emfrail_control: Control parameters for emfrail
In frailtyEM: Fitting Frailty Models with the EM Algorithm

Description Usage Arguments Details Value See Also Examples

Control parameters for emfrail

emfrail_control(opt_fit = TRUE, se = TRUE, se_adj = TRUE,
  ca_test = TRUE, lik_ci = TRUE, lik_interval = exp(c(-3, 20)),
  lik_interval_stable = exp(c(0, 20)), nlm_control = list(stepmax = 1),
  zph = FALSE, zph_transform = "km", em_control = list(eps = 1e-04,
  maxit = Inf, fast_fit = TRUE, verbose = FALSE, upper_tol = exp(10),
  lik_tol = 1))

`opt_fit`	Logical. Whether the outer optimization should be carried out. If `FALSE`, then the frailty parameter is treated as fixed and the `emfrail` function returns only log-likelihood. See details.
`se`	Logical. Whether to calculate the variance / covariance matrix.
`se_adj`	Logical. Whether to calculate the adjusted variance / covariance matrix (needs `se == TRUE`)
`ca_test`	Logical. Should the Commenges-Andersen test be calculated?
`lik_ci`	Logical. Should likelihood-based confidence interval be calculated for the frailty parameter?
`lik_interval`	The edges, on the scale of θ, of the parameter space in which to search for likelihood-based confidence interval
`lik_interval_stable`	(for dist = "stable") The edges, on the scale of θ, of the parameter space in which to search for likelihood-based confidence interval
`nlm_control`	A list of named arguments to be sent to `nlm` for the outer optimization.
`zph`	Logical. Should the `cox.zph` test be performed at the maximum likelihood estimate?
`zph_transform`	One of `"km", "rank", "identity"` or a function of one argument to be pased on to `cox.zph`.
`em_control`	A list of parameters for the inner optimization. See details.

The nlm_control argument should not overalp with hessian, f or p.

The em_control argument should be a list with the following items:

eps A criterion for convergence of the EM algorithm (difference between two consecutive values of the log-likelihood)
maxit The maximum number of iterations between the E step and the M step
fast_fit Logical, whether the closed form formulas should be used for the E step when available
verbose Logical, whether details of the optimization should be printed
upper_tol An upper bound for θ; after this treshold, the algorithm returns the limiting log-likelihood of the no-frailty model. That is because the no-frailty scenario corresponds to a θ = ∞, which could lead to some numerical issues
lik_tol For values higher than this, the algorithm returns a warning when the log-likelihood decreases between EM steps. Technically, this should not happen, but if the parameter θ is somewhere really far from the maximum, numerical problems might lead in very small likelihood decreases.

The fast_fit option make a difference when the distribution is gamma (with or without left truncation) or inverse Gaussian, i.e. pvf with m = -1/2 (without left truncation). For all the other scenarios, the fast_fit option will automatically be changed to FALSE. When the number of events in a cluster / individual is not very small, the cases for which fast fitting is available will show an improvement in performance.

The starting value of the outer optimization may be set in the distribution argument.

An object of the type emfrail_control.

emfrail, emfrail_dist, emfrail_pll