statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.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-like, opti

statsmodels.regression.linear_model.OLSResults.rsquared_adj static OLSResults.rsquared_adj()

statsmodels.sandbox.regression.gmm.LinearIVGMM.predict LinearIVGMM.predict(params, exog=None) [source]

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]

statsmodels.regression.linear_model.RegressionResults.pvalues static RegressionResults.pvalues()

statsmodels.tools.tools.categorical statsmodels.tools.tools.categorical(data, col=None, dictnames=False, drop=False) [source] Returns a dummy matrix given an array of categorical variables. Parameters: data : array A structured array, recarray, or array. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : ?string?, int, or None If data is a structured array or a recarray, col can

statsmodels.genmod.cov_struct.CovStruct class statsmodels.genmod.cov_struct.CovStruct(cov_nearest_method='clipped') [source] A base class for correlation and covariance structures of grouped data. Each implementation of this class takes the residuals from a regression model that has been fitted to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The state of the covariance structure is represented through the value of the class

statsmodels.miscmodels.count.PoissonZiGMLE.nloglike PoissonZiGMLE.nloglike(params)

statsmodels.sandbox.regression.gmm.GMMResults.tvalues static GMMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.genmod.families.family.NegativeBinomial class statsmodels.genmod.families.family.NegativeBinomial(link=, alpha=1.0) [source] Negative Binomial exponential family. Parameters: link : a link instance, optional The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.family.links for more information. alpha : float, optional The ancillary parameter for the negative binomial distribution. For