feglm.control: Set 'feglm' Control Parameters

In amrei-stammann/alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Set feglm Control Parameters

Description

Set and change parameters used for fitting feglm.

Usage

feglm.control(dev.tol = 1e-08, step.tol = 1e-08, pseudo.tol = 1e-05,
  rho.tol = 1e-04, iter.max = 100L, trace = 0L, drop.pc = TRUE)

Arguments

dev.tol	tolerance level for the first stopping condition of the maximization routine. The stopping condition is based on the relative change of the deviance in iteration r and can be expressed as follows: Δ dev / dev < tol. Default is 1.0e-08.
step.tol	tolerance level for the second stopping condition of the maximization routine. The stopping condition is based on the euclidean norm of the step size in iteration r and can be expressed as follows: ||Δ β|| < tol. Default is 1.0e-08.
pseudo.tol	tolerance level for the stopping condition of the “pseudo demeaning” algorithm. The stopping condition is based on the relative change of euclidean norm in iteration i and can be expressed as follows: ||Δ v|| / ||v_old|| < tol. Default is 1.0e-05.
rho.tol	tolerance level for the stephalving in the maximization routine. Stephalving only takes place if the deviance in iteration r is larger than the one of the previous iteration. If this is the case, ||Δ β|| is halfed until the deviance is less or equal compared to the deviance of the previous iteration. Stephalving fails if the the following condition holds: ρ < tol, where ρ is the stepcorrection factor. If stephalving fails the maximization routine is canceled. Default is 1.0e-04.
iter.max	unsigned integer indicating the maximum number of iterations in the maximization routine.
trace	unsigned integer indicating if output should be produced in each iteration. Default is 0.
drop.pc	logical indicating to drop observations that are perfectly classified and hence do not contribute to the log-likelihood. This option is useful to reduce the computational costs of the maximization problem, since it reduces the number of observations and does not change the estimates. Default is TRUE.

Value

The function feglm.control returns a named list of control parameters.

See Also

feglm

amrei-stammann/alpaca documentation built on Sept. 30, 2022, 6:59 a.m.