statsmodels.discrete.discrete_model.LogitResults.resid_pearson static LogitResults.resid_pearson() Pearson residuals Notes Pearson residuals are defined to be where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.iolib.foreign.StataReader class statsmodels.iolib.foreign.StataReader(fname, missing_values=False, encoding=None) [source] Stata .dta file reader. Provides methods to return the metadata of a Stata .dta file and a generator for the data itself. Parameters: file : file-like A file-like object representing a Stata .dta file. missing_values : bool If missing_values is True, parse missing_values and return a Missing Values object instead of None. encoding : string, optional Use

statsmodels.genmod.generalized_linear_model.GLM.hessian_factor GLM.hessian_factor(params, scale=None, observed=True) [source] Weights for calculating Hessian Parameters: params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is returned. I

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.rvs ExpTransf_gen.rvs(*args, **kwds) Random variates of given type. 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Retu

statsmodels.discrete.discrete_model.BinaryResults.cov_params BinaryResults.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

Regression Diagnostics and Specification Tests Introduction In many cases of statistical analysis, we are not sure whether our statistical model is correctly specified. For example when using ols, then linearity and homoscedasticity are assumed, some test statistics additionally assume that the errors are normally distributed or that we have a large sample. Since our results depend on these statistical assumptions, the results are only correct of our assumptions hold (at least approximately).

statsmodels.regression.linear_model.RegressionResults.remove_data RegressionResults.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

statsmodels.iolib.summary.Summary class statsmodels.iolib.summary.Summary [source] class to hold tables for result summary presentation Construction does not take any parameters. Tables and text can be added with the add_ methods. Attributes tables list of tables Contains the list of SimpleTable instances, horizontally concatenated tables are not saved separately. extra_txt string extra lines that are added to the text output, used for warnings and explanations. Methods add_extra_txt(etext)

statsmodels.discrete.discrete_model.ProbitResults.remove_data ProbitResults.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 attribu

statsmodels.discrete.discrete_model.NegativeBinomialResults.load classmethod NegativeBinomialResults.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 :