statsmodels.genmod.families.links.probit class statsmodels.genmod.families.links.probit(dbn=) [source] The probit (standard normal CDF) transform Notes g(p) = scipy.stats.norm.ppf(p) probit is an alias of CDFLink. Methods deriv(p) Derivative of CDF link deriv2(p) Second derivative of the link function g??(p) inverse(z) The inverse of the CDF link inverse_deriv(z) Derivative of the inverse of the CDF transformation link function

statsmodels.stats.power.TTestIndPower.plot_power TTestIndPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then o

statsmodels.discrete.discrete_model.CountResults.cov_params CountResults.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,

statsmodels.sandbox.tools.tools_pca.pca statsmodels.sandbox.tools.tools_pca.pca(data, keepdim=0, normalize=0, demean=True) [source] principal components with eigenvector decomposition similar to princomp in matlab Parameters: data : ndarray, 2d data with observations by rows and variables in columns keepdim : integer number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included normalize : boolean if true, then eigenvectors are normalized by sqrt of eigenvalues

statsmodels.discrete.discrete_model.MultinomialResults.save MultinomialResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Not

statsmodels.discrete.discrete_model.DiscreteResults.remove_data DiscreteResults.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 att

statsmodels.tsa.ar_model.ARResults.predict ARResults.predict(start=None, end=None, dynamic=False) [source] Returns in-sample and out-of-sample prediction. Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date s

statsmodels.genmod.generalized_estimating_equations.GEEResults.load classmethod GEEResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.genmod.families.links.inverse_power.deriv inverse_power.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.robust.robust_linear_model.RLM.predict RLM.predict(params, exog=None) [source] Return linear predicted values from a design matrix. Parameters: params : array-like, optional after fit has been called Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model as not yet been fit, params is not optional.