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.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.sandbox.regression.gmm.IVRegressionResults.uncentered_tss static IVRegressionResults.uncentered_tss()

statsmodels.tsa.arima_model.ARIMA.loglike_css ARIMA.loglike_css(params, set_sigma2=True) Conditional Sum of Squares likelihood function.

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond_mean LinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.sandbox.regression.gmm.IVGMMResults.normalized_cov_params IVGMMResults.normalized_cov_params()

statsmodels.graphics.gofplots.ProbPlot.theoretical_percentiles static ProbPlot.theoretical_percentiles() [source]

statsmodels.regression.linear_model.WLS class statsmodels.regression.linear_model.WLS(endog, exog, weights=1.0, missing='none', hasconst=None, **kwargs) [source] A regression model with diagonal but non-identity covariance structure. The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W. Parameters: endog : array-like 1-d endogenous response variable. The d

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