proportions_chisquare_pairscontrol statsmodels.stats.proportion.proportions_chisquare_pairscontrol(count, nobs, value=None, multitest_method='hs', alternative='two-sided') [source] chisquare test of proportions for pairs of k samples compared to control Performs a chisquare test for proportions for pairwise comparisons with a control (Dunnet?s test). The control is assumed to be the first element of count and nobs. The alternative is two-sided, larger or smaller.

statsmodels.genmod.families.family.Family.predict Family.predict(mu) [source] Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.regression.linear_model.GLS.fit GLS.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.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err