statsmodels.genmod.generalized_estimating_equations.NominalGEE.fit_regularized

NominalGEE.fit_regularized(pen_wt, scad_param=3.7, maxiter=100, ddof_scale=None, update_assoc=5, ctol=1e-05, ztol=0.001, eps=1e-06, scale=None)

Regularized estimation for GEE.

Parameters

pen_wt : float

The penalty weight (a non-negative scalar).

scad_param : float

Non-negative scalar determining the shape of the Scad penalty.

maxiter : int

The maximum number of iterations.

ddof_scale : int

Value to subtract from nobs when calculating the denominator degrees of freedom for t-statistics, defaults to the number of columns in exog.

update_assoc : int

The dependence parameters are updated every update_assoc iterations of the mean structure parameter updates.

ctol : float

Convergence criterion, default is one order of magnitude smaller than proposed in section 3.1 of Wang et al.

ztol : float

Coefficients smaller than this value are treated as being zero, default is based on section 5 of Wang et al.

eps : non-negative scalar

Numerical constant, see section 3.2 of Wang et al.

scale : float or string

If a float, this value is used as the scale parameter. If “X2”, the scale parameter is always estimated using Pearson’s chi-square method (e.g. as in a quasi-Poisson analysis). If None, the default approach for the family is used to estimate the scale parameter.

Returns

GEEResults instance. Note that not all methods of the results

class make sense when the model has been fit with regularization.

Notes

This implementation assumes that the link is canonical.

References

Wang L, Zhou J, Qu A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics. 2012 Jun;68(2):353-60. doi: 10.1111/j.1541-0420.2011.01678.x. https://www.ncbi.nlm.nih.gov/pubmed/21955051 http://users.stat.umn.edu/~wangx346/research/GEE_selection.pdf