statsmodels.genmod.cov_struct.Nested class statsmodels.genmod.cov_struct.Nested(cov_nearest_method='clipped') [source] A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups, each level of which contributes to the random error term of the model. When using this working covariance structure, dep_data of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level grou

statsmodels.genmod.cov_struct.Independence class statsmodels.genmod.cov_struct.Independence(cov_nearest_method='clipped') [source] An independence working dependence structure. Methods covariance_matrix(expval, index) Returns the working covariance or correlation matrix for a given cluster of data. covariance_matrix_solve(expval, index, ...) Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this clas

statsmodels.genmod.cov_struct.GlobalOddsRatio class statsmodels.genmod.cov_struct.GlobalOddsRatio(endog_type) [source] Estimate the global odds ratio for a GEE with ordinal or nominal data. Notes The following data structures are calculated in the class: ?ibd? is a list whose i^th element ibd[i] is a sequence of integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. cpp is a dictionary where cpp[group] is a map from cut-point pa

statsmodels.genmod.cov_struct.Exchangeable class statsmodels.genmod.cov_struct.Exchangeable [source] An exchangeable working dependence structure. Methods covariance_matrix(expval, index) Returns the working covariance or correlation matrix for a given cluster of data. covariance_matrix_solve(expval, index, ...) Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. initialize(model) Called b

statsmodels.genmod.cov_struct.CovStruct class statsmodels.genmod.cov_struct.CovStruct(cov_nearest_method='clipped') [source] A base class for correlation and covariance structures of grouped data. Each implementation of this class takes the residuals from a regression model that has been fitted to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The state of the covariance structure is represented through the value of the class

statsmodels.genmod.cov_struct.Autoregressive class statsmodels.genmod.cov_struct.Autoregressive(dist_func=None) [source] An autoregressive working dependence structure. The dependence is defined in terms of the time component of the parent GEE class. Time represents a potentially multidimensional index from which distances between pairs of observations can be determined. The correlation between two observations in the same cluster is dep_params^distance, where dep_params is the autocorrelati

Generalized Method of Moments gmm statsmodels.gmm contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and n

Generalized Linear Models (Formula) Link to Notebook GitHub This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models. To begin, we load the Star98 dataset and we construct a formula and pre-process the data: In [1]: from __future__ import print_function import statsmodels.api as sm import statsmodels.formula.api as smf star98 = sm.datasets.star98.load_pandas().data formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \

Generalized Linear Models Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm from scipy import stats from matplotlib import pyplot as plt GLM: Binomial response data Load data In this example, we use the Star98 dataset which was taken with permission from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook information can be obtained by typing: In [2]: print(sm.datasets.star98.NOTE)

Generalized Linear Models Generalized linear models currently supports estimation using the one-parameter exponential families See Module Reference for commands and arguments. Examples # Load modules and data import statsmodels.api as sm data = sm.datasets.scotland.load() data.exog = sm.add_constant(data.exog) # Instantiate a gamma family model with the default link function. gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma()) gamma_results = gamma_model.fit() Detailed ex