statsmodels.regression.mixed_linear_model.MixedLM.starting_values MixedLM.starting_values(start_params) [source]

statsmodels.sandbox.distributions.extras.SkewNorm_gen.std SkewNorm_gen.std(*args, **kwds) Standard deviation of the distribution. 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) Returns: std : float standard deviation of the distribution

statsmodels.miscmodels.count.PoissonGMLE.nloglikeobs PoissonGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.tsa.arima_model.ARMAResults.remove_data ARMAResults.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 attribute is access

statsmodels.tsa.vector_ar.dynamic.DynamicVAR class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source] Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares Parameters: data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {?expanding?, ?rolling?} min_periods : int or None Minimum number of observations to require in window, defaults to wi

statsmodels.sandbox.distributions.extras.ACSkewT_gen.median ACSkewT_gen.median(*args, **kwds) Median of the distribution. 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distributions.rv_d

statsmodels.stats.power.TTestPower class statsmodels.stats.power.TTestPower(**kwds) [source] Statistical Power calculations for one sample or paired sample t-test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, df, ...]) Calculate the power of a t-test for one sample or paired samples. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample t

statsmodels.genmod.families.family.Poisson.starting_mu Poisson.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.genmod.families.links.CDFLink.deriv CDFLink.deriv(p) [source] Derivative of CDF link Parameters: p : array-like mean parameters Returns: g?(p) : array The derivative of CDF transform at p Notes g?(p) = 1./ dbn.pdf(dbn.ppf(p))

statsmodels.tools.tools.recipr0 statsmodels.tools.tools.recipr0(X) [source] Return the reciprocal of an array, setting all entries equal to 0 as 0. It does not assume that X should be positive in general.