statsmodels.miscmodels.tmodel.TLinearModel.reduceparams TLinearModel.reduceparams(params)

statsmodels.duration.hazard_regression.PHReg.initialize PHReg.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.discrete.discrete_model.NegativeBinomial.information NegativeBinomial.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.OLSResults.resid_pearson static OLSResults.resid_pearson() Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

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

statsmodels.iolib.smpickle.load_pickle statsmodels.iolib.smpickle.load_pickle(fname) [source] Load a previously saved object from file Parameters: fname : str Filename to unpickle Notes This method can be used to load both models and results.

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.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.discrete.discrete_model.Probit.information Probit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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