statsmodels.discrete.discrete_model.LogitResults.llf static LogitResults.llf()

statsmodels.stats.weightstats.DescrStatsW.nobs static DescrStatsW.nobs() [source] alias for number of observations/cases, equal to sum of weights

statsmodels.iolib.table.SimpleTable.index SimpleTable.index(value[, start[, stop]]) ? integer -- return first index of value. Raises ValueError if the value is not present.

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.entropy SkewNorm2_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: 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).

statsmodels.tsa.vector_ar.var_model.VARResults.plotsim VARResults.plotsim(steps=1000) Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.miscmodels.count.PoissonOffsetGMLE.initialize PoissonOffsetGMLE.initialize()

statsmodels.robust.robust_linear_model.RLMResults.cov_params RLMResults.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, o

statsmodels.sandbox.distributions.transformed.LogTransf_gen.logpdf LogTransf_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. 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, opti

statsmodels.tsa.arima_model.ARIMAResults.llf static ARIMAResults.llf()

statsmodels.regression.mixed_linear_model.MixedLM.group_list MixedLM.group_list(array) [source] Returns array split into subarrays corresponding to the grouping structure.