Parametric Shared Frailty Models: Estimate
Estimate
An optional panel to specify the settings to control the estimation of the shared frailty models and the optional feature selection process.
 Alternating Direction Method or Multipliers (ADMM)

 Fast
 Applies the fast alternating direction method of multipliers (ADMM). This is the default.
 Traditional
 Applies the traditional ADMM algorithm.
 Apply L1 regularization
 Conducts the process to control feature selection. The Penalty Parameter field specifies the penalty parameter that controls the regularization process. It must be a single value greater than 0. The default setting is 0.001.
 Model Convergence Criteria

 Parameter Convergence
 Specifies the convergence criteria for the parameter. It must be a single numeric value belonging to [0, 1). The default setting is 0.000001. For Type, you can select either ABSOLUTE to apply the absolute convergence to the inner optimization or RELATIVE to apply the relative convergence to the inner optimization. The optional Value specifies a numeric threshold for the convergence type.
 Objective Function Convergence
 Specifies the convergence criteria for the objective function. It must be a single numeric value that belongs to [0, 1). The default setting is 0, which does not apply the convergence criteria. For Type, you can select either ABSOLUTE to apply the absolute convergence to the inner optimization or RELATIVE to apply the relative convergence to the inner optimization. The optional Value specifies a numeric threshold for the convergence type.
 Hessian Convergence
 Specifies the convergence criteria for the Hessian matrix. It must be a single numeric value that belongs to [0, 1). The default setting is 0, which does not apply the convergence criteria. For Type, you can select either ABSOLUTE to apply the absolute convergence to the inner optimization or RELATIVE to apply the relative convergence to the inner optimization. The optional Value specifies a numeric threshold for the convergence type.
 Residual Convergence Criteria
 An option to control the optimization process.
 Both primal and dual residual
 Applies both primal and dual residual convergence criterion. This setting is by default.
 Primal residual only
 Applies the primal residual convergence criterion.
 Dual residual only
 Applies the dual residual convergence criterion.
 Method
 An optional parameter to specify the estimation method.
 Auto
 Automatically chooses the method based on the sample data set. This method is selected by default. The Threshold number of predictors field specifies the threshold of the number of predictors, and must be a single integer greater than 1. The default value is 1000.
 NewtonRaphson
 Applies the NewtonRaphson’s method.
 LBFGS
 Applies the limitedmemory BFGS algorithm. The Update field specifies the number of the past updates that are maintained by the limitedmemory BFGS algorithm, and must be a single integer greater than or equal to 1. The default value is 5.
 Iteration

 Maximum iterations
 Specifies the maximum number of iterations. It must be a single integer that belongs to [1, 300]. The default setting is 20.
 Maximum stephalving
 Specifies the maximum number of stephalving. It must be a single integer that belongs to [1, 200]. The default setting is 5.
 Maximum number of line searches
 Specifies the maximum number of line searches. It must be a single integer that belongs to [1, 300]. The default setting is 20.
 Absolute convergence for iteration process
 Specifies the absolute convergence for the outer iteration process. It must be a single numeric value that belongs to (0, 1). The default setting is 0.0001.
 Relative convergence for iteration process
 Specifies the relative convergence for the outer iteration process. It must be a single numeric value that belongs to (0, 1). The default setting is 0.01.